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CONTENTSARTICLESDo Futures and Options Trading Increase Stock Market Volatility? 3Retailing of Government Securities 9Volatility Indices - A Leading Market Indicator 13GOVERNMENT NEWS 16

I. National Advisory Committee on Accounting Standard 16II. Reopening / Revision of Annual accounts 16III. Debenture Redemption Reserve 16IV. Disqualification of Directors 16

RBI NEWS 16I. External Commercial Borrowings - Parking of funds abroad 16II. Overseas Investments 16III. Acquisition of Foreign Securities by Resident Individual under ESOP Scheme 17IV. Trading of Government Securities on Stock Exchanges 17V. Credit Exposure Norms - Measurement of Credit Exposure of Derivative Products - 17

Methodology for MeasurementVI. Bank financing of Equities and Investments in Shares 17VII. Public Issue of Shares and Debentures - Underwriting by Merchant Banking Subsidiaries of 17

Commercial BanksSEBI NEWS 17

I. Secondary Market Advisory Committee 17II. Introduction of T+2 Rolling Settlement in Equity Market 18III. Appointment of Common Agency for Physical and Electronic Share Registry Work 18IV. Secretarial Audit and Reconciliation of the Admitted, Issued and Listed Capital 18V. Introduction of Futures and Options on Additional Stocks 19VI Conversion of Close-ended Schemes to Open-ended Schemes 19VII. Amendment to Listing Agreement - Clause 32 and Clause 41 19

NSE NEWS 19I. Introduction of Futures and Options on Additional Securities 19II. FM inaugurates retail trading in Government securities on NSE 20

MARKET REVIEW 20 Membership 20 Capital Market 20

Trading 20Indices 22Settlement 24

Futures & Options Market 24Trading 25Member Trading Pattern 25Settlement 26

Wholesale Debt Market 26 Investor Grievances 27 Arbitration 28 System & Telecom 28 NSE's Certification in Financial Markets 28

ANNEXURES

'NSENEWS' is normally published in the second week of the succeeding month. * Articles on subjects of interest to Professionals in thesecurities market are welcome. * Authors (other than employees of NSE group) are suitably rewarded. * Views expressed by authors are theirown and NSE does not accept any responsibility. *All rights reserved. No part of the NSENEWS may be reproduced or copied in any form byany means without the written permission of NSE.

nse newsJanuary 2003

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MANAGERIAL PERSONNEL OF NSEILNAME DESIGNATION DEPARTMENT TEL. NO. EXTN.

Mr Ravi Narain Managing Director and CEO 26598200 7000Ms Chitra Ramkrishna Director, Business Operations NSCCL, HRD, Corporate Communication 26598201 7001

& AdministrationMr Raghavan Putran Director, Business Operations Development & Projects, Listing, Premises, 26598202 7002

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& Company SecretaryMr M L Soneji Sr. Vice President WDM, Capital Market, IPO, Membership 26598204 5000

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MANAGERIAL PERSONNEL OF NSE.IT

Mr Satish Naralkar CEO 28268850 7777

Mr C Kajwadkar Vice President Operations 28268851 1001

Mr G M Shenoy Vice President Projects 26598207 2000

Mr Rohit Punja Vice President Quality 28268852 1002

Ms T. S. Jagadharini Asst. Vice President Functional Specialist 28268854 1004

Ms Lalitha M Sr. Consultant Projects 26598230 6095

Mr Anand Pachchhapur Sr. Manager Customer Support 28268857 1009

Mr K H Shriram Sr. Manager Marketing 28268856 1007

Mr Ashish Dandekar Manager Telecom 26598229 2117

Ms Chandra Bhattacharjee Group Head HRD, Admin, CC 28268858 7608

Mr HariHaran M N Manager Finance & Accounts 28271227 7619

Ms Mamatha Rangaprasad Manager Neat XS/Neat iXS 28270645 7618

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Mr M Vasudev Rao Manager Legal & Secretarial 28270061 7616

Ms Pallavi Narvekar Manager ICMS � 7623

Mr Puneet Kohli Manager (Delhi Office) Telecom (011) 23344503 105

Mr P. R. Visvas Manager Projects 26598100 2130

Mr Shripad Bhanap Manager PAYGT 26598472 5111


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D o F u t u r e s a n d O p t i o n s t r a d i n g i n c r e a s e s t o c k m a r k e t v o l a t i l i t y ?

Dr. Premalata Shenbagaraman, CFA

I. Introduction

In the last decade, many emerging and transition economieshave started introducing derivative contracts. As was the casewhen commodity futures were first introduced on the ChicagoBoard of Trade in 1865, policymakers and regulators in thesemarkets are concerned about the impact of futures on theunderlying cash market. One of the reasons for this concernis the belief that futures trading attracts speculators who thendestabilize spot prices. This concern is evident in the followingexcerpt from an article by John Stuart Mill (1871):

�The safety and cheapness of communications, whichenable a deficiency in one place to be, supplied from thesurplus of another render the fluctuations of prices muchless extreme than formerly. This effect is much promotedby the existence of speculative merchant. Speculators,therefore, have a highly useful office in the economy ofsociety�.

Since futures encourage speculation, the debate on theimpact of speculators intensified when futures contracts werefirst introduced for trading; beginning with commodityfutures and moving on to financial futures and recentlyfutures on weather and electricity. However, this traditionalfavorable view towards the economic benefits of speculativeactivity has not always been acceptable to regulators. Forexample, futures trading was blamed by some for the stockmarket crash of 1987 in the USA, thereby warranting moreregulation. However before further regulation in introduced,it is essential to determine whether in fact there is a causallink between the introduction of futures and spot marketvolatility. It therefore becomes imperative that we seekanswers to questions like: What is the impact of derivativesupon market efficiency and liquidity of the underlying cashmarket? To what extent do derivatives destabilize thefinancial system, and how should these risks be addressed?Can the results from studies of developed markets beextended to emerging markets?

This paper seeks to contribute to the existing literaturein many ways. This study seeks to examine the impact offinancial derivatives introduction on cash market volatilityin an emerging market, India. Further, this study improvesupon the methodology used in prior studies by using aframework that allows for generalized auto-regressiveconditional heteroskedasticity (GARCH) i.e., it explicitlymodels the volatility process over time, rather than usingestimated standard deviations to measure volatility. This

* Asst. Professor, Department of Finance Clemson University Clemson, SC 29634, USA Email: [email protected]

estimation technique enables us to explore the link betweeninformation/news arrival in the market and its effect oncash market volatility. The study also looks at the linkages inongoing trading activity in the futures market with theunderlying spot market volatility by decomposing tradingvolume and open interest into an expected component andan unexpected (surprise) component. Finally this is the firststudy to our knowledge that looks at the effects of bothstock index futures introduction as well as stock indexoptions introduction on the underlying cash market volatility.

The results of this study are crucial to investors, stockexchange officials and regulators. Derivatives play a veryimportant role in the price discovery process and incompleting the market. Their role in risk management forinstitutional investors and mutual fund managers need hardlybe overemphasized. This role as a tool for risk managementclearly assumes that derivatives trading do not increase marketvolatility and risk. The results of this study will throw somelight on the effects of derivative introduction on theefficiency and volatility of the underlying cash markets.

The study is organized as follows. Section II discussesthe theoretical debate and summarizes the empirical literatureon derivative listing effects, Section III details the modeland the econometric methodology used in this study, SectionIV outlines the data used and discusses the main results ofthe model and finally Section V concludes the study andpresents directions for future research.

II. Theoretical foundations and survey of the empiricalliterature.

The introduction of equity index futures markets enablestraders to transact large volumes at much lower transactioncosts relative to the cash market. The consequence of thisincrease in order flow to futures markets is unresolved onboth a theoretical and an empirical front. Stein (1987)develops a model in which prices are determined by theinteraction between hedgers and informed speculators. Inthis model, opening a futures market has two effects; (1).The futures market improves risk sharing and thereforereduces price volatility, and (2). If the speculators observe anoisy but informative signal, the hedgers react to the noisein the speculative trades, producing an increase in volatility.

In contrast, models developed by Danthine (1978) arguethat the futures markets improve market depth and reducevolatility because the cost to informed traders of responding


4

to mispricing is reduced. Froot and Perold(1991) extendKyle�s(1985) model to show that market depth is increasedby more rapid dissemination of market-wide informationand the presence of market makers in the futures market inaddition to the cash market. Ross (1989) assumes that thereexists an economy that is devoid of arbitrage and proceedsto provide a condition under which the no-arbitrage situationwill be sustained. It implies that the variance of the pricechange will be equal to the rate of information flow. Theimplication of this is that the volatility of the asset price willincrease as the rate of information flow increases. Thus, iffutures increase the flow of information, then in the absenceof arbitrage opportunities, the volatility of the spot pricemust change. Overall, the theoretical work on futures listingeffects offers no consensus on the size and the direction ofthe change in volatility. We therefore need to turn to theempirical literature on evidence relating to the volatility effectsof listing index futures and options.

The first stock index futures contract introduced inthe world was the Value line contract, introduced by theKansas City Board of Trade in 1982 in the USA. Since thenwe have seem numerous markets all over the world launchnew derivative contracts every year. Following theintroduction of derivative contracts in developed marketslike the US and UK, researchers have sought to analyze theimpact of derivatives introduction on the volatility andefficiency of the underlying cash market. The empiricalevidence is however quite mixed. Most studies summarizethat the introduction of derivatives does not destabilize theunderlying market; either there is no effect or perhaps onlya very small decline in volatility1 . The impact however, seemsto vary depending on the time period studied and the countrystudied. For example, in a study of 25 countries, Gulen andMayhew (2000) find that futures trading is associated withincreased volatility in the United States and Japan. In somecountries, there is no robust, significant effect, and in manyothers, volatility is lower after futures have been introduced.

Nathan Associates (1974) was the first to study theimpact of listing options on the Chicago Board of Exchange.He reported that the introduction of options seemed tohave helped stabilize trading in the underlying stocks. Thisresult has been supported by Skinner (1989) and also byother authors for the UK, Canada, Switzerland and Sweden.More recent work by Lamoureux and Pannikath (1994),Freund, McCann and Webb (1994) and Bollen (1998) havefound that the for different stocks. Or perhaps, there arespill over effects associated with listing options for somestocks, such that the dynamics of other stocks also changes(Detemple and Jorion, 1990, and Cao 1999).

In looking at the effect on liquidity, Nathan Associates(1974) found that the trading volume did not change withoption introduction. However, later studies like Kumar,Sarin and Shastri (1995) have found that the volume in theunderlying stock does increase after the introduction ofstock options. Studies have also found that after theintroduction of options, prices tend to reflect newinformation more quickly, bid-ask spreads narrow, and theadverse selection component of the spread becomessmaller. Relatively few authors have studied the impact ofstock index options listing on volatility in the cash market.Evidence reported by Chatrath, Kamath, Chakornpipat andRamchander (1995) indicates that S&P 100 stock indexoptions trading had a stabilizing effect on the underlyingstock index. Studies of volatility effects of individual equityoptions have also reported mixed results; some find thatvolatility is unchanged, while some report a small decreasein volatility. Only one paper Wei, Poon and Zee (1997)report an increase in volatility for options on OTC stocksin the USA. However no consensus result emerges, whichis probably a result of different data and time-periodsstudied, as also the inherent endogenous nature of theoption listing decision2 .

III. Model and Methodology

The impact of stock index futures and option contractintroduction in the Indian market is examined using aunivariate GARCH (1,1) model3 . The time series of dailyreturns on the S&P CNX Nifty Index is modeled as aunivariate GARCH process. Following Pagan and Schwert(1990) and Engle and Ng (1993), we need to remove fromthe time series any predictability associated with lagged worldreturns and/or day of the week effects. It is important toremove market-wide influences on Nifty returns, if we areto isolate the impact of futures introduction. In order to dothis we need a proxy that is not associated with any futurescontract, and yet captures market-wide influences in India.For example, information news releases relating to economicconditions like inflation rates, growth forecasts, exchangerates, etc are likely to affect the whole market. It is necessaryto remove the effects of all these factors on price volatility.Since the Nifty Junior has no futures contracts traded on it,it serves as a perfect control variable for us to isolate marketwide factors and thereby concentrate on the residual volatilityin the Nifty as a direct result of the introduction of theindex derivative contracts. We therefore introduce the returnon the Nifty Junior index as an additional independentvariable. The following conditional mean equation is

1 For a detailed summary of this literature, see surveys by Hodges (1992), Damodaran and Subrahmanyam (1992), Stucliffe (1997) and Mayhew (1999).2 In a recent working paper, Mayhew and Mihow (2000) explicitly model the exchanges� option listing choice using a logit model to account for thisendogeniety.3 Alternative GARCH models were estimated, the GJR-GARCH, EGARCH AND TGARCH, but we find the GARCH (1,1) model to providethe best fit for the data in this study.


5

estimated:

� Equation 1

where tniftyR , is the daily return on the S&P CNX NiftyIndex calculated as the first difference of the log of theindex, 1,500 -tspR is the lagged S&P500 index return, andDAYj are day-of-the-week dummy variables for Tuesday toFriday. The lagged S&P500 index return is used as anindependent variable to remove the effects of worldwideprice movements on the volatility of the Nifty Index return.For example, if the Indian market is influenced by USmarkets, this will be reflected through the lagged S&P500return.

The advantage of a GARCH model is that it capturesthe tendency in financial data for volatility clustering. Ittherefore enables us to make the connection betweeninformation and volatility explicit, since any change in therate of information arrival to the market will change thevolatility in the market. Thus, unless information remainsconstant, which is hardly the case, volatility must be timevarying, even on a daily basis. In studying the links betweeninformation, cash market volatility and derivatives trading,two issues are interesting. First, how the initial introductionof derivative contracts impact cash market volatility. Second,whether the existence of futures trading affects daily volatilityin the cash market. To address the first issue, we introduce adummy variable into the conditional variance equation. Ifthe coefficient on the Dummy is statistically significant thenthe introduction of futures has an impact on the spot marketvolatility. To address the second issue, we divide the sampleinto the pre-futures and post- futures sub-sample and aGARCH model is estimated separately for each sub-sample.This allows us to compare the nature of volatility beforeand after the onset of futures trading.

Section IV. Data and Results

Daily closing prices for the period 5th Oct 1995 to 31st Dec2002 for the CNX Nifty and the Nifty Junior were obtainedfrom the CD-ROMs provided by NSE and the NSE website.Data on Nifty futures contract volume and open interestwere downloaded from the NSE website. Data on theS&P500 index were obtained from Reuters Inc. Allestimations in this study are done using SAS. The CNXNifty is an index of 50 stocks traded on the National StockExchange and represents approximately 50% of the totalmarket capitalization of the market. Nifty Junior is an indexof the next most liquid 50 stocks. The first index future inIndia was introduced on the CNX Nifty on June 12, 2000.The first index options contract was introduced on 4th June,2001.

Table 1 provides summary statistics for the Nifty and NiftyJunior indices. All returns are calculated as the first differenceof the log of the index daily close price and Chart 1 graphsthe returns on the Nifty index over time. As seen in Table 1,

tj

jjtrniftyjuniotsptnifty uDAYRRR ++++= å=

-

5

2,21,50010, aaaa

the overall sample has 1805 time series observations. The meanreturn on the Nifty is 0.003% per day with a standard deviationof 1.67% per day. The mean daily return on the Nifty Junioris 0.007% with a standard deviation of 1.95%. If we dividethe sample period into pre-futures vs. post-futures using theJune 12, 2000 cutoff date, the mean daily return on the Niftyis a positive 0.029% before and a negative 0.044% after thefutures was introduced. A similar pattern in Nifty Junior returnsis also apparent. The average daily standard deviation for theNifty return pre-futures is 1.79% and 1.42% post-futures.However, the daily standard deviation for the Nifty Junior,for which no index futures were traded, pre-futures is 2% andpost futures is 1.7%. A very similar pattern emerges whenone examines the pre-options and post-option sub-samplemeans and standard deviations.

Table 1: Descriptive Statistics

Means and standard deviations of first differences of thelog of the Nifty and the Nifty Junior daily price indices, Oct1995 to Dec 2002

Period NOB Nifty Nifty Junior

Mean Std. Mean Std.Deviation Deviation

1995-2002 1805 0.00003 0.01674 0.00007 0.01952

Pre-Futures 1163 0.00029 0.01795 0.00066 0.02036

Post-Futures 642 -0.00044 0.01429 -0.00099 0.01788

Pre-Options 1410 0.00007 0.01785 0.00018 0.02080

Post-Options 395 -0.00011 0.01199 -0.00033 0.01405

Futures contracts were introduced on June 12, 2000 and Optionscontracts on June 4, 2001.

As mentioned earlier, in order to estimate the impact of theintroduction of the futures and options contracts, weintroduce a Dummy variable in the conditional volatilityequation. A significant positive co-efficient would indicatean increase in volatility; a significant negative coefficientwould indicate a decrease in volatility. The results of theestimation for the impact of futures introduction arepresented in Table 3. The coefficient on the futures

Chart 1: Return on the CNX Nifty return


6

dummy 3g , is not significantly different from zero, indicatingno impact on volatility. There appear to be significant day-of-the-week effects as evidenced by the coefficients on thedummies for Tuesday and Friday.

1g can be viewed as a"news" coefficient, with a higher value implying that recentnews has a greater impact on price changes. It relates to theimpact of yesterday's news on today's price changes. Incontrast,

2g reflects the impact of "old news', i.e. it is pickingup the impact of prior news on yesterdays variance and assuch indicated the level of persistence in the informationeffect on volatility. Table 4 presents the results of the modelwith an Options dummy. Index options were introduced onJune 4th, 2001. The Dummy-Options is zero before and 1on/after June 4th 2001. The introduction of options hashad no statistically discernable effect on spot market volatility.

Table 2: Descriptive StatisticsMeans and standard deviations of Index returns for sub-periods

Period NOB Nifty Nifty Junior

Mean Std. Mean Std.Deviation Deviation

1995-1999 1054 0.00033 0.01712 0.00111 0.01793

Jan00-Jun00 109 -0.00008 0.02465 -0.00363 0.03613

Jun00-2002 641 -0.00042 0.01429 -0.00096 0.01787

Table 3: Estimates of the GARCH(1,1) model withFutures dummy

tj

jjtsptrniftyjuniotnifty uDAYRRR ++++= å=

-

5

21,5002,10, aaaa

tttt Dhh 3122

110 ggegg +++=--

where D is a dummy variable that takes a value of 1 afterJune 12th 2000 and 0 before.

0a Intermcept -0.00116* -2.69

1a NiftyJunr return 0.75360* 77.25

2a Lagged S&P500 0.10380* 7.02

3a Dummy-Tue 0.00142* 2.19

4a Dummy-Wed 0.00110 1.69

5a Dummy-Thur 0.00008 1.36

6a Dummy-Fri 0.00175* 2.72

0g Arch0 0.00000* 4.03

1g Arch1 0.05310* 5.42

2g Garch1 0.92200* 68.97

3g Dummy-Futures 0.00000 0.10

* Statistically significant at the 5% level.

Total R-square= 0.6741

N=1675

Unconditional variance=0.00008427

Table 4: Estimates of the GARCH(1,1) model withOptions dummy

tj


-

5

21,5002,10, aaaa

tttt Dhh 3122

110 ggegg +++=--

where D is a dummy variable that takes a value of 1 afterJune 4th 2001 and 0 before.

0a Intercept -0.00116* -2.69


2a Lagged S&P500 0.10370* 6.94

3a Dummy-Tue 0.00142* 2.19

4a Dummy-Wed 0.00110 1.70

5a Dummy-Thur 0.00085 1.36

6a Dummy-Fri 0.00175* 2.73

0g Arch0 0.00000* 3.90

1g Arch1 0.05335* 5.42

2g Garch1 0.92170* 68.21

3g Dummy-Options 0.00000 0.01

* Statistically significant at the 5% level.Total R-square= 0.6742N=1675Unconditional variance=0.00008486

The results thus far suggest that the introduction offutures and options has had no effect on spot marketvolatility. However, in reality, one might expect a lot ofuncertainty in the market leading up to the introduction ofthe derivative contracts, which our cut-off dates are unableto capture in the model. Table 2 presents some basic statisticson the means and standard deviations of the returns for thesix months leading up to the introduction of the futurescontracts in June 2000. The standard deviation of niftyreturns up until Dec 1999 was 1.7%. Between Jan 2000 andJune 2000, the standard deviation rose to 2.5% and thenafter June 2000 dropped back to 1.4%. Interestingly, a similarpatter emerges for the Nifty Junior returns, even though nounderlying futures contracts were being introduced for stocksin this index. This was also an extremely volatility period inworld stock markets, especially the US stock markets. Theincrease in volatility in the Indian market might have been aconsequence of increased volatility in the US markets. Thiseffect is picked up by the lagged return on the S&P 500index in our model. In conclusion, we find little evidencethat the spot market volatility changed significantly as a resultof futures or options introduction.

Chart 2 plots the GARCH model predicted conditionalerror standard deviation over time. Clearly, the model is ableto capture the temporary increase in the volatility leading upto the introduction of the futures contracts in the first sixmonths of 2000. Further, one can see that if we ignore this6 month period, the volatility has not changed much beforeand after the futures introduction.


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It is interesting to explore further whether the nature ofthe GARCH process was altered as a result of the futuresintroduction. We therefore estimate the GARCH modelseparately for the pre-futures and the post-futures periodseparately. Table 5 presents the results of this estimation. Thefirst point to note in comparing the results before and afterfutures introduction is that the onset of futures trading hasaltered the nature of the volatility. Before futures, the ARCHand the GARCH effects are significant, suggesting that bothrecent news and old news had a lingering impact on spotmarket volatility. The results also show the presence of day-of-the-week effects for Tuesday and Friday. After the futuresintroduction, the day-of-the-weeks effects are no longerstatistically significant. Also the coefficient on the GARCHvariable is no longer significant, suggesting that old news hasno impact on today's spot price changes. However our sampleperiod post futures is fairly small, only 597 observations, sowe must treat these results with some caution.

Table 5: Estimates of the GARCH(1,1) model beforeand after futures introduction.

tj


-

5

21,5002,10, aaaa

122

110 --++= ttt hh gegg

BEFORE AFTEREstimate t-stat Estimate t-stat

0a Intercept -0.00148* -2.89 -0.00058 -0.78

1a NiftyJunr return 0.86490* 64.49 0.60740* 34.57

2a Lagged S&P500 0.13380* 6.87 0.08580* 3.49

3a Dummy-Tue 0.00189* 2.51 0.00159 1.45

4a Dummy-Wed 0.00040 0.53 0.00070 0.67

5a Dummy-Thur 0.00100 1.37 0.00122 1.13

6a Dummy-Fri 0.00191* 2.51 0.00146 1.30

0g Arch0 0.00000* 3.14 0.00006* 16.36

1g Arch1 0.07680* 5.52 0.07940 1.43

2g Garch1 0.90610* 56.01 0.00000 0.00

Total R-square 0.6744 0.6370

N 1078 597

Unconditional variance 0.000097 0.000071


We have thus far, tested whether there appears tobe any structural change in the underlying spot market atthe time of futures and options introduction. Now wetest to see if there is any relationship, after the futuresare introduced, between the level of futures tradingactivity and the volatility of the spot market return. Wefollow Bessembinder and Sequin (1992) and using anARIMA (p,q) model, decompose the time series of thefutures trading volume and open interest into expectedand unexpected components. The expected componentrepresents a threshold level (or average) of futures trading,and the unexpected component picks up any suddenincrease in trading volume as a result of unexpected pricechanges. Bessembinder and Sequin find that spot marketvolatility in the US market is positively related to theunexpected components of volume and open interest,and negatively related to the expected component,suggesting an increase in volatility due to unexpectedinformation , but an otherwise stabilizing influence offutures trading activity.

Using an ARIMA (1,1) model for the contracts volumeand an ARIMA (2,2) model for the Open Interest, wedecompose each series into an expected and an unexpectedcomponent. We then insert these components as additionalvariables in the conditional variance equation:

The results of this estimation are presented inTable 6. None of the coefficients on the trading activityvariables are statistically significant. This however, maybe an artifact of the rather low sample size in the postfutures period. As more data becomes available, it will beinteresting to re-estimate this model to evaluate the impactof continuing trading activity in the futures and/oroptions market on the underlying spot market. Also, indecomposing the volume indicator variab les, noadjustment was made to remove any seasonal effects likecontract expiry months, etc. An interesting topic forfurther research would be to see if adjusting for thisseasonality will have a significant impact on thedecomposition of the permanent and temporarycomponents of trading activity.

V. Conclusion

In this study, we have examined the effects of theintroduction of the Nifty futures and options contracts onthe underlying spot market volatility using a model thatcaptures the heteroskedasticity in returns that characterizestock market returns. The results indicate that derivativesintroduction has had no significant impact on spot marketvolatility. This result is robust to different modelspecifications.

CONTexDhh tttt 432

12110 ggeggg +++++=--

OIunexOIexCONTunex 765 ggg ++

Chart 2:Estimated error standard deviation from the

GARCH (1,1) model


8

Table 6: Estimates of the AUGMENTED GARCH (1,1)model after futures introduction.

tj


-

5

21,5002,10, aaaa

122

110 --++= ttt hh gegg

Estimate t-stat

0a Intercept -0.00009 -1.16


2a Lagged S&P500 0.07160* 2.83

3a Dummy-Tue 0.00202 1.82

4a Dummy-Wed 0.00104 0.95

5a Dummy-Thur 0.00158 1.39

6a Dummy-Fri 0.00179 1.58

0g Arch0 0.00006* 2.20

1g Arch1 0.09050* 1.64

2g Garch1 0.00088 0.00

3g Cont-expected 0.00002 1.39

4g Cont-unexpected -0.00000 -0.00

5g OI-expected 0.00000 0.00

6g OI-unexpected 0.00000 0.00

Total R-square 0.6430

N 594

Unconditional variance 0.000069


Cont=change in the log of the total number of contracts traded for allexpiry for the nifty futures.

OI=change in the log of the open interest for all expiry horizons fornifty futures contracts.

An ARIMA (1, 1) is used to decompose contracts series into expectedand unexpected components.

An ARIMA (2, 2) model is used to decompose the OI series intoexpected and unexpected components.

We then estimated the model separately for the preand post futures period and find that the nature of theGARCH process has changed after the introduction of thefutures trading. Pre-futures, the effect of information waspersistent over time, i.e. a shock to today's volatility, has aneffect on tomorrow's volatility and the volatility for days tocome. After futures contracts started trading the persistencehas disappeared. Thus any shock to volatility today has noeffect on tomorrow's volatility or on volatility in the future.This might suggest increased market efficiency, since allinformation is incorporated into prices immediately.However, we prefer to treat our results here with somecaution since we are estimating the GARCH model withonly two and a half years of data.

Next, using a procedure inspired by Bessembinder andSequin (1992), we find that after the introduction of futurestrading, we are unable to pick up any link between the volumeof futures contracts traded and the volatility in the spot

market. As more data becomes available, it will be interestingto explore this link once more.

It is important to emphasize that although we havesought to analyze the impact of the introduction of futures/options on spot market volatility, in reality the listing of indexderivative contracts is hardly an exogenous event. The listingis usually preceded by many decisions made by regulatorsand stock exchange officials, who in turn may be reacting toworld developments. Further, it is quite possible that theintroduction of futures and options has different impact onspot volatility depending on the trading mechanisms, contractdesigns and regulatory environments. This might explainthe rather mixed results reached by researchers in differentmarkets. Further research needs to explore the relationshipbetween these factors and the nature of spot market volatilitybefore and after derivatives trading began. As more databecomes available in the Indian market, such a study wouldbe immensely beneficial to investors, institutional traders andregulators alike.

Further, it should be noted that a relatively long timeseries , is required to obtain reliable GARCH parameterestimates. For the model estimated over the entire sampleperiod, Oct 1995-Dec 2002, this might not be a problem.However in our estimations for the post futures period, clearlythis is affects the reliability of our estimates. Unfortunately,the only solution is patience and persistence. In summary, wefind little evidence that the introduction of new stock indexfutures or options contracts in emerging markets like Indiawill destabilize stock markets. On the contrary, it appears thatthe stock markets become more efficient and information isincorporated into prices a lot faster.

ReferencesBollen, Nicolas P.B., 1998, A note on the impact of options on stock returnvolatility, Journal of Banking and Finance v22: 1181-1191.Bollerslev, t. 1986, Generalized Autoregressive ConditionalHeteroscedasticity, Journal of Econometrics 31, 307-327.Bessembinder, Hendrik and Paul J. Seguin, 1992, Futures trading activityand stock price volatility, Journal of finance 47, 2015-2034.Cao, H.Henry, 1999, The effect of derivative assets on information ininformation acquisition and price behavior in a rational expectationsequilibrium, Review of Financial Studies v12 n1: 131-163.Chatrath, A., R. Kamath, R. Chakornpipat and S. Ramchander., 1995, Lead-lag associations between option trading and cash market volatility, AppliedFinancial Economics 5(6), 373-381.Damodaran, Aswath and Marti G. Subrahmanyam, 1992, The Effects ofDerivative Securities on the Markets for the underlying assets in the UnitedStates: A Survey, Financial Markets, Institutions and Instruments 1(5),1-22.Danthine, J., 1978, Information, futures prices, and stabilizing speculation,Journal of Economic Theory 17, 79-98.Detemple, Jerome and Philippe Jorion, 1990, Option Listing and StockReturns, Journal of Banking and Finance v14: 781-801.Engle, Robert and Victor Ng, 1993, Measuring and Testing the Impact ofNews on Volatility, Journal of Finance 48, 1749-1778.Engle,Robert and Joseph Mezrich,1995, Grappling with GARCH, Risk, 8,112-117.Fama, E.F., 1965, The behavior of stock market prices, Journal of Business38, 34-105.

(Contd. on Page 15)


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R e t a i l i n g o f G ov e r n m e n t S e c u r i t i e s

Golaka C Nath*

1 Manager, NSEIL, Mumbai. The views expressed and the approach suggested in this paper are of the author and not necessarily of NSEIL.

In recent past there have been increasing debates onrole of exchange driven secondary market retail trading inGilts for deepening the bond market in India. However, fora long time, the retail participation in Gilts has beenadvocated by academician, market participants, regulatorsas well as policy makers. To encourage retail participation,RBI allowed retail investors to take part in Gilts auctionsthrough non-competitive bidding and in the process, 5% ofthe issue size was earmarked for the same. Though, therewas no bar on retail investors to buy and sell Gilts, it waslittle difficult for the retail investors to actively participate inthe market due to institutional structure of the market. TheGilts market in India, like many other countries, hastraditionally remained within the realms of institutionalplayers. The policy frameworks were designed in such a waythat the retail investors were not enthused to participate inthe market. The retail investors found solace in manysubsidized interest rate schemes and products that were moreattractive in terms of returns on investment. The financialsector reforms process ensured increasing deregulation ofinterest rate and made most of the products aligned to themarket determined rates reducing subsidy to retail investors.Though there are still few products available today thatprovide relatively higher returns though the products aresovereign in nature. However, the maturity of financial sectorreforms process has ensured moving towards a lower interestrate regime that not only has implications for the retailinvestors but also for wholesale players like banks andinstitutions.

Traditionally Gilts market in India saw wholesale marketplayers participating as these institutions, predominantlybeing part of the Government, were also supposed to helpGovernment to raise resources from the market as therewas no appetite for the same among retail investors and thiswas also institutionalized with regulatory requirement of SLRholdings. As things progressed, regulatory requirementunderwent change so also the valuation norms that allowedmarket participants to trade in Gilts. This can be explainedwith the increasing volume of trading in Gilts market. Totake the reforms process further, it was necessary to reachout to retail investors as a market needs high level of retailparticipation to grow. In the light of the above, the regulatorsthought it prudent to allow focused retail secondary markettrading in Gilts through exchanges as it is one of the bestway to reach out to a large section of investors that areconnected to stock exchanges through their networks.

Fixed-income securities markets have traditionally beendecentralized, with trading in over-the-counter (OTC)markets where the physical trading infrastructure has playeda minor role. Trades have been conducted by dealers or largeinvestors who directly contact a number of potentialcounterparties or by brokers in the professional dealermarket, with trades completed by telephone. The relativelyinformal infrastructure has served the needs of wholesalemarket participants as well as dealers, brokers, and, to a lesserextent, their institutional clients. Most fixed-income securitiesmarkets have traditionally been opaque, with scant anddelayed information on transactions available to the public.In contrast, the general transparency of most governmentsecurities markets in the world is low, reflecting the traditionalwholesale nature of the market and the perception amongsome market participants and regulators that there is a trade-off between liquidity and the level of market transparency.Institutional participation in Gilts is a must at the initiationof any market movement. The institutional participationstructure provides a solid foundation for any market but asthe market develops, it naturally brings many others includingsmall investors to its fold provided the market is perceivedas safe and liquid. International experience also tells us thatdeveloped markets like USA has a large retail investmenteven if we exclude funds that also receive large amount ofinvestment from retail investors. The USA market has awhopping 10.4% individual participation as of June 30, 2002.The experience of equity market in India gives us enoughencouragement to believe that retail participation in Giltsmarket would increase over time. The retail participation inequity market in India has very little parallel in internationalequity markets where the major role was played by investmentfunds but in India small retail investors directly participatedin the market. Hence the structure of Indian market is a bitdifferent from other developed markets and the success ofequity derivatives market in India has proved the pointbeyond any reasonable doubt. The experience of the equityas well as the derivatives market in India has clearly shownthat the cost of intermediation as well as the actual cost(implicit as well as explicit) of doing a transaction comesdown drastically when the liquidity increases and the retailinvestors enjoyed the benefit.

It has been argued and opined by eminent economiststhat next phase of growth is likely to be the fixed incomesecurities market as most governments have revealed acommon interest in fostering market liquidity and


10

policymakers have regarded the size of the governmentsecurities market as a key consideration. In industrialcountries where budget surpluses are shrinking debt, theauthorities are trying to preserve liquidity by maintaininggross issuance in specific securities even as net issuance inall securities declines. At the same time, authorities inemerging market countries view growing debt as providingan opportunity to develop domestic bond markets. Suchmarkets would help reduce not only the cost of borrowingbut also reduce institutional risk on financial system. Historysuggests that risk premiums fall as the perceived threat ofan economic downturn progressively fades.

Central banks have multiple interests in thedevelopment of Gilts markets. At a fundamental level, theGilts markets help to fund budget deficits in a non-inflationary way and so enhance the effectiveness ofmonetary policy. In addition, many central banks use Giltsmarkets for the conduct of monetary policy as well asGovernment debt, including participating in clearing andsettlement process. The experience of Mexico, Brazil andChile in developing the bond market in their respectivecountries is commendable. These countries have beenstriving to develop a more sustainable debt structure in stages.The Chilean market today boasts of holding by pensionfunds and pooled funds over 50% of GDP equivalent ofGilts and it is the most developed market in Latin America.The equity as well as fixed income securities market playsequally important roles in sustaining long term developmentin developing market.

Retail Gilts Market in India

The outstanding debt of the Government is estimated tobe about Rs.6,30,000 crores and the average cost ofborrowing has remained high as earlier borrowings have beenmade at higher rates though the cost has come downsubstantially for the new debts. However, we need tounderstand the rationality of such fall in interest rate andthe risk it poses on the financial system. Today, due toinstitutional structure of the Gilts market, institutions havebeen pumping in funds to the market as the credit off-takefrom banking industry is not encouraging and hence creatingmore demand for Gilts and reducing the yield and increasingthe price. This process may create institutional risk for thefinancial system in the long run and in the better interest ofthe all concerned, the market should be broad based as itwill not only create additional demand for the Gilt stocksbut also reduce the risk of concentration. This will result ingenuine low yield due to demand spread across varioussegments of the market and hence the lower cost ofborrowing for Government. The basic purpose ofdeveloping the retail Gilts market is to make financial marketsmore complete by generating market interest rate that reflectthe opportunity cost of funds at each maturity. Anothergeneral reason for developing retail bond market is to avoidconcentrating intermediation uniquely on banks. Since banksare leveraged, this may make the economy more vulnerableto crises. The damage caused by such crises to the real

economy is generally more vulnerable to restructuringprocess more difficult, in the absence of a well functioningand well-developed bond market. A well-developed bondmarket with active retail participation can realistically beexpected to substitute for institutions. Another reason forfostering bond market is that such a market can help theoperations of monetary policy. A well functioning Giltsmarket is essential for smooth transmission of policy asmonetary policy relies increasingly on indirect instrumentsof control. In addition, prices in the long-term bond marketgive valuable information about expectations of likelymacroeconomic developments and about reactions tomonetary policy moves. A major problem of highconcentration of institutional participation in Gilts marketis it makes the market a �captive� one which can underminethe creation of a true market in bonds, and in effect deterother investors.

No doubt, valuation of bonds by retail investors willnot be as perfect as institutional investors at the beginningwhen the market lacks liquidity but as the market developsand liquidity increased, there is no reason to believe thatretail investors will not be able to do a just valuation oftradable bonds. It requires some effort in educating the retailinvestors and the equity derivatives market in India alsoproves that with education, this handicap can be over comein the long run. Traditionally retail investors have investedin bank deposits and have taken the repricing risk as well asreinvestment risk. When an investor invests in a bank deposit,he is not really bothered about the risk profile of the bankin the balance sheet. If that had been the case, then duringlast few years nobody would have invested in deposits ofUnited Bank of India, UCO Bank and Indian Bank whosenetworth turned negative before Government support camein the form of recapitalisation. People relied on the fact thatthese banks were part of sovereign body and hence did notperceive any credit risk. The way yield is important to retailinvestor in Gilts market, the same way the interest rate in SBor fixed deposit account is equally important. The way yieldhas been falling in Gilts, the same way there has been fall ininterest rate on fixed deposits. The fall in interest rate onfixed deposits have not seen fall in outstanding fixed depositsof the banking system. Hence when investing inGovernment securities, it is not rationally correct to questionthe balance sheet of the Government. Government bondsare the backbone of most fixed-income securities marketsin both developed and developing countries as it provides abenchmark yield curve and help establish the overall creditcurve. Government bonds typically are backed by the �faithand credit� of the government, not by physical or financialassets and hence �risk free�. Any portfolio diversificationneeds the combination of risky and risk free securities. Arisk free security is bound to provide much lesser returnthan the risky security.

A retail market will not only attract retail investors who,until recently, had not been able to buy Gilts in small amountsbut also it will give opportunity to institutional investors to


11

offload securities in the market and retail investment level isvery low and has a higher possibility of growth. There aremany investors who would possibly like to buy the Giltsand hold the same till maturity, the way they invest in fixeddeposits. There is no reason to believe that retail investorswould be forced to price bonds without the benefit ofknowing the price of institutional investors are willing topay. When the market develops, at the initial stage, therewould improper or higher bid-ask spreads but as liquidityincreases, this imperfection would surely go away. There isno reason to believe that retail as well as institutional investorswould not participate in price discovery process. If themarket can create liquidity, this will not be an unsolvedmystery. Moreover, the novation or guarantee of settlementwould also make this market more attractive.

Initially, as the experience suggests, the transaction costfor the retail investors would be little higher as their size ofthe trade would be small unlike an institutional investor. Butonce the market liquidity increases, it would also fall.However, it need to be kept in mind that transaction costfor a large deal would be surely less than a small deal underany circumstances because of economies of scale. Today,the retail investor has to pay a high hidden cost in the formof late clearance of his payout cheque from the institutionalintermediary that can be removed through the mechanismof straight through processing which is in place in equitymarket in India.

In India about 65% of transactions are done with thehelp of brokers in the market and in any marketdevelopment, intermediaries play a very important anddominant role. Today institutional players have in anadvantageous position due to fragmentation of the marketinto retail and wholesale. If we close this artificial barrierand merge the wholesale and retail market together, it wouldsurely enlarge the market and remove the possiblediscrepancies and provide equal access to all as in the caseof equity and equity derivatives market. This will not onlyincrease liquidity and it would also serve the purpose ofredistribution of holdings in Gilts. This will surely makeprice discovery a process of participation by retail as well asinstitutional players.

Hence providing an exchange based retail trading ofGilts in India is a first step in the right direction as it facilitatesparticipation of all investors who were not able to participatein institutional markets directly. However, there are investorswho have been investing in the market through institutionswho have been happily charging them not only theintermediation fees but also pass on their inefficiency costto the clients. This is happening today, as investors have nochoice but to access the institutional market through theseinstitutional intermediaries. And all those who cannot directlyaccess the institutional market till now can think ofparticipating directly in the market in the new environment.A market will serve the best economic purpose if all haveequal access to participate in the same irrespective of theirfinancial clout.

There has been arguments in the press that the growthof retail market would not ensure reduction in cost ofborrowing for the Government. We have seen during lastcouple of years that the cost of borrowing has been steadilyfalling that is a market outcome and not because of institutionalparticipation in the market. Today non-competitive bids areaccepted from retail investors upto 5% of the issue size thatcan be increased once the retail participation increases. If thereis enough retail interest in the market, an IPO mechanismcan be introduced through book building system that is invogue in stock exchanges that will surely be transparent andwould produce the best price discovery mechanism. Hence itis not proper to argue that aggressive participation by retailinvestors would not help the Government to reduce itsborrowing cost. No issuer, even if it were Government, wouldlike to pay higher cost than what the issue deserves in theongoing market structure. It is necessary to understand thatrisks in the primary and secondary markets are different. Thesecondary market price would be driven by the intrinsic valueof the asset that may not exactly match the price an investorpaid in the primary market.

It is argued that as there are many sovereign productsthat provide higher yield than the Gilts of similar or highermaturities, it may not enthuse retail investors to participatein the market. It needs to be kept in mind that there hasbeen increasing rationalization of interest rate structure andsooner or later all interest rate are going to be in sync withthe Gilts rates with premium for illiquidity.

The market would be best served if the Governmentintroduces zero coupon papers in large volumes to take careof reinvestment risk but this will surely not take out theprice risk, as the movement of interest rate would determinethe same. However, there have been discussions to introduceSTRIPS to provide large stocks of various maturities as wellas various options to investors to lock into various terms asper their need.

Valuation Methodology

The market has been using YTM for valuation purposesdue to unavailability of an acceptable spot curve. If webelieve in time value of money, there is no reason why allfuture cash flows have to be discounted by the same yield.The future cash flows should be discounted with the yieldpertaining the associated term. However, the disseminationof NSE Zero Curve has helped in mitigating this problemof true valuation. Globally, spot curves are derived out ofthe coupon paying bonds and there is no reason why weshould have only zero coupon bonds if we want to have aproper spot curve. There are many methodologies likeNelson-Siegel functional form, Cubic splines, B-splines, etc.,that can be used to derive such spot curve. Stress testing ofthe same can reduce noise significantly and can be replicativeof the term structure of the market. A zero coupon yieldcurve will be useful for valuation of non-traded securities,provide model prices for all securities and can be used tocalculate the VaR numbers and can also be used to constructa benchmark index or a synthetic security.


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Another point needs to be kept in mind that developinga zero curve out of zero coupon bonds will remain a mysteryin the present Indian scenario as it will not be possible toreplace the existing outstanding into zeros unless weintroduce STRIPS. Further at any point of time, there cannotbe physically zeros trading in all maturities and hence amethod of bootstrapping and interpolation need to be doneto arrive at a zero curve. The fundamental theories behindthese methods are not sound and have little theoretical andstatistical backing. Hence a model driven determination ofterm structure is not only robust in methodology but alsowell accepted in all spheres of life in bond market.

In fact, forecasting with the yield curve does have anumber of advantages. As deregulation of interest rate inIndia has paved the way for the market to determine thetrue cost of money, by some measures, the yield curve shouldbe an even better predictor now than it has been in the past.Policymakers also need an accurate and timely predictor offuture economic growth and indicator of monetary policydirections. The central bank of the country should need itmore as the yield curve will provide him the indicative ratesat which the Government securities are going to be issuedin auction.

Conclusion

Bond markets worldwide are built on the same basicelements: a number of issuers with long-term financing needs,investors with a need to place savings or other liquid funds ininterest- earning securities, intermediaries that bring togetherinvestors and issuers, and an infrastructure that provides aconducive environment for securities transactions, ensureslegal title to securities and settlement of transactions, andprovides price discovery information. The regulatory regimeprovides the basic framework for bond markets and, indeed,for capital markets in general. Efficient bond markets arecharacterized by a competitive market structure, lowtransaction costs, low levels of fragmentation, a robust andsafe market infrastructure, and a high level of heterogeneityamong market participants.

The retail trading in Gilts has been the best thing to happento Indian financial system. It provides a facility to all whowere missing the institutional market. To take care of therisk of pricing that will percolate down to other marketparticipants, it has been suggested to use the model pricefor risk management rather than the observed prices. Thisis important in an illiquid market and a robust model willcapture the illiquidty and according charge the premia forthe same. The spreading of the market will also reduce thecaptive nature of the market. A diversified investor base forfixed-income securities is important for ensuring highliquidity and stable demand in the market. A heterogeneousinvestor base with different time horizons, risk preferences,and trading motives ensures active trading, creating highliquidity. On the other hand, even liquid markets can becomeilliquid in periods where one group of investors leaves orenters the market over a short period and where there areno counterbalancing order flows from other investor groups.

The exchange driving retail trading in Gilts provide thefollowing distinct advantages. (a) the stock exchanges arebest suited to promote retail trading in Gilts as they canreach the wide spectrum of investors located in all parts ofthe country in a very short time through their broker network;(b) it provides a vehicle for risk diversification to all investors;(c) it also provides the vehicle for efficient price discovery inprimary as well as secondary market; (d) it also provides thescope for reduction in cost of debt issuance for theGovernment; (e) it provides the scope of better moneymanagement by the RBI as it would move away from acaptive market; (f) it would also reduce institutional riskconcentration; (g) it would also allow equal access to all typesof investors to a market which was within the realms of theinstitutional market till recently; (h) it would also lead tobetter liquidity and fall in intermediation cost as we haveseen in equity markets; (i) it would provide guarantee ofsettlement to all participants in the market; (j) it would alsobring out the regulatory clarity as the retail trading in Giltsthrough stock exchanges would be guided by SEBI; (k) andit would ensure that market follows an uniform riskcontainment system for all.

In many emerging markets, the administrative andinformation technology costs of going straight to retailinvestors have been prohibitive. However, the situation inIndia ha been different with well spread out exchangesoffering connectivity at remote areas, the situation is rapidlychanging, and possibilities for cost-efficient sale anddistribution of government securities are increasing. Utilizingthe new technology to access a broader set of potentialinvestors could also have implications for the design andfunctioning of the primary market, and will put bankdominance in the retail end of the market under pressure.

The ability to use audit trails and other forms of off-market surveillance to detect trading practice violations, suchas front running and market manipulation, is also an essentialaspect of any efficient system which is not possible in anOTC market. The safeguards, which need to be compatibleacross trading systems, will be increasingly essential as adefense against systemic risk and this can be met by stockexchanges only.

Catering to the needs of retail investors is often anessential part of the overall strategy to develop a morediversified investor base for government securities. Retailinvestors will contribute to a stable demand for governmentsecurities, which, in times of volatility, can cushion the impactof sales from institutional and foreign investors. The futurewill say whether introduction of retail trading in Gilts throughstock exchanges is a correct decision or not. There is nothingwrong in providing an alternative and easy mode of avenuefor investment to investors as the existing infrastructure andsystem has been leveraged without significant additional cost.It is a choice for the investors to make. We should stopmaking decision for him and telling him whether retail tradingis good or bad, rather we should allow him to take his owndecisions given the infrastructure.

❏ ❏ ❏


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Vo l a t i l i t y I n d i c e s - A l e a d i n g M a r k e t i n d i c a t o r .

Roy Antony & Dr. Y.V. Reddy

What is a Volatility index?

Options traders have long used the volatility indices to helpthem determine market direction. The computation ofvolatility index may sound complex. But drawing referencesfrom the movement of volatility index is as simple andrealistic as reading any stock market index like the Nifty.Off late the Volatility indices have gained lot of attentionand momentum. Today it is another leading marketindicator which every trader watches closely.

Volatility is a measure of fluctuations in share price(crudely: an indicator of the share�s up/downess). One wellknown conclusion of empirical studies pertaining tosecurity markets is that the volatility of asset returns tendsto change over time. While changing volatility is apparentin most markets, it is perhaps most evident in stock markets.There are two fundamentally different approaches for thedetermination of volatility. On one hand, it is possible todetermine the historical volatility by measuring the(standard deviation) prices for any particular security orindex over a given period of time. On the other hand, onemight look for the volatility which is currently implied byoption prices, i.e. the implied volatility, based on theassumptions of the trades involved. The term impliedvolatility is obviously self-explanatory - that level of volatilitythat will calculate a fair value actually equal to the currenttrading option price. The implied volatility can be regardedas a measure of an option�s �expensiveness� in the market.

The computation of a volatility index is based on thesecond assumption that the future or current trend in themarket can be captured by the current level of impliedvolatility in the options market. Volatility index is calculatedby taking a weighted average of the implied volatility fromcalls and puts traded on an underlying. Very often put andcall option contracts on a broad stock index as underlyingis chosen for the computation.

Major volatility indices

Two of the world�s most popular measures of investors�expectations about future stock market volatility are theCBOE Volatility Index (VIX) and the CBOE NasdaqVolatility Index (VXN). The CBOE VIX and VXN indexprices of both are designed to reflect the impliedvolatilities of certain index options contracts; VIX isbased on the prices of eight S&P 100® (OEX®) indexputs and calls, while VXN is based on the prices ofNasdaq-100® (NDXSM) options prices.

The prevalence of volatility fluctuations has promptedthe CBOE to introduce, in February 1993, a MarketVolatility Index, the VIX, to assist investors in tracking the

volatility risk in the stock market. Since the introductionof the VIX, exchanges in several other countries have alsolaunched volatility indices. An example is the VDAX indexdisseminated by the Deutsche Borse which calculates theimplied volatility using the prices of option contracts onthe German stock index DAX.

Computation Methodology

To put it in one sentence, what the computation methodof a volatility index does is, the implied volatilities ofselected option contracts are calculated and these areweighted in such a manner that the volatility indexrepresents the implied volatility of a hypothetical thirty-calendar day (twenty-two-trading day), at-the-money indexoption contract. The number thus calculated is comparedwith the base or previous day�s value to ascertain whetherthe implied volatility in the market has risen /fallen orremained the same.

Without going into a great deal of detail, suffice tosay that an approximation method is used for calculatingthe implied volatilities of the option contract.

We need a couple of definitions before we proceedwith the computation of a volatility index. To be moreprecise and specific let�s take the computation methodologyused in CBOE�s volatility indices computation. As statedabove, the volatility index is constructed from the impliedvolatilities of the eight near-the-money, nearby, and secondnearby index option series. The nearby series are definedas the front-month series1. The second nearby series usesthe contract month following the nearby series.

That definition alone can be confusing, so let�s simplifyit with an example. If we are currently on the 8th of January2003 ; 22 calendar days away from January expiration, (taking 30th JAN as the expiry date) the nearby month wouldbe defined as January and the second nearby would bedefined as February.

For setting the at-the-money level of the index optionsat the current cash-settled value of the index, we wouldthen pick a Put and a Call just above that level and justbelow that level for each of the expiration months for atotal of 8 options � 2 January calls, 2 January puts, 2February calls and 2 February puts. That is the basis forthe calculation. So taking the current value of the Index at1085, here are the options we would use for the calculation:

January - 1080 Call, 1080 Put, 1090 Call and 1090 Put

February - 1080 Call, 1080 Put, 1090 Call, and 1090 Put

The first step in the calculation involves averaging the


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implied volatilities in each of the 4 groups of options asfollows:

IV1 = (IV of the JAN 1080 Call + IV of the JAN 1080 Put)/2

IV2 = (IV of the FEB 1080 Call + IV of the FEB 1080 Put)/2

IV3 = (IV of the JAN 1090 Call + IV of the JAN 1090 Put)/2

IV4 = (IV of the FEB 1090 Call + IV of the FEB 1090 Put)/2

IV here stands for implied volatility

The remainder of the calculation involvesinterpolation2 between the nearby implied volatilities (IV1and IV3) and the second nearby implied volatilities (IV2and IV4) to create hypothetical �at-the-money� impliedvolatilities for each expiration month.

The formula for interpolation in this case can be put downas the following

The at the money January implied volatility is

IV JAN = IV1 ((1090-1085) / (1090-1080)) + IV3 ((1085-1080)/ (1090-1080))

The at the money FEBRUARY implied volatility is

IV FEB= IV2 ((1090-1085) / (1090-1080)) + IV4 ((1085-1080)/ (1090-1080))

The final step is to interpolate between the IV-JAN andIV-FEB implied volatilities to create a thirty calendar-day(or 22 trading day) implied volatility.

Volatility index = IV of JAN ((Nt2 � 22) / (N

t2 � N

t1)) +

IV of FEB ((22-Nt1) / (N

t2-N

t1))

Nt1 is the number of trading days to expiration of the

nearby contract, and Nt2 is the number of trading days to

expiration of the second nearby contract. That is 16 tradingdays for Jan expiry and 36 trading days for Feb expiry(expiry dates being 30 Jan and 27th Feb respectively).

; Volatility index = IV of JAN ((36 � 22) / (36 � 16)) + IVof FEB ((22-16) / (36-16))

This will give the final volatility index number.

Relevance and applications of volatility indices

A simple analogy, if you were a farmer in an area in whichthe forecast for coming months was for an unusually severeperiod of drought and cyclones, you might find thatinsurers would like to raise the premiums that local farmersthere are required to pay for insurance against damage ofcrops. A long-term index of the general trends in the costsof insurance (or options) premiums could provide usefulinformation to buyers and sellers of those products. Ifyou are an owner of a portfolio of stocks and are interestedin seeing a measure of the general trends in the cost to

protect that portfolio with index options, the volatilityindices can give a general idea of the relative cost ofprotection. For example, if volatility index value is relativelyhigh, the index options premiums� prices would be atrelatively high levels and the options buyer would berequired to pay a relatively high price for the options tothe options seller. In this scenario, the buyer might bewilling to pay the higher price in a time of market stress.

Since the introduction of the VIX by CBOE, exchangesin several other countries have also launched volatilityindices. The usefulness of such indices is predicated onthe understanding of the risk of changing volatility infinancial assets� returns, risk that may not be measuredcorrectly by asset returns. As a result, positions in theseassets, or in derivative products based on these assets, maynot be sufficient to hedge away all the uncertaintyembedded in volatility. Contingent claims on the volatilityof assets may well be needed to increase the set of hedginginstruments available to investors. This is perhaps whatprompted several exchanges around the world to considerintroducing derivatives written on volatility indices. TheCBOE and the AMEX, for instance, have pending optionson volatility. The German Futures and Options Exchangehave already innovated and market a futures contract onthe VDAX.

Options traders have long used the volatility indices to helpthem determine market direction. A low volatility indexindicates that traders have become somewhat uninterestedin the market and generally is the forerunner to a sell off.One need to be careful while deciding the underlying thatthere is sufficient liquidity in the contracts traded on themor else the volatility index created on this may not be agood indicator of market direction. A good descriptionof the volatility indices is that it has an inverse relationshipto the market. The value of a volatility index normallyincreases as the market goes down and decreases whenthe market moves in an upward direction. A rising stockmarket is viewed as less risky and a declining stock marketmore risky. The higher the perceived risk is in stocks, thehigher the implied volatility and the more expensive theassociated options, especially puts. Hence, implied volatilityis not about the size of the price swings, but rather theimplied risk associated with the stock market. When themarket declines, the demand for puts usually increases.Increased demand means higher put prices and higherimplied volatilities. However the reverse does not happenwhen there is Bull Run. One possible explanation for thisis that in times of market turmoil, investors buy index putoptions as portfolio insurance. The excess demand forindex puts drive prices (and hence the volatility index)upward. The converse is not true, however.

1 The nearby series are defined as the front-month series provided there are at least 8 calendar days until expiration. 2 ccasionally it mayrequire to extrapolate if the required strikes are not available


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The history of the VIX3

The VIX measures the volatility of the U.S. equity market. Manyinvestors say that if the VIX goes above 35 it signals a bottom inthe stock market.

The relevance of a volatility index can be further establishedby looking at the historical values of the VIX computedby CBOE. The Figure above plots the monthly high andlow values of the VIX from January 1986 thoughDecember 1999. The most interesting fact coming out ofthe figure above is that the monthly high level of VIX has

3 Taken from the article "The Investor Fear Gauge" by Robert E. Whaley Feb 2000

had periodic spikes. For example at the time of the MarketCrash in October 1987, VIX reached its recorded level.The jump in mid-1990 occurred when Iraq invaded Kuwait;and the jump in early 1991 corresponds to UN forcesattacking Iraq. Then two sharp spikes occurred one inOctober 1997 and one in October 1998. The October 1997spike occurred following a stock market sell-off in whichthe DJIA fell 555 points. The October 1998 spike occurredin a period with general nervousness in the stock market.The VIX then returned to more normal levels in 1999.

Summary

Volatility index by definition is a measure of market risk.Historically, the volatility index has acted reliably as a fearmeasure. High levels of volatility indices are coincidentwith high degrees of market turmoil, whether the turmoilis attributable to stock market decline, the threat of war,unexpected change in interest rates, or a number of othernewsworthy events. Volatility index can be computed byanyone who has the trade data with him and this can act asa good benchmark in assessing the risk managementpolicies of the institution. Many broking firms andinstitutions already have their own volatility indices as partof their Risk management kit.

❏ ❏ ❏

(Contd. from Page 08)

Do Futures and Options trading increase stock market volatility?

Freund, Steven, P. Douglas McCann and Gwendolyn P. Webb, 1994, ARegression Analysis of the Effects of option introduction on stockvariances, Journal of Derivatives v1: 25-38.Froot, K.A., and A.F. Perold, 1991, New trading practices and short-runmarket efficiency, WP MIT.Glosten, Lawrence R., Ravi Jagannathan and David E. Rundle, 1993, Onthe Relation between the Expected Value and the volatility of the NominalExcess Return on Stocks, Journal of Finance 48, 1779-1801.Gulen, Huseyin and Stewart Mayhew, 1999, The Dynamics of InternationalStock Index Returns, Working paper, University of Georgia.Gulen, Huseyin and Stewart Mayhew, 2000, Stock Index Futures Tradingand Volatility in International equity markets, Working paper, Universityof Georgia.Hodges, Stewart, 1992, Do Derivative Instruments Increase Marketvolatility?, Options: Recent Advances in Theory and Practice vII (chapter12), Stewart Hodges, ed., Manchester University Press.Kumar, Raman, Atulya Sarin and Kuldeep shastri, 1995, The impact ofthe listing of index options on the underlying stocks, Pacific,-Basin FinanceJournal 3, 303-317.Kyle,A.S., 1985, Continuous auctions and insider trading, Econometrica53, 1315-1335.Lamoureux, Christopher G. and Sunil K. Panikkath, 1994, Variations inStock Returns: Asymmetries and other patterns, working paper.Mandelbrot,B., 1963, The variation of certain speculative prices, Journalof Business 36, 394-419.Mayhew, Stewart, 2000, The Impact of Derivatives on Cash Markets: Whathave we learned? , Working paper, University of Georgia

Mayhew, Stewart and Vassil Mihov, 2002, Another Look at option listingeffects, Working Paper, Purdue University.Mill,J.S., 1871, Principles of political economy II, 7th ed. (Longmans, Green,Reader and Dyer).Nathan Associates, 1974, Review of Initial Trading Experience at theChicago Board Options Exchange.Nelson,D., 1991, Conditional Heteroscedasticity in Asset returns: A NewApproach, Econometrica 59, 347-370.Pagan, A. and G.W.Schwert, 1990, Alternative Models for Conditional StockVolatility, Journal of Econometrics 45, 267-290.Ross, S.A., 1989, Information and volatility: The no-arbitrage martingaleapproach to timing and resolution irrelevancy, Journal of Finance 44, 1-17.Skinner, Douglas J., 1989, Options markets and stock return volatility,Journal of Financial Economics v23: 61-78.Soresu, Sorin M., 1999, The effect of options on stock prices: 1973-1995,Journal of finance, forthcoming.Stein,J.C., 1987, Informational externalities and welfare-reducingspeculation, Journal of Political Economy 95, 1123-1145.Sutcliffe,C., 1997, Stock Index Futures: Theories and International evidence,2nd ed., International Thomson Business Press.Wei,P., P.S. Poon and S.Zee, 1997, the effect of option listing on bid-askspreads, Price volatility and trading activity of the underlying OTC stocks,Review of Quantitative Finance and Accounting 9(2), 165-80.

❏ ❏ ❏

Chart 1:High and low of CBOE Market Volatility Index (VIX)


16

GOVERNMENT NEWS

I. National Advisory Committee on AccountingStandard

The Central Government constituted an advisorycommittee under the chairmanship of Shri Y H Malegamto advise the Government on the formulation and layingdown of accounting policies and accounting standardsfor adoption by companies or class of companies underCompanies Act, 1956.

Source: Financial The gazette of India, Part-II, Section 3, Sub-section(ii), dated 10th January 2003.

II. Reopening / revision of Annual accounts

In partial modification of the existing provisions onreopening/revision of annual accounts, the CentralGovernment has now issued clarification that a companycould reopen and revise its accounts even after theiradoption in the annual general meeting and filing withthe Registrar of Companies in order to comply withtechnical requirements of any law to achieve the objectof exhibiting true and fair view. The revised annualaccounts would be required to be adopted in theextraordinary general meeting or in the subsequentannual general meeting and filed with the Registrar ofCompanies.

Source: General circular No. 1/2003, Ministry of CompanyAffairs, dated 13th January 2003.

III. Debenture Redemption Reserve

The Central Government has clarified that for Housingcompanies registered with National Housing Bank underHousing Finance Companies Directions, 2001, �theadequacy� of Debenture Redemption Reserve (DRR)will be 50% of the value of debentures issued throughpublic issues and no DRR is required in the case ofprivately placed debentures.


IV. Disqualification of Directors

The Central Government has clarified that default ofprivately placed bonds / debentures / debt instrumentsby public financial institutions will not be considered asdefault to disqualify directors under section 274(1)(g)of the Companies Act, 1956.


RBI NEWS

I External Commercial Borrowings - Parking offunds abroad

It has now been decided by RBI that corporatesraising ECBs may retain the funds abroad in a bankaccount for their future forex requirements subject tothe following: (a) the debits in the account should beonly for approved purposes for which the loan is raised,(b) the payment to the overseas supplier, if any, shall bemade against usual import documents including Bill ofLading/Airway Bill. Further, documentary evidence insupport of imports made into India should be submittedto the concerned Regional Office of Reserve Bankalongwith the ECB2 return, duly certified by a CharteredAccountant., (c) the deposit held abroad should not beutilised for any fund based or non-fund based facilitiesin India, and (d) the account should be closed as soon asthe forex requirements are met and any unspent balanceshould be repatriated to India immediately.

Source: RBI Notification No. A.P.(DIR Series) Circular No.70Dated January 13, 2003

II. Overseas Investments

At present residents are not permitted to makeinvestments in equity of companies registered overseasexcept by way of setting up joint ventures or whollyowned subsidiaries. It has now been decided to permitrelaxations as under: (i) Listed Indian companies arepermitted to invest abroad in companies (a) listed on arecognised stock exchange and (b) which has theshareholding of at least 10 per cent in an Indian companylisted on a recognised stock exchange in India (as on 1st

January of the year of the investment) and suchinvestments shall not exceed 25 per cent of the Indiancompany�s net worth, as on the date of latest auditedbalance sheet (ii) Resident individuals are permitted toinvest in overseas companies indicated at (i) abovewithout any monetary limit, (iii) At present, MutualFunds are permitted to invest in ADRs/GDRs of theIndian companies and rated debt instruments, within anoverall cap of USD 500 million. It has now been decidedto permit Mutual Funds to also invest in equity ofoverseas companies indicated at (i) above. It has alsobeen decided to enhance the overall cap to USD 1 billion.Accordingly, Mutual Funds desirous of availing of thisfacility may approach the Reserve Bank after obtainingthe necessary permission from SEBI in the matter.

Source: RBI Notification No. A.P. (DIR Series) CircularNo.66 Dated January 13, 2003


17

III. Acquisition of Foreign Securities by ResidentIndividual under ESOP Scheme

The RBI circular no. A.P. (DIR Series) Circular No.16dated December 15, 2001 in terms of which a residentindividual, who is an employee or a director of an IndianOffice or branch of a foreign company or of a subsidiaryin India of a foreign company or of an Indian company,is permitted to remit upto USD 20,000 in a calendaryear for purchase of equity shares offered by the saidforeign company under Employees Stock Option(ESOP) Scheme. RBI has now been decided to removethe limit of USD 20,000 for purchase of foreignsecurities by resident individual. Accordingly, remittancesfor the acquisition of foreign securities under ESOPScheme may be permitted by authorised dealers as perthe terms of offer without any monetary limit. The otherconditions as indicated below remain unchanged.

Source: RBI Notification No. A.P. (DIR Series) CircularNo.68 Dated January 13, 2003

IV. Trading of Government Securities on StockExchanges

Trading of dated Government of India (GOI) securitiesin dematerialized form is allowed on automated orderdriven system of the National Stock Exchange (NSE),The Stock Exchange, Mumbai (BSE) and the Over theCounter Exchange of India (OTCEI). The Scheme willsubsequently be extended to GOI Treasury Bills andState Government Securities.

Source: RBI Notification No. IDMC.PDRS.No. 2896 /03.05.00 /2002-03. Dated January 14, 2003 & RBINotification No DBOD.No.FSC.BC. 60 /24.76.002/2002-03 January 16, 2003

V. Credit Exposure Norms - Measurement of CreditExposure of Derivative Products - Methodology forMeasurement

All term lending and Refinancing institutions have beenadvised to use the following criteria with effect fromApril 1, 2003: (a) the non-fund based exposures shouldalso be reckoned at 100 per cent value, instead of at 50per cent, as prescribed earlier; and (b) for determiningthe credit exposure to individual / group borrowers, theforward contracts in foreign exchange and other foreignexchange derivative products such as currency swaps,options, etc., should be included at their replacementcost in determining the individual / group borrowerexposures, (c) the methodology for arriving at the�replacement cost� of the derivatives was to be advisedby us subsequently, which is as under.

Source: RBI Notification No DBS.FID.No.C-12/ 01.02.00/2002-2003 January 20, 2003

VI. Bank financing of Equities and Investments inShares

Banks have been advised to more specifically review theirrisk management systems pertaining to capital marketexposures and exposures to stock broking entities / marketmakers. The review, which should be placed before theBoard of Directors, should, inter alia, assess the efficiencyof the risk management systems in place in the bank, assessthe extent of compliance with the guidelines issued videcircular dated 11th May 2001 referred to above, and identifythe gaps in compliance with the above guidelines forinitiating appropriate steps immediately.

Source: RBI Notification No. DBOD. Dir. BC. 63/13.07.05/2002-03Dated: January 29, 2003

VII. Public Issue of Shares and Debentures -Underwriting by Merchant Banking Subsidiaries ofCommercial Banks

In order to provide a level playing field to the merchantbanking subsidiaries of banks, it has now been decidedby the RBI that in partial modification of the earlierguidelines, the existing ceiling on underwritingcommitments prescribed therein would not be applicableto merchant banking subsidiaries of banks, withimmediate effect. The merchant banking subsidiariesof banks regulated by SEBI would, consequently, begoverned by the norms on the various aspects of theunderwriting exercise taken up by them. The prudentialexposure ceiling on underwriting and similarcommitments of banks, however, remain unchanged andthey shall be continued to be reckoned within the normsprescribed by RBI earlier on overall single borrower/issue size limits from time to time. Banks should alsoensure continued viability of their merchant bankingsubsidiaries through periodic reviews of theirperformance. Other prudential norms on capital marketexposure, asset-liability management, allocation ofadditional capital for risk weighted assets of thesubsidiaries will also continue to apply.

Source: RBI Notification No. DBOD.No.FSC.BC 66/24.01.002/2002-03 Dated: January 31, 2003

SEBI NEWS

I. Secondary Market advisory Committee

The Committee recommended a regulatory frameworkfor issuance and trading of all corporate debt securities,


18

including those issued through the private placementroute, for protecting the interests of investors as alsofor imparting a high degree of transparency to thismarket. The Committee made the followingrecommendations:

A listed company (equity / debt) issuing debt securitieseither on private placement basis or through the publicissue should have the same exhaustive disclosurerequirements as are normally required under theCompanies Act, 1956, SEBI DIP Guidelines and the listingagreement of the stock exchange(s). The company issuingdebt securities on private placement basis should beallowed to make full disclosures on the websites of thecompany, stock exchange(s) and SEBI only, provided suchsecurities are issued and traded in standard denominationof Rs. 1 million. Thus, the relaxation is only with regardto the modalities of disclosure and not with regard toquality and nature of disclosures. The companies mustappoint debenture trustees for all such issuances. Anunlisted company making private placement of debtsecurities and intending to list them should makedisclosures in the manner prescribed above. However, ifthe securities are not proposed to be listed, SEBI registeredintermediaries should be discouraged from associatingwith issuance / trading of such securities in any manner.The continuing disclosures, as stipulated under the listingagreement, shall also be made by listed companies inrespect of all outstanding debt issues. A separate listingagreement may be devised for listing all debt securities.However, if the securities of the company are alreadyotherwise listed, the company may be exempted fromcompliance with the requirements which are already beingcomplied with under the earlier listing agreement. That is,though the company would sign debt listing agreement, itwould comply with incremental requirements only. It isdesirable that the corporate debt securities are issued andtraded in demat form. With the above disclosures, thecorporate debt securities can be brought into tradingplatform of the exchanges.

Source: SEBI Press Release No. PR 21/2003 Dated January16, 2003.

II. Introduction of T+2 Rolling Settlement inEquity Market

SEBI had introduced T+5 rolling settlement in equitymarket from July 2001 and subsequently shortened thesettlement cycle to T+3 from April 2002. After havinggained experience of T+3 rolling settlement and alsotaking further steps such as introduction of STP, it isnow felt appropriate to further reduce the settlement

cycle to T+2 thereby reducing the risk in the market andto protect the interest of investors. Now SEBI hasdecided to introduce T+2 rolling settlement in Indianequity market from 1st April 2003. The calendar of events/ activities in T+2 rolling settlement would be as follows:

Confirmation of the institutional trades by the custodiansto be sent to Stock exchanges latest by 11.00 a.m. onT+1. A provision of an exception window would beavailable for late confirmations. The time limit and theadditional charges for the exception window would bedecided by the exchanges. The exchanges / ClearingHouse / Clearing Corporation would process anddownload the obligation files to the brokers� terminalslatest by 1.30 p.m. on T+1. DPs shall accept instructionsfor pay-in of securities by the investors in physical formatleast upto 4 p.m. and in electronic form by 6 p.m. onT+1. The depositories would accept the requests fromDPs till 8:00 p.m. for �same day processing�. TheDepository would permit the downloading of the pay-in files of securities and funds till 10:30 a.m. on T+2from the broker pool accounts. The Depository wouldprocess the pay-in requests and transfer the consolidatedpay-in files to the Clearing House / Clearing Corporationby 11:00 a.m. on T+2. The Exchanges / Clearing House/ Clearing Corporation would execute the pay-out ofsecurities and funds latest by 1:30 p.m. on T+2 to theDepositories and Clearing Banks and the Depositoriesand the Clearing Banks would in turn complete theprocess by 2:00 p.m. on T+2.

Source: SEBI Press Release No. PR 06/2003 Dated January03, 2003.

III. Appointment of Common Agency for Physicaland Electronic Share Registry Work

SEBI has made it mandatory that all the work related toshare registry in terms of both physical and electronicshould be maintained at a single point i.e. either in-houseby the company or by a SEBI registered R & T Agent. Ithas been advised to implement the aforesaid instructionsas early as possible but in any case not latter that February01, 2003.

Source: SEBI Press Release Ref. No. PR 02/2003 DatedJanuary 1, 2003.

IV. Secretarial Audit and Reconciliation of theAdmitted, Issued and Listed Capital

SEBI in order to protect the interest of the investorshave directed that all issuer companies must immediatelysubject themselves to a Secretarial Audit to be undertakenby a qualified Chartered Accountant or a CompanySecretary and reconcile that the total shares held in


19

CDSL, NSDL and in physical form tallies with theadmitted, issued and listed capital. The issuer companiesshall also be required to submit a quarterly audit reportto the stock exchange(s) where their original shares arelisted and any differences observed shall be brought tothe notice of the SEBI and depositories immediately.

Similarly an obligation has been cast on the Registrarsand Share Transfer Agents to reconcile the admitted,issued and listed capital to all the stock exchanges andsubmit a quarterly report to the Stock Exchanges. Inexceptional cases where the capital cannot be reconciledshall be reported to SEBI. Further, the Registrars andShare Transfer Agents will also be required to maintainproper systems and procedures in place to verify thatthe securities tendered for dematerialisation were notdematerialised earlier.

Source: SEBI Press Release Ref. No. PR-03/2003 DatedJanuary 2, 2003.

V. Introduction of Futures and Options onAdditional Stocks

SEBI considered the applications made by the NationalStock Exchange and the Stock Exchange, Mumbai forintroduction of futures and option contracts onadditional stocks found eligible by the Exchanges basedon the criteria laid down by Advisory Committee onDerivatives. SEBI granted permission to the Exchangesto introduce futures and options contracts on 31additional stocks on the F&O segment of NSE andadditional 21 stocks on F&O segment at BSE. Thecombined list of total 31 additional stocks is as underout of which on first 21 stocks trading would bepermitted both on NSE and BSE and on the remaining10 stocks, trading would be permitted only at NSE.

Source: SEBI Press Release Ref. No. PR 11/2003 DatedJanuary 9, 2003.

VI. Conver sion of Close-ended Schemes toOpen-ended Schemes

According to Regulation 33(3) of SEBI (Mutual Funds)Regulations 1996, the units of a close ended schememay be converted to open ended scheme, if the offerdocument of such scheme discloses the option and theperiod of such conversion or the unitholders areprovided with an option to redeem their units in full.The following requirements are being clarified once againin the interests of investors of the mutual funds:

A draft of the communication to unit holders shall besubmitted to SEBI which shall include the latest portfolioof the scheme in the format prescribed for half yearly

disclosures as per SEBI Circular MFD/CIR/9/120/2000 dated November 24, 2000, the details of financialperformance of the scheme since inception in themanner prescribed under the Standard Offer Documentalongwith comparison with appropriate benchmarks andthe addendum to the offer document detailing themodifications (if any) made to the scheme. Further, it isadvised that the unitholders shall be given a time periodof at least 30 days for the purpose of exercising the exitoption. The unitholders who opt to redeem theirholdings in part or full, shall be allowed to exit at theNAV applicable for the day on which such request isreceived, during the prescribed period.

Source: SEBI Circular Ref. No. MFD/CIR 22/2311/03Dated January 30, 2003.

VII. Amendment to Listing Agreement � Clause 32and Clause 41

Accounting Standards Committee of SEBI (ASC)considered the issues with regard to disclosure by listedcompanies in respect of loans/ advances and investmentsand SEBI has advised the Stock exchanges to modify thelisting agreement to incorporate disclosure of auditqualifications and actions thereon, present disclosurerequirements under listing agreement etc. and has madefollowing recommendations. The same would includedisclosures of amounts at the year end and the maximumamount of loans/ advances/ investments outstandingduring the year from both parent to subsidiary and viceversa, un-audited quarterly results of all listed companiesshall be subjected to Limited Review from the quartersending on or after June 30, 2003, publication of consolidatedfinancial results along with stand-alone financial results shallbe applicable on annual basis only. However, companiesmay have option to publish consolidated financial resultsalong with stand alone financial results on a quarterly/halfyearly basis, In addition to the above the stock exchangesshall also be required to inform Securities and ExchangeBoard of India (SEBI) in cases where companies have failedto remove audit qualifications.

Source: SEBI Circular Ref. No. SMD/Policy/Cir-2 /2003Dated January 10, 2003.

NSE NEWS

I. Introduction of Futures and Options onAdditional Securities

Consequent to the letter received from SEBI regardingintroduction of additional securities to the existing listof stocks on the derivatives segment, SEBI has now


20

conveyed to the Exchange that the introduction of thesecurities of the following 6 companies be deferred to afuture date till further review by SEBI in view of theirnames appearing in the Chapter 7 of the JPC report onthe stock market scam 2001. However, the Exchangehas been advised by SEBI that they may introduce singlestock futures contracts and stock option contracts onthe 12 additional stocks from January 31, 2003 afterobtaining final confirmation from SEBI.

II. FM inaugurates retail trading in Governmentsecurities on NSE

Trading in government securities in the retail market wasmade available to the Indian market for the first timethrough Exchanges. This facility on NSE was launchedthis morning by Hon. Union Minister for Finance andCompany Affairs Shri Jaswant Singh at New Delhi onJanuary 16, 2003. On the first day, 85 governmentsecurities were made available for trading. The tradingactivity was quite brisk and the traded value wasRs. 141.89 lakhs comprising of 154 trades on 23 securitiesspread over more than 40 clients. While the average tradesize was approx. Rs.92,000/- the spreads in the marketwere quite fine.

MARKET REVIEWThe summary statistics of different market segmentsof NSE for the month of January 2003 are presented inTable 1. All three segments taken together reported aturnover of Rs. 2,636,202 million during January 2003compared to Rs. 2,349,760 million in December 2002registering a growth of 12.19%. The market capitalisationof the securities available for trading at the end monthstood at Rs. 14,457,603 million.

Table 1: Dimensions of Market Segments of NSEMarket At the end of January 03 Turnover (Rs. in

Seg mn.)ments No. of No. of Market Dece- January

Members Securities/ Capitali- mber 02 03Contracts sation

(Rs. mn.)

CM 891 789a 5,72,2766 619,733 647,622

WDM 81 1,968 8,734,297 1,173,826 1,397,180

F&O 546 1,814b � 556,201c 591,400c

Total 899d 4,571 14,457,063 2,349,760 2,636,202

a Excludes suspended securities.b 3 index futures, 58 index options, 123 stock futures and 1,630

stock option contracts.c includes notional turnover [(strike price + premium) % quantity]

in index options and stock options.d do not add up to total because of multiple membership.

Membership

The current membership strength of the Exchange is899 and 88% of the same are corporate members. Thecomposition of members at the end of the month ispresented in Table 2. There are 13 registered professionalclearing members at the end of January 2003.

Table 2: Distribution of Membership as on January31, 2003

Consti- CM WDM CM & CM, CM & Totaltution Segment Segment WDM WDM F&O

Segments & F&O SegmentsSegments

Corporates 265 8 30 43 447 793

Individuals 26 � � � 25 51

Firms 24 � � � 31 55

Total 315 8 30 43 503 899

Capital Market

TradingThe Capital Market (CM) segment of the Exchangereported a trading volume of Rs. 647622 million withabout 24 million transactions during the current month.The average daily trading volume for the month wasRs. 28,158 million as compared to Rs. 29,510 million duringthe preceding month. On an average, over 1.04 milliontrades were transacted daily and the average transactionsize was Rs. 27,054. The demat turnover accounted nearly100% of the total turnover. Figure 1 presents the dailymovement in turnover in terms of value and quantityduring the month. The business growth of CM segmentis presented in Figure 2 and Annexure I. The details ofdaily trades are given in Annexure II.

Figure 1:

Value and Quantity of Turnover: January 2003

18,000

23,000

28,000

33,000

38,000

1-Ja

n-03

2-Ja

n-03

3-Ja

n-03

6-Ja

n-03

7-Ja

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8-Ja

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9-Ja

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10-Ja

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13-Ja

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14-Ja

n-03

15-Ja

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16-Ja

n-03

17-Ja

n-03

20-Ja

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21-Ja

n-03

22-Ja

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23-Ja

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24-Ja

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27-Ja

n-03

28-Ja

n-03

29-Ja

n-03

30-Ja

n-03

31-Ja

n-03

Tu

rno

ver

100

120

140

160

180

200

220

Tra

ded

Qua

nti

ty

Average Daily Turnover (Rs. mn.) Turnover (Rs. mn.)

Date


21

Figure 2:

Business Growth of Capital Market Segment

High Volume Securities

The '5' most traded securities during the month accountedfor 39% of the total turnover (Table 3). The shares oftop '10' and '100' securities in total turnover during themonth were 55% and 95% respectively. This indicates thattrading is concentrated in a limited number of securitiesand is very thin in a large number of securities. The detailsof top '10' securities in terms of turnover for the monthof January 2003 are presented in Annexure III.

Table 3: Contribution of Top 'N' Securities(In per cent)

Top 'N' 2001-02 December JanuarySecurities 2002 2003

5 44 47 44

10 63 62 60

25 82 83 80

50 91 92 92

100 96 97 97

High Volume Members

The share of top 'N' trading members in total turnoveris presented in Table 4. The share of top '5' and top '10'members in turnover was 10% and 16% respectively,while top '100' trading members accounted for 59% oftotal turnover during the current month.

Table 4: Share of the Top 'N' Trading Members(In per cent)

Top 'N' 2001-02 December JanuaryMembers 2002 2003

5 7 12 11

10 12 18 18

25 24 31 31

50 36 44 44

100 53 61 61

Advance/Decline Ratio

The month's advance/decline ratio representing themarket climate is presented in Annexure IV. It fell to0.78 in the January 2003 as compared to 0.91 inDecember 2002. 296 securities advanced and 378declined during the month with the largest number ofadvances taking place on January 28, 2003 when 460stocks surged in value.

City-wise Turnover

During the month, Mumbai contributed 41.62% to thetotal turnover of the exchange while the contributionsfrom Delhi and Kolkata were 18.03% and 12.20%respectively. The city-wise contribution to the turnoveris presented in Annexure V.

Market Capitalisation

At the end of the month, 789 companies, 677 listed and112 permitted, were available for trading in the CMsegment of the Exchange. The list of securities admittedfor trading on the CM segment and the list of securitiessuspended from trading during the month are presentedin Annexure VI (a) and VI (b) respectively.

The market capitalisation of securities available fortrading in the CM segment decreased to Rs. 5,722,766million in January 2003 from Rs. 6,728,620 million atend-December 2002, registering a fall of 14.9% duringthe month. The S&P CNX Nifty and CNX Junior Niftysecurities accounted for 58.76% and 6.07% of totalmarket capitalisation respectively. The growth of marketcapitalisation as well as number of companies availablefor trading in CM segment is presented in Figure 3.

Figure 3:

Growth of Market Capitalisation on CM Segment

Debentures

During the month 287 trades involving 0.09 milliondebentures for Rs. 7.21 million were transacted in theCM segment of the Exchange as against 306 trades

0

200000

400000

600000

800000

1000000

1200000

1400000

16000001-

Jan-

95

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60000

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Average Daily Turnover (Rs. mn.) Turnover (Rs. mn.)

Market Capitalisation (Rs. mn.) No. of Companies Available for Trading

-

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

Jan-9

5Ap

r-95

Jul-9

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5Jan

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Apr-

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Oct

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22

involving 0.3 million debentures for Rs. 629.7 million inthe preceding month. The details of trades in debenturestraded in the CM Segment are presented in Table 5.

Table 5: Debenture Traded in the CM Segment

YearNo. of Traded TurnoverTrades Quantity (mn.) (Rs. mn.)

1995-96 17,227 4.5 392.2

1997-98 52,278 16.1 1075.5

1998-99 47,158 10.4 857.1

1999-00 28,240 13.6 1036.7

2000-01 4,152 0.2 119.5

2001-02 9,266 5.4 588.1

Apr-Jan 03 1,943 0.4 671.10

IndicesS&P CNX Nifty

The market benchmark index S&P CNX Nifty closed at1041.85 on January 31, 2003, registering a decrease of51.65 points (4.72%) during the month as compared to1093.50 on December 31, 2002. It recorded a high of1105.6 on January 02, 2003 and a low of 1026.20 onJanuary 31, 2003. The market capitalisation of S&P CNXNifty securities decreased to Rs. 3,362,670 million as onJanuary 31, 2003 from Rs. 3,529,432 million onDecember 31, 2002, registering a fall of Rs. 166,762million (4.72%). The daily movement of S&P CNXNifty is presented in Annexure VII and in Figure 4. Thedistribution of industry-wise weightage for S&P CNXNifty is presented in Table 6.

Figure 4:

Movement of S&P CNX Nifty: January 2003

Table 6: S&P CNX Nifty Industry-wise Weightageas on January 31, 2003

Sl. Industry Weightages (%) Returns (%)No.

1 Aluminium 1.32 2.38

2 Automobiles - 2 & 3 Wheelers 3.06 (1.43)

3 Automobiles - 4 Wheelers 1.78 (6.48)

4 Banks 9.43 4.05

5 Cement and Cement Products 2.34 (3.99)

6 Cigarettes 4.70 (3.35)

7 Computers - Software 22.37 (12.88)

8 Diversified 12.89 (6.43)

9 Electrical Equipment 1.66 5.61

10 Finance - Housing 2.72 4.67

11 Food & Food Processing 2.29 1.94

12 Hotels 0.24 (6.61)

13 Lubricants 0.71 (8.02)

14 Media & Entertainment 1.02 (14.61)

15 Personal Care 0.93 1.49

16 Petrochemicals 12.09 (6.50)

17 Pharmaceuticals 8.84 2.14

18 Power 1.58 (0.25)

19 Refineries 4.65 (3.57)

20 Shipping 0.52 (7.85)

21 Steel and Steel Products 1.66 0.33

22 Tea and Coffee 0.27 (7.53)

23 Telecommunication - Services 2.92 14.17

Total 100.00

During the month 1,619 trades involving 0.4 millionNifty BeES, the Exchange Traded Fund (ETF), valuedat Rs. 44.56 million were reported.

CNX Nifty Junior

The CNX Nifty Junior Index closed at 1376.85 onJanuary 31, 2003 registering a decrease of 36.20 points(2.56%) as compared to 1413.05 on December 31, 2002.During the month, the CNX Nifty Junior reported ahigh of 1462.9 on January 22, 2002 and a low of 1353.75on January 27, 2003. The market capitalisation of CNXNifty Junior index decreased from Rs. 356,667 millionon December 31, 2002 to Rs.347,540 million on January31, 2003, i.e., a decrease of Rs. 9,127 million (2.63%).The daily movement of CNX Nifty Junior is presentedin Annexure VII and in Figure 5.

Return = - 4.72%Average daily volatility = 0.796%

Date

High Low Close

1105.60

1026.201020

1045

1070

1095

1120

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23

in the three month. CNX MidCap 200 has also given apositive return of 15.68 in the long run of one yearperiod.

Table 7: Performance of Various Indices as at endJanuary 2003

(In per cent)

Indices 1 month 3 months 6 months 1 year

S&P CNX Nifty -4.72 9.51 8.65 -3.12

S&P CNX 500 -3.07 8.26 6.01 4.84

S&P CNX Defty -4.46 10.84 10.65 -1.72

CNX Nifty Junior -2.56 9.68 -5.43 2.10

CNX MidCap 200 -3.41 8.76 1.06 15.68

CNX IT Index -12.84 5.87 21.77 -9.47

Yet another exercise of comparing the performance off ive major sectoral indices, viz., S&P CNXPetrochemicals Index, S&P CNX Finance Index, S&PCNX FMCG Index, S&P CNX Pharma Index and S&PCNX IT Index, with that of S&P CNX Nifty Indexduring January 2003 is presented in Figure 7. The S&PCNX Pharmaceutical Index and S&P CNX FMCGIndex outperformed the S&P CNX Nifty while othermajor indices underperformed S&P CNX Nifty. S&PCNX IT index proved to be the worst performer in themonth followed by S&P CNX Finance whichunderperformed during the whole period.

Figure 7:

Performance of the Select Indices: January 2003

Date

High Low Close

798.25

742.85

725

750

775

800

825

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Return = - 4.46 % Average daily volatility = 0.788%

Figure 5:

Movement of CNX Nifty Junior Index: January 2003

S&P CNX Defty

The S&P CNX Defty, the dollar representative of S&PCNX Nifty, closed at 755.10 on January 31, 2003registering a fall of 35.25 points (4.46%) over its previousmonth's close of 790.35. During the month underreview, Defty reached a high of 798.25 on January 02,2003 and touched the low of 742.28 on January 28, 2003.The daily movement of Defty is presented in AnnexureVII and in Figure 6.

Figure 6:Movement of S&P CNX Defty: January 2003

The details of individual securities in S&P CNX Niftyand CNX Nifty Junior are presented in Annexure VIIIand IX respectively.

Other Indices

An analysis of the monthly, quarterly, half yearly andyearly performance of the indices as of end-January 2003(Table 7) reveals that the indices over all have performedwell in the one month, three month and one year period.The investments made in S&P CNX Nifty securities amonth back gave a negative return of 4.72%. The S&PCNX Defty and CNX Nifty Junior proved to be thebest performer giving positive return of 10.84 and 9.68

80 .00

85 .00

90 .00

95 .00

100.00

105.00

110.00

31-D

ec-2

002

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FMCG IT Finance Petrochemicals Pharmaceutical s Ni fty

Return = -2.56%Average daily volatility = 1.051%

1462.90

11353.75

1325

1350

1375

1400

1425

1450

1475

1-Ja

n-0

3

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Date

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24

Overseas Indices

A correlation analysis for the major overseas stock marketindices was carried out for the month of January 2003.Table 8 presents the returns and the volatility of theseindices and Table 9 presents the respective correlation.

Table 8: Returns and Volatility of Select Indicesduring January 2003

(In per cent)

Indices Monthly Volatility ofReturns Returns*

DJIA -3.45 1.56

HANG SENG -0.67 0.96

FTSE 100 -9.47 1.27

NIKKEI -2.79 1.23

NASDAQ Composite -1.09 1.95

S&P CNX NIFTY -4.72 0.80

* Volatility is the standard deviation of daily returns for January 2003.

Table 9: Correlation between Select Indices duringJanuary 2003

Indices DJIA HANG FTSE NIKKEI NAS- S&PSENG 100 DAQ CNX

COM- NiftyPOSITE

DJIA 1 0.71 0.41 0.38 0.94 0.08

HANG SENG 1 0.49 0.46 0.70 0.13

FTSE 100 1 0.08 0.30 0.12

NIKKEI 1 0.30 -0.08

NASDAQ 1 -0.04Composite

S&P CNX NIFTY 1

SettlementDuring January 2003, in terms of quantity of securitiestraded, 23.28% of securities were settled by net deliveryand in terms of value, the net delivery worked out to14.60% of turnover. However, these deliveries includeonly the net deliveries made by the trading members tothe clearing corporations. The gross deliveries made byall clients would be much higher. Of total delivery, 100%of securities were delivered in demat form. These indicatepreference for settling trades in demat form and reassuressuccess of rolling settlement. The segment witnessedsubstantial reduction in the share of short and baddeliveries. Short deliveries averaged around 0.47% during

the month. The percentage of unrectified bad deliveryto delivery has also been negligible because of full dematsettlements. The details of settlement of trades on NSEare presented in Annexure X.

The corpus of the Settlement Guarantee Fund of theCapital Market Segment at the end of January 2003 wasRs 15.33 billion.

Futures & Options (F&O) Market

TradingThe total turnover in the F&O segment amounted toRs. 591,400 million in the month of January 2003 asagainst Rs. 556,201 million in December 2002. Thesegment witnessed a rise of 6.32% in the current monthover the previous month's turnover. The average dailyturnover during the month was Rs. 25717 million.

Index futures recorded a total turnover of Rs. 55,570million and the near month index futures contractrecorded the highest turnover of Rs. 46,820 millionduring the month. The movement of Nifty as comparedto Nifty futures in the month of January 2003 ispresented in Figure 8.

The stock futures recorded a total turnover ofRs. 382,990 million during January 2003. The near monthcontract expiring on January 30, 2003 recorded thehighest turnover of Rs. 328,917 million. The stockfutures are most popular among various derivativeproducts, accounting for 55.63% of total turnover inthe segment.

The index options recorded a total notional turnover ofRs. 9,400 million during the month with the near monthoption contract recording the highest notional turnoverof Rs. 4,692 million for call options and Rs. 3,097 millionfor put options.

The total turnover of stock options during the monthwas Rs. 143,530 million. The option expiring on January30, 2003 recorded the highest notional turnover ofRs. 87,055 million for call options and Rs. 36,425 millionfor put options.

The trade details of the F&O segment for the monthare presented in Table 10. It is evident that near monthcontracts are more popular than not-so-near monthcontracts; futures are more popular than options;contracts on securities are more popular than those onindices; and call options are more popular than putoptions. The business growth of F&O market segment


25

is presented in Annexure XI and Figure 9 respectively.The derivatives turnover, sub-segment wise have beenon an increasing trend. Figure 10a shows the derivativessub-segment turnover growth for the period December2001 -January 2003. The distribution of F&O volume ispresented in Figure 10b.

Table 10: Trade Details of F&O Market for January 2003

Product Contract No. of Turnover Openexpiring on Contracts (Rs. mn.)* Interest

Traded (No. ofcontractsas at end

of month)

30-Jan-2003 217,361 46,820.2 4,678**

Index 27-Feb-2003 40,675 8,551.2 9,073

Futures 27-Mar-2003 909 193.2 343

27-Apr-2003 10 2.1 7

30-Jan-2003 1,090,999 328,916.5 16,579**

Stock 27-Feb-2003 211,895 53,782.6 54,739

Futures 27-Mar-2003 1,223 288.5 429

27-Apr-2003 5 0.8 5

30-Jan-2003 21,393 4,692.1 5,854**

27-Feb-2003 4,890 1,055.9 2,661Call

27-Mar-2003 93 20.9 17

Index 27-Apr-2003 0 0.00 0

Options 30-Jan-2003 14,276 3,097.1 3,452**

27-Feb-2003 5,528 537.4 1300Put

27-Mar-2003 1 0.2 1

27-Apr-2003 0 0 0

30-Jan-2003 269,879 87,055.0 44,038**

27-Feb-2003 52,937 14,671.6 23,016Call

27-Mar-2003 60 16.5 40

Stock 27-Apr-2003 0 0 0

Options 30-Jan-2003 112,698 36,425.0 14,108**

27-Feb-2003 19,297 5,377.9 9,112Put

27-Mar-2003 18 5.2 13

27-Apr-2003 8 2.3 8

* Notional turnover [(Strike Price + Premium) % Quantity] in caseof index options and stock options.

** As on expiry day.

The F&O Segment provides a nation-wide market.Mumbai accounted for 48% of total turnover during

January 2003. The city-wise distribution of turnover ispresented in Table 11a.

Table 11a: City-wise Distribution of Turnover inF&O Segment

Sl. Location Share in Turnover (%)

No. 2001-02 Dec. 02 Jan. 03

1 Mumbai 49.08 48.05 48.04

2 Delhi/Ghaziabad 24.28 23.54 24.80

3 Kolkata/Howrah 12.60 13.97 13.63

4 Hyderabad/Secunderabad/Kukatpally 1.54 2.11 1.98

5 Chennai 2.01 2.05 1.89

6 Ahmedabad 2.25 1.64 1.58

7 Cochin/Ernakulam/Parur/Kalamserry/Alwaye 2.44 1.57 1.40

8 Others 5.80 7.27 6.68

Total 100.00 100.00 100.00

Member Trading Pattern

An analysis of the members trading across both thederivatives products in the month of January 2003indicates that the majority of members have a monthlytraded value of more than Rs. 50 million with 62members contributing more than Rs. 5,000 million. Table11b shows the turnover wise member breakup.

Table 11b: Member Trading Patter n in theDerivatives Segment

Month No. of Members

Dec 02 Jan 03

Upto Rs. 50 mn. 26 29

Rs. 50 mn. Up to Rs. 500 mn. 134 149

Rs. 500 mn. Up to Rs. 2,500 mn. 189 181

Rs. 2,500 mn. Up to Rs. 5,000 mn. 59 56

More than Rs. 5,000 mn. 54 62


26

1030. 00

1038. 00

1046. 001054. 00

1062. 00

1070. 00

1078. 00

1086. 001094. 00

1102. 00

1110. 001-Jan-03

2-Jan-03

3-Jan-03

4-Jan-03

5-Jan-03

6-Jan-03

7-Jan-03

8-Jan-03

9-Jan-03

10-Jan-03

11-Jan-03

12-Jan-03

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24-Jan-03

25-Jan-03

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D ate

NIFTY JANFU T FEBFUT MARFUT

250

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Month & Year

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0

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80,000

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Figure 8:

Movement of Nifty vs. Nifty Futures: January 2003

SettlementAll derivative contracts are currently cash settled. DuringJanuary 2003, such cash settlement amounted to Rs. 2,827million. The details of settlement are presented inAnnexure XII. The settlement of futures and of optionsinvolved Rs. 2,214 million and Rs. 613 millionrespectively.The Settlement Guarantee Fund of the F&O Segmenthad a balance of Rs. 13,154 million at the end ofJanuary 2003.

Figure 9:

Business Growth of F&O Segment

Figure 10a:

Derivatives Sub-Segment Turnover Growth(Dec 01- Jan. 03)

0

40,000

80,000

120,000

160,000

200,000

240,000

280,000

320,000

360,000

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M onth

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ue

in R

s. c

r

Ind ex Fu tu res Stock Fu tur es Ind ex O ptions Stock Options

Figure 10b:

Distribution of F&O Volume: January 2003

Wholesale Debt MarketThe WDM segment witnessed a turnover ofRs. 1,397,180 million during January 2003, as againstRs. 1,173,826 million in December 2002, recording a growthof about 19% over the previous month. The average dailyturnover during the month was Rs. 51,747 million asagainst Rs. 48,909 million in the preceding month. Dailyturnover ranged widely between Rs. 97,248 million onJanuary 15, 2003 and Rs. ,22,290 million on January 31,2003. The business growth of WDM segment sinceinception is presented in Figure 11 and Annexure XIII.

Figure 11:

Business Growth of WDM Segment

The Subsidiary General Ledger reported a turnover ofRs. 2,576,231 million during January 2003. Trades onWDM Segment accounted for 53.04% of SGL turnover.

The ten most active securities accounted for 64.95% ofthe turnover on the segment. Details of these securitiesare presented in Annexure XIV.

The market is dominated by dated governmentsecurities, which reported a turnover of Rs. 1,307,589million contributing 93.59% of total turnover duringJanuary 2003. Treasury Bills accounted for Rs. 47,200

Stock Futures65%

Index Options2%

Stock Options24%

Index Futures9%


27

Table 12: Market Capitalisation of WDM Segmentas on January 31, 2003.

Security Type Market Capitalisation Share in Total(Rs. mn.) (%)

Govt. Securities 6,627,659 75.88

PSU Bonds 395,881 4.53

State Loans 709,317 8.12

MF Units 135,651 1.55

Fin. Institutions 232,859 2.67

Treasury Bills 349,341 4.00

Corporate Bonds 192,188 2.20

Others* 91,401 1.05

Total 8734296 100.00

*Others include securitised debt and bonds of local bodies.

The FIMMDA NSE MIBID/MIBOR are based on ratespolled by NSE from a representative panel of 29 banks/institutions/primary dealers. The overnight rates aredisseminated daily to the market at 0950 (IST) and the14 day, 1 month and 3 month rates at 1145 (IST). Thedaily FIMMDA NSE MIBID/MIBOR rates for January2003 presented in Annexure XV.

Investor Grievances

Despite all precautionary measures taken by NSE andinvestors, certain grievances and issues do arise in theday-to-day functioning. The Investor Grievance Cellhandles these complaints lodged by investors againsttrading members/companies. The status of receipt anddisposal of investor grievances by the Exchange forJanuary 2003 is presented in Annexure XVI.

Arbitration

Arbitration is an alternative dispute resolutionmechanism provided by the Exchange for resolvingdisputes between the trading members and between atrading member and his client in respect of trades doneon the Exchange. The status of arbitration matters withthe Exchange till January 2003 presented in AnnexureXVII.

Inst Bonds0.75% Others

2.29%

T bills3.38%

Govt Sec93.59%

Corporates/MFs1.88%

Primary Dealers22.96%

Fin. Inst.1.79%

Indian Banks39.02%

Trading Members26.37%

Foreign Banks7.98%

million or 3.38% of the total turnover. Institutionalbonds, bonds issued by banks and other securitiesaccounted for Rs. 42,390 million or 3.03% of totalturnover. Distribution of turnover of securities tradedduring the month on WDM is presented in Figure 12.

Figure 12:

Security-wise Distribution of WDM Trades,January 2003

The domestic banks continue to be the market leaderswith about 39.02% of share in the total turnover asagainst 41.31% in the previous month. The share ofprimary dealers in turnover decreased to 22.96% inJanuary compared to 24.78% in the previous month. Theshare of foreign banks increased to 7.98% inJanuary 2003 from 5.33% in December 2002.Distribution of turnover for various categories ofparticipants is presented in Figure 13.

Figure 13:

Participant-wise Distribution of WDM Trades,January 2003

During the month, 48 securities with a totaloutstanding debt of Rs. 209,007 million were addedfor trading. Total market capitalisation of securitiesavailable on WDM segment stood at Rs. 8,734,296million on January 31, 2003. The market capitalisationof various securities on WDM segment as on January31, 2003 is presented in Table 12.


28

System and Telecom

NSE Network

The expansion of NSE network in different cities andnumber of VSATs since November 1994 is presentedin Figure 14. The list of towns and cities having NSEVSAT terminals is presented in Annexure XVIII. As atend-January 2003, NSE had 2,800 VSATs in 354 citiesacross the country. To supplement the current VSATnetwork as well as to eliminate the downtime, NSE alsoprovides TBTN (Leased Lines) links to trading members.As of Janaury 31, 2003, 853 such lines have beencommissioned. Distribution of VSATs across the citiesis presented in Table 13.

Table 13: Distribution of VSATs across cities as atend January 2003

No. of VSATs No. of Cities No. of VSATsin a City in the Cities

1 152 152

2 62 124

3 31 93

4 17 68

5 21 105

6-10 38 277

11-25 21 336

26-50 4 125

51-100 5 321

>100 3 1199

Total 354 2,800

Figure 14:

Growth of VSATs and Cities

NSE's Certification in Financial Markets(NCFM)

NCFM is a fully automated, on-line, nation-wide testingand certification system. It tests practical knowledge andskill required to operate in financial market in a secureand unbiased manner to ensure that the persons enteringthe field have minimum understanding of the market,products and regulations. The number of candidatestaking NCFM tests has increased manifold in the recentpast. Module-wise and centre-wise break up of thenumber of candidates who have taken the NCFM testby January 31, 2003 is presented in Table 14.

Table 14: Module-wise and Centre-wise Distributionof Candidates Tested

Test Deri- Capital NSDL - AMFI- Insu- FIMM- Oth- Intro- TotalCentre vatives Market Depository Mutual rance DA-NSE ers duction

Core Modules Operations Fund Modules Debt toModule Module Modules Market financial

(Basic) Plann-Module ing

Mumbai 4,620 5,416 3,124 4,887 4,201 260 438 89 21,914

Delhi 3,296 4,451 2,246 2622 2,829 102 734 60 15,418

Kolkata 1,738 1,996 915 1,521 1,862 64 285 13 7,985

Chennai 1,680 1,626 1,428 1,800 1,666 42 680 20 7,991

Hyderabad 619 949 772 837 928 39 9 16 3,947

Ahmedabad684 571 806 774 704 24 4 9 3,368

Pune 268 333 372 551 569 13 0 0 2,106

Others 1,337 1,434 1,485 4,029 253 17 0 0 8,186

Total 13,025 16,153 10,882 16,105 11,891 502 2,150 207 70,915

Month & Year

No. of Cities No. of VSATs

0

50

100

150

200

250

300

350

400

450

Jan-

95

May

-95

Sep-

95

Jan-

96

May

-96

Sep-

96

Jan-

97

May

-97

Sep-

97

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30

Date No. of Companies No. of Traded Quantity Turnover

Traded Trades (mn.) (Rs. mn.)

1-Jan-03 704 748,613 105 19,479

2-Jan-03 731 1,077,729 158 31,119

3-Jan-03 728 932,581 132 27,579

6-Jan-03 724 876,966 122 23,956

7-Jan-03 714 956,529 134 26,448

8-Jan-03 717 937,078 126 27,908

9-Jan-03 716 1,024,795 148 27,782

10-Jan-03 725 1,166,410 171 36,306

13-Jan-03 712 974,545 139 29,252

14-Jan-03 714 975,000 145 30,587

15-Jan-03 720 974,212 141 30,272

16-Jan-03 732 982,497 151 25,747

17-Jan-03 734 1,004,536 147 26,255

20-Jan-03 722 936,390 167 22,083

21-Jan-03 727 1,081,326 196 26,015

22-Jan-03 726 1,117,211 201 25,541

23-Jan-03 724 1,079,886 181 27,410

24-Jan-03 722 1,170,356 175 31,164

27-Jan-03 718 1,096,101 169 27,875

28-Jan-03 708 1,243,460 198 31,028

29-Jan-03 712 1,220,950 192 30,113

30-Jan-03 711 1,200,168 170 33,431

31-Jan-03 712 1,160,484 164 30,273

Total 763 23,937,823 3,634 647,623

ANNEXURE IITRADE STATISTICS: JANUARY 2003

* Average Impact Cost is calculated for an execution of a Rs. 50 lakh portfolio in a security, in proportion of it's market capitalisation to that of the marketcapitalisation of S&P CNX Nifty.

Securities No. of Traded Turnover Average Daily Share in Average

Trades Quantity (Rs. mn.) Turnover Total Impact

(Mn. Shares) (Rs. mn.) Turnover (%) Cost (%)*

INFOSYS TECHNOLOGIES LTD 813,482 19 84,548 3,676 13.06 0.05

SATYAM COMPUTER SERVICES 1,665,091 309 77,581 3,373 11.98 0.04

MASTEK LTD 1,039,477 85 46,947 2,041 7.25 0.06

DIGITAL GLOBALSOFT LTD. 1,005,821 78 45,118 1,962 6.97 0.05

HINDUSTAN PERTOLEUM CORP 896,200 96 28,687 1,247 4.43 0.07

HEXAWARE TECHNOLOGIES LTD 1,346,239 190 25,990 1,130 4.01 0.10

RELIANCE INDUSTRIES LTD 806,305 89 25,556 1,111 3.95 0.06

WIPRO LTD 430,658 13 20,101 874 3.10 0.09

VISUALSOFT TECHNOLOGIES L 625,435 87 19,280 838 2.98 0.07

STATE BANK OF INDIA 521,062 53 15,233 662 2.35 0.06

Total of Top Ten Securities 9,149,770 1,018 389,042 16,915 60.07

Total 23,937,823 3,634 647,622 28,158 100.00

ANNEXURE IIITOP TEN SECURITIES ON THE CM SEGMENT: JANUARY 2003


31

Month/Date Advances Declines Advance /Decline Ratio

Apr-00 394 583 0.68May-00 447 485 0.92June-00 448 485 0.92July-00 418 487 0.86Aug-00 419 427 0.98Sep-00 365 486 0.75Oct-00 352 425 0.83Nov-00 455 382 1.19Dec-00 424 431 0.99Jan-01 399 451 0.89Feb-01 405 478 0.85Mar-01 318 499 0.64Apr-01 372 421 0.88May-01 426 401 1.06June-01 328 474 0.69July -01 395 525 0.75Aug-01 480 488 0.98Sep-01 432 541 0.80Oct-01 527 446 1.18Nov-01 597 458 1.30Dec-01 481 551 0.87Jan-02 342 372 0.92Feb-02 361 361 1.00Mar-02 342 374 0.91Apr-02 381 354 1.08May-02 329 412 0.80Jun-02 412 352 1.17Jul-02 304 464 0.66Aug-02 355 376 0.94Sep-02 302 419 0.72Oct-02 301 369 0.82Nov-02 374 297 1.26Dec-02 420 253 0.911-Jan-2003 396 249 1.592-Jan-2003 248 443 0.563-Jan-2003 280 395 0.716-Jan-2003 182 500 0.367-Jan-2003 258 404 0.648-Jan-2003 455 214 2.139-Jan-2003 432 247 1.7510-Jan-2003 227 453 0.5013-Jan-2003 274 383 0.7214-Jan-2003 369 287 1.2915-Jan-2003 338 337 1.0016-Jan-2003 426 260 1.6417-Jan-2003 352 321 1.1020-Jan-2003 270 406 0.6721-Jan-2003 262 426 0.6222-Jan-2003 334 341 0.9823-Jan-2003 196 492 0.4024-Jan-2003 100 595 0.1727-Jan-2003 79 610 0.1328-Jan-2003 460 211 2.1829-Jan-2003 333 333 1.0030-Jan-2003 274 395 0.6931-Jan-2003 270 402 0.67

Average 296 378 0.78

ANNEXURE IVADVANCES/DECLINES (NO. OF SECURITIES)

ANNEXURE VCITY-WISE CONTRIBUTION TO TURNOVER ON CM SEG-MENTSl. City Share in Turnover (%)

No. 2000-01 2001-02 Dec-02 Jan-03

1 Ahmedabad 2.68 2.49 2.20 2.15

2 Amritsar _ 0.17 0.09 0.08

3 Bangalore 1.69 2.79 2.29 2.32

4 Baroda 0.73 0.62 0.73 0.64

5 Bhubaneshwar 0.04 0.07 0.04 0.06

6 Bhavanagar 0.17 0.15 0.12 0.12

7 Chandigarh/Mohali/ 0.86 1.02 0.96 0.90Panchkula

8 Chennai 3.4 3.56 3.52 3.39

9 Cochin/Ernakulam/ 0.75 0.79 0.83 0.76Parur/Kalamserry

10 Coimbatore 0.59 0.60 0.43 0.40

11 Delhi/Ghaziabad 17.03 19.40 18.22 18.03

12 Gauhati 0.11 0.12 0.01 0.02

13 Gajuwaka/Vishakhapatnam 0.95 1.40 0.79 0.83

14 Hyderabad/Secunderabad/ 2.3 2.85 3.26 3.13Kukatpally

15 Indore 1.13 1.08 0.72 0.85

16 Jaipur 1.06 1.16 1.37 1.39

17 Kanpur 0.55 0.95 0.74 0.62

18 Kolkata/Howrah 8.24 9.15 12.73 12.20

19 Lucknow 0.16 0.24 0.18 0.20

20 Ludhiana 0.18 0.53 0.43 0.43

21 Mumbai 48.35 40.20 40.22 41.62

22 Patna 0.08 0.11 0.11 0.12

23 Nagpur 0.15 0.13 0.28 0.20

24 Pune 1.07 1.03 1.06 1.15

25 Mangalore 0.09 0.12 0.09 0.10

26 Rajkot 0.38 0.29 0.35 0.30

27 Surat 0.41 0.48 0.43 0.49

28 Vijayawada 0.33 0.36 0.24 0.26

29 Others 6.52 8.17 7.31 7.27

Total 100 100.00 100.00 100


32

SL. SYMBOL NAME OF COMPANY DESCRIPTION DATE NO. OF ISSUE

NO. OF SECURITIES PRICEALLOTMENT ISSUED (Rs.)

I. New Issues By Listed Companies

A. Equity Shares

1 ARHAMFISCL Color Chips Limited Equity shares of Rs. 10/- each 10-Jul-02 1,000,000 102 AUROPHARMA Aurobindo Pharma Ltd Equity shares of Rs.10/- each issued at price Rs.226/- 20-Mar-02 400,000 2263 AUROPHARMA Aurobindo Pharma Ltd Equity shares of Rs.10/- each issued at price Rs.226/- 14-Apr-02 1,000,000 2264 AUROPHARMA Aurobindo Pharma Ltd Equity shares of Rs.10/- each issued at price Rs.226/- 27-Mar-02 70,000 2265 AUROPHARMA Aurobindo Pharma Ltd Equity shares of Rs.10/- each issued at price Rs.226/- 12-Apr-02 530,000 2266 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares allotted on Preferential Basis to Affinity 5-Jul-01 8,000,000 225

Investments Limited7 JISLJALEQS Jain Irrigation Systems Ltd. Equity shares of Rs.10/- each 12-Jul-02 1,432,334 14.28 VISESHINFO Visesh Infosystems Ltd. Equity shares of Rs.10/- each issued on preferential basis 20-Aug-02 2,225,000 23

at a price of Rs.23/- per share to Infotecnics India Ltd9 HDFC Housing Development Equity shares of Rs.10/- each allotted as bonus in the

Finance Corporation Ltd. ratio of 1:1 30-Dec-02 121,884,38310 PENTSFWARE Pentamedia Graphics Limited Equity shares of Rs.10/- each 4-Oct-02 7,495,82911 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 3-Jan-00 50 112.512 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 1-Dec-99 250 112.513 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 18-Apr-02 200 5014 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 6-Dec-01 200 5015 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 10-Oct-01 1,500 5016 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 27-Apr-01 200 5017 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 3-Jan-00 50 10018 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity Shares of Rs.10/- each 1-Dec-99 250 10019 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares allotted out of shares kept in abeyance at time 5-Jul-01 2,019

of rights issue in 1992 as bonus-1:120 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 18-Apr-02 200 56.2521 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 6-Dec-01 200 56.2522 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- 10-Oct-01 1,500 56.2523 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each 27-Apr-01 200 56.2524 ACC Associated Cement Co. Ltd Equity shares of Rs 10/- each issued under the ESOS 31-Oct-02 4,000 10825 CRISIL The Credit Rating Information Equity shares of Rs.10/- each issue under the ESOS 12-Sep-02 34,300 105.55

Services of India Ltd26 CYBERTECH Cybertech Systems And Equity shares of Rs. 10/- each issued under the ESOS 21-Oct-02 1,800 11.2

Software Ltd.27 DIGITALEQP Digital GlobalSoft Ltd. Equity shares of Rs.10/- each issued under the ESOP 6-Dec-02 2,000 502.1528 DIGITALEQP Digital GlobalSoft Ltd. Equity shares of Rs.10/- each issued under ESOP 6-Dec-02 4,750 224.4529 DIGITALEQP Digital GlobalSoft Ltd. Equity shares of Rs.10/- each allotted under ESOP 19-Dec-02 500 224.4530 DIGITALEQP Digital GlobalSoft Ltd. Equity shares of Rs.10/- each allotted under ESOP 19-Dec-02 3,000 484.9531 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 10-May-02 10,850 13832 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity Shares of Rs.10/- each allotted under ESOS 4-Jun-02 4,200 13833 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 18-Apr-02 9,200 13834 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs. 10/- each allotted under ESOS 31-Mar-01 400 13535 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 10-Jan-02 7,000 13836 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 8-Feb-02 10,550 13837 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 8-Mar-02 8,500 13838 GUJAMBCEM Gujarat Ambuja Cement Ltd Equity shares of Rs.10/- each allotted under ESOS 6-Dec-01 5,000 13839 HCLTECH HCL Technologies Ltd Equity shares of Rs. 2/- each issued under the ESOP 25-Nov-02 86,474 127.540 HCLTECH HCL Technologies Ltd Equity shares of Rs 2/- each issued under the ESOP 10-Dec-02 33,972 127.541 HCLTECH HCL Technologies Ltd Equity shares of Rs 2/- each issued under the ESOP 10-Dec-02 6,410 3.542 ICICIBANK ICICI Bank Ltd Equity Shares of Rs.10/- each allotted under ESOS 11-Dec-02 3,000 10543 MASTEK Mastek Ltd Equity shares of Rs.5/- each 17-Sep-02 21,282 8044 POLARIS Polaris Software Lab Limited Equity shares of Rs. 5/- each under the Associate 26-Oct-02 122,685 57

Stock Option Plan 200145 SATYAMCOMP Satyam Computer Services Ltd Equity shares of Rs.2/- each issued under ESOC 23-Oct-02 200 171.0546 WIPRO Wipro Ltd Equity shares of Rs.2/- each issued under ESOP 2-Dec-02 200 133847 WIPRO Wipro Ltd Equity shares of Rs.2/- each issued under ESOP 2-Dec-02 4,424 108648 WIPRO Wipro Ltd Equity shares of Rs.2/- each allotted under ESOP 6-Dec-02 14,918 108649 WIPRO Wipro Ltd Equity shares of Rs.2/- each allotted under ESOP 6-Dec-02 100 103250 WIPRO Wipro Ltd Equity shares of Rs.2/- each allotted under ESOP 23-Dec-02 9,775 108651 PENTSFWARE Pentamedia Graphics Limited Equity shares issued at a price of USD 0.38 per share 25-Nov-02 62,500,000 18.33

on cash basis as shares underlying 6,25,00,000(1 GDR = 1 Equity share) Global Depository Receipts(Issue price Rs.18.33)

(Contd....)

ANNEXURE VI (a)SECURITIES INTRODUCED: JANUARY 2003


33

SL. SYMBOL NAME OF COMPANY DESCRIPTION DATE NO. OF ISSUE

NO. OF SECURITIES PRICEALLOTMENT ISSUED (Rs.)

ANNEXURE VI (b)SECURITIES SUSPENDED: JANUARY 2003

SL. SYMBOL COMPANY NAME SECURITY DESCRIPTION SUSPENSION REASONNO. DATE FOR

SUSPENSION

1 ICICI1199 ICICI Limited ICICI Nov-99 Deep Discount Bond Section 88 24-Jan-03 Final Redemption2 ICICI1199 ICICI Limited ICICI Nov-99 Deep Discount Bond Section 54EA 24-Jan-03 Final Redemption3 POLYCHEM* Polychem Ltd Equity shares of Rs.10/- each 10-Jan-03 Captial reduction as per

BIFR Order4 UTIMVUP98 Unit Trust of India - Master Value Units of Master Value Unit Plan 1998 8-Jan-03 Conversion of scheme into

Unit Plan 1998 open ended scheme5 JKCORP JK Corp Ltd. Equity shares of Rs.10/- each 21-Jan-03 Withdrawal of dealings6 PADMINPOLY Padmini Technologies Limited Equity shares of Rs.10/- each 21-Jan-03 Withdrawal of dealings7 UTIUS64OCI Unit Trust of India Units of UTI US 64- Cash Income scheme of 20-Jan-03 Government of India

Unit Trust of India (post split ) announcing a specialpackage for repurchase ofUS-64 & subsequentlysplitting of demat units into3 categaories having 3

* Polychem Limited is already under suspension for non-compliance since 11/11/02Retail Debt Market1 1100G06 Government of India GOI LOAN 11% 2006 21-Jan-03 Shut period before interest

payment2 1130G10 Government of India GOI LOAN 11.30% 2010 21-Jan-03 Shut-period before interest

payment3 1143H15 Government of India GOI LOAN 11.43% 2015 30-Jan-03 On account of corporate

action for interest payment4 1150H11 Government of India GOI LOAN 11.50% 2011 29-Jan-03 For interest payment on

05/02/03

(Contd....)

DEBT1 IDBI1102 Industrial Development Infrastructure (Tax Saving) Bond - Option A 25-Nov-02 462,779 5000

Bank of India2 IDBI1102 Industrial Development Infrastructure (Tax Saving) Bond - Option B 25-Nov-02 27,219 5000

Bank of India3 IDBI1102 Industrial Development Infrastructure (Tax Saving) Bond - Option C 25-Nov-02 205,119 5000

Bank of India4 IDBI1102 Industrial Development Infrastructure (Tax Saving) Bond - Option D 25-Nov-02 25,303 5000

Bank of India5 IDBI1102 Industrial Development Growing Interest Bond 25-Nov-02 2,411 5000

Bank of India6 IDBI1102 Industrial Development Money Multiplier Bond - Option A 25-Nov-02 7,437 7500

Bank of India7 IDBI1102 Industrial Development Money Multiplier Bond - Option B 25-Nov-02 9,487 10000

Bank of India8 IDBI1102 Industrial Development Money Multiplier Bond - Option C 25-Nov-02 5,492 12500

Bank of India9 IDBI1102 Industrial Development Regular Income Bond - Option A 25-Nov-02 101,522 5000

Bank of India10 IDBI1102 Industrial Development Regular Income Bond - Option B 25-Nov-02 6,020 5000

Bank of India11 IDBI1102 Industrial Development Regular Income Bond - Option D 25-Nov-02 174,954 5000

Bank of India12 IDBI1102 Industrial Development Regular Income Bond - Option E 25-Nov-02 2,552 5000

Bank of India13 IDBI1102 Industrial Development Regular Income Bond - Option C 25-Nov-02 8,278 5000

Bank of India14 IDBI1102 Industrial Development Regular Income Bond - Option F 25-Nov-02 1,376 5000

Bank of India

PREFERENCE SHARES1 MARICOIND Marico Industries Ltd. 8% Redeemable Preference Shares of Rs. 10/- each allotted 30-Sep-02 29,000,000

as bonus in the ratio of 1:12 SUNPHARMA Sun Pharmaceuticals 6% Cumulative Redeemable Preference Shares of Re 1/- each 2-Nov-02 187,177,232

Industries Ltd issued as bonus in the ratio of 4:1

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34

S&P CNX Nifty CNX Nifty Junior S&P CNX DeftyDate

Open High Low Close Open High Low Close Open High Low Close

1-Jan-03 1093.60 1102.10 1093.60 1100.15 1413.25 1424.85 1413.20 1416.35 789.95 796.05 789.95 794.65

2-Jan-03 1100.55 1105.60 1091.20 1093.05 1416.10 1424.00 1398.60 1400.85 794.65 798.25 787.90 789.20

3-Jan-03 1094.45 1099.85 1087.30 1089.60 1401.95 1411.45 1389.10 1391.25 789.70 793.60 784.60 786.20

6-Jan-03 1089.75 1093.05 1081.25 1084.35 1391.15 1397.95 1385.05 1387.10 786.70 789.05 780.50 782.80

7-Jan-03 1084.40 1089.85 1078.95 1081.80 1387.75 1400.85 1385.50 1388.85 782.95 786.90 779.05 781.10

8-Jan-03 1082.40 1091.45 1082.30 1089.35 1389.15 1410.10 1389.15 1406.65 781.65 788.20 781.60 786.70

9-Jan-03 1089.75 1099.15 1087.75 1097.35 1406.60 1428.85 1406.30 1426.95 787.00 793.75 785.55 792.45

10-Jan-03 1097.60 1103.25 1077.40 1080.25 1427.65 1438.00 1418.10 1420.90 793.30 797.40 778.75 780.80

13-Jan-03 1080.25 1080.40 1070.75 1073.75 1420.25 1429.35 1413.40 1422.45 780.10 780.25 773.25 775.40

14-Jan-03 1072.70 1080.80 1070.30 1078.95 1422.50 1432.00 1422.50 1427.30 775.35 781.15 773.60 779.85

15-Jan-03 1077.90 1087.50 1077.80 1085.00 1427.95 1444.30 1427.90 1437.00 779.25 786.20 779.15 784.40

16-Jan-03 1085.05 1091.35 1083.95 1088.35 1437.00 1447.00 1435.45 1441.50 784.05 788.65 783.30 786.50

17-Jan-03 1089.35 1090.25 1083.20 1086.50 1441.70 1451.10 1437.20 1443.90 787.45 788.15 783.10 785.45

20-Jan-03 1086.25 1087.00 1074.20 1076.35 1443.60 1448.20 1437.35 1440.75 785.30 785.80 776.55 778.15

21-Jan-03 1076.30 1080.50 1074.10 1077.90 1440.85 1462.25 1440.00 1455.70 777.75 780.80 776.20 778.95

22-Jan-03 1078.45 1086.20 1073.60 1082.90 1456.60 1462.90 1441.25 1444.15 779.35 784.90 775.85 782.55

23-Jan-03 1081.40 1083.15 1069.05 1070.90 1443.90 1451.30 1430.30 1434.70 782.10 783.40 773.15 774.50

24-Jan-03 1070.60 1075.20 1053.40 1056.05 1434.80 1444.45 1398.75 1402.25 774.45 777.75 762.00 763.90

27-Jan-03 1057.80 1059.35 1030.35 1037.65 1402.35 1413.30 1353.75 1361.80 765.35 766.45 745.50 750.80

28-Jan-03 1036.35 1047.95 1026.75 1046.20 1362.25 1383.90 1357.10 1381.45 749.85 758.20 742.85 756.95

29-Jan-03 1059.80 1059.80 1033.30 1037.20 1383.15 1402.05 1383.05 1386.70 766.60 766.60 747.50 750.30

30-Jan-03 1037.15 1050.55 1031.35 1034.60 1386.65 1397.15 1378.35 1382.00 751.55 761.25 747.30 749.70

31-Jan-03 1034.75 1044.25 1026.20 1041.85 1381.45 1384.10 1370.95 1376.85 749.95 756.80 743.75 755.10

ANNEXURE VIIMOVEMENT OF INDICES: JANUARY 2003


35

ANNEXURE VIIIS&P CNX NIFTY INDEX: JANUARY 2003Sl. Name of Issued Capital Market Weightage Beta R2 Average Daily Monthly ImpactNo. Security (Rs. mn.) Capitalisation (%) Volatility Return Cost

(Rs. mn.) (%) (%)

1 ABB 424 11,829 0.35 0.39 0.04 1.88 12.25 0.172 ACC 1,714 24,473 0.73 0.96 0.30 1.63 (13.54) 0.063 BAJAJAUTO 1,012 51,948 1.54 0.68 0.14 1.54 2.01 0.084 BHEL 2,448 43,849 1.30 1.04 0.24 1.81 3.82 0.115 BPCL 3,000 57,600 1.71 1.09 0.13 2.05 (11.38) 0.086 BRITANNIA 259 13,219 0.39 0.15 0.03 0.42 (0.45) 0.217 BSES 1,378 30,577 0.91 0.38 0.06 0.72 (0.05) 0.098 CASTROL 1,235 23,879 0.71 0.36 0.11 0.96 (8.02) 0.129 CIPLA 600 49,672 1.48 0.24 0.03 1.61 (7.82) 0.09

10 COLGATE 1,360 18,345 0.55 0.13 0.01 0.55 0.04 0.1311 DABUR 286 12,973 0.39 0.40 0.07 2.03 3.53 0.1312 DIGITALEQP 329 18,922 0.56 1.62 0.39 3.35 (8.51) 0.0413 DRREDDY 383 69,147 2.06 0.79 0.17 1.76 0.58 0.0714 GLAXO 745 22,294 0.66 0.38 0.05 1.03 (1.93) 0.115 GRASIM 917 29,825 0.89 0.63 0.19 1.23 3.32 0.1416 GUJAMBCEM 1,552 24,504 0.73 0.79 0.22 1.42 (3.34) 0.1317 HCLTECH 577 46,744 1.39 1.72 0.31 2.31 (13.25) 0.118 HDFC 2,439 91,495 2.72 0.23 0.03 0.90 4.67 0.1619 HDFCBANK 2,820 66,873 1.99 0.31 0.04 1.91 9.74 0.1220 HEROHONDA 399 50,910 1.51 0.96 0.16 1.44 (4.94) 0.0821 HINDALCO 737 44,224 1.32 0.38 0.08 0.66 2.38 0.122 HINDLEVER 2,201 376,743 11.20 0.89 0.33 0.75 (5.81) 0.0923 HINDPETRO 3,393 98,779 2.94 1.34 0.15 2.19 0.99 0.0724 ICICIBANK 6,130 91,894 2.73 0.94 0.13 1.87 6.69 0.1225 INDHOTEL 451 8,001 0.24 0.52 0.10 1.23 (6.61) 0.1926 INFOSYSTCH 331 284,551 8.46 1.41 0.43 2.29 (9.91) 0.0427 IPCL 2,502 22,458 0.67 0.68 0.05 2.61 13.75 0.128 ITC 2,475 158,098 4.70 0.57 0.13 1.21 (3.35) 0.0629 L&T 2,487 46,126 1.37 0.75 0.27 1.17 (13.03) 0.0630 M&M 1,105 10,717 0.32 1.32 0.34 2.06 (14.01) 0.0831 MTNL 6,300 73,238 2.18 1.17 0.16 4.80 22.63 0.1332 NESTLE 964 52,026 1.55 0.18 0.03 1.06 3.07 0.1633 NIIT 386 5,202 0.15 1.99 0.30 4.07 (28.10) 0.0634 NOVARTIND 159 8,279 0.25 0.59 0.11 1.57 (3.49) 0.1335 ORIENTBANK 1,925 10,349 0.31 0.71 0.12 4.28 8.15 0.1536 RANBAXY 1,855 120,627 3.59 0.68 0.14 1.22 9.52 0.0637 RELIANCE 13,964 384,074 11.42 1.21 0.45 1.64 (7.69) 0.0638 SATYAMCOMP 629 70,426 2.09 2.00 0.58 2.68 (19.62) 0.0339 SBIN 2,823 148,101 4.40 0.90 0.30 1.54 (0.44) 0.0540 SCI 5,263 17,559 0.52 1.54 0.18 3.20 (7.85) 0.1341 SMITKLBECH 454 11,808 0.35 0.14 0.01 0.80 (0.34) 0.2642 SUNPHARMA 468 27,164 0.81 0.26 0.04 1.76 (3.41) 0.243 TATACHEM 1,806 10,540 0.31 1.09 0.19 2.99 0.34 0.1344 TATAPOWER 2,035 22,613 0.67 1.03 0.39 1.18 (0.54) 0.145 TATATEA 562 9,043 0.27 0.91 0.24 1.22 (7.53) 0.146 TELCO 3,198 49,129 1.46 1.36 0.33 1.68 (4.83) 0.0647 TISCO 3,681 55,975 1.66 1.11 0.31 1.41 0.33 0.0448 VSNL 2,850 25,109 0.75 0.54 0.07 2.68 (10.51) 0.1349 WIPRO 465 326,375 9.71 1.64 0.36 2.36 (13.97) 0.0850 ZEETELE 413 34,362 1.02 1.86 0.35 2.88 (14.61) 0.06

Total 95,890 3,362,669 100 1.00 � 0.80 �4.72 0.10

Note : * Beta & R2 are calculated for the period 01-Feb-2002 to 31-Jan-2003* Beta measures the degree to which any portfolio of stocks is affected as compared to the effect on the market as a whole.* The coefficient of determination (R2) measures the strength of relationship between two variables the return on a security versus that of the

market.* Volatility is the Std. deviation of the daily returns for the period 01-Jan 2003 to 31-Jan-2003* Last day of trading was January 31, 2003


36

Sl. Name of Issued Capital Market Weightage Beta R2 Average Daily MonthlyNo. Security (Rs. mn.) Capitalisation (%) Volatility Return

(Rs. mn.) (%) (%)

1 APOLLOTYRE 363 4,552 1.31 0.99 0.12 1.97 (6.77)2 ASHOKLEY 1,189 11,286 3.25 0.88 0.15 2.57 (3.75)3 AUROPHARMA 222 5,499 1.58 0.54 0.19 2.39 18.324 BANKBARODA 2,960 21,519 6.19 1.13 0.31 3.06 (4.59)5 BANKINDIA 4,886 18,200 5.24 1.12 0.30 3.39 0.686 BEL 800 16,048 4.62 1.50 0.27 2.56 15.697 BOOTSPHARM 162 5,003 1.44 0.20 0.04 1.62 8.548 CARRIERAIR 234 2,296 0.66 0.02 0.00 0.62 (1.01)9 CHENNPETRO 1,437 4,097 1.18 1.23 0.29 1.65 4.59

10 CMC 152 7,588 2.18 1.12 0.15 3.90 (1.42)11 CONCOR 650 15,130 4.35 1.02 0.26 1.21 1.5012 CORPBANK 1,434 20,247 5.83 0.93 0.22 3.79 15.2713 EMERCK 169 4,048 1.16 0.51 0.18 0.96 (5.00)14 ESCORTS 722 3,088 0.89 0.87 0.20 1.66 (7.87)15 FINCABLES 343 3,037 0.87 0.26 0.05 0.87 (7.96)16 GERMANREM 82 1,872 0.54 0.17 0.03 1.33 (9.56)17 GESHIPPING 1,903 6,519 1.88 0.78 0.21 1.65 5.2218 GLOBLTRUST 1,214 2,015 0.58 1.41 0.34 3.06 (4.60)19 GLOBALTELE 708 5,295 1.52 1.62 0.31 3.89 (21.88)20 HIMACHLFUT 1,332 4,103 1.18 1.67 0.31 5.25 (19.69)21 HINDZINC 4,225 7,542 2.17 0.66 0.11 1.88 0.8522 HOECHST 230 6,669 1.92 0.24 0.05 1.75 (1.26)23 HUGHESSOFT 336 4,821 1.39 1.42 0.25 4.79 (27.56)24 ICI 409 4,886 1.41 0.84 0.23 1.89 1.0125 IFCI 6,387 3,449 0.99 1.56 0.14 2.46 (5.26)26 INDIACEM 1,395 2,254 0.65 0.79 0.22 1.32 (16.32)27 INDSHAVING 326 9,525 2.74 0.93 0.21 1.25 (6.40)28 INDOGULF 2,252 12,049 3.47 0.75 0.14 0.84 4.2929 INGERRAND 316 6,634 1.91 0.66 0.11 1.83 (9.71)30 KIRLOSKCUM 1,980 10,306 2.97 0.57 0.13 2.08 7.1031 KOTAKMAH 592 9,477 2.73 1.11 0.15 2.54 (7.94)32 LICHSGFIN 751 4,747 1.37 1.14 0.27 2.27 (1.48)33 MADRASCEM 120 4,373 1.26 0.19 0.03 1.59 (4.58)34 MOREPENLAB 905 2,186 0.63 0.99 0.15 2.18 (19.77)35 MOSERBAER 258 5,399 1.55 0.58 0.07 4.48 35.2936 NICOLASPIR 380 8,769 2.52 0.60 0.20 1.39 (5.60)37 ORCHIDCHEM 280 1,974 0.57 0.85 0.21 2.03 (9.38)38 PENTSFWARE 1,450 2,182 0.63 1.58 0.25 4.67 (40.63)39 PFIZER 234 8,169 2.35 0.36 0.11 1.54 (8.52)40 POLARIS 514 7,802 2.24 1.26 0.17 5.11 (15.65)41 PUNJABTRAC 608 8,706 2.51 0.64 0.15 0.59 (3.63)42 RAYMOND 614 6,184 1.78 0.50 0.16 0.52 (1.23)43 ROLTA 637 4,478 1.29 1.76 0.33 2.38 (23.09)44 SIEMENS 331 10,353 2.98 0.70 0.17 2.12 0.6645 SILVERLINE 857 1,195 0.34 1.69 0.28 5.30 (38.41)46 TATAUNISYS 184 3,565 1.03 1.52 0.23 3.30 (19.34)47 THOMASCOOK 146 3,166 0.91 0.40 0.10 0.87 (4.45)48 TITAN 423 2,695 0.78 1.43 0.20 3.55 (18.43)49 VYSYABANK 226 5,784 1.66 1.02 0.15 2.21 1.9750 WOCKPHARMA 363 16,764 4.82 0.20 0.04 1.33 3.47

Total 48,691 347,544 100 1.00 � 1.05 (2.56)

Note : * Beta & R2 are calculated for the period 01-February-2002 to 31-January-2003* Beta measures the degree to which any portfolio of stocks is affected as compared to the effect on the market as a whole.* The coefficient of determination (R2) measures the strength of relationship between two variables, the return on a security versus that of the

market.* Volatility is the Std. deviation of the daily returns for the period 01-january-2003 to 31-January-2003* Last trading day of the month is January 31, 2003

ANNEXURE IXCNX NIFTY JUNIOR INDEX: JANUARY 2003


37

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0000

0.00

0030

,347

18,7

61

Jan-

0223

3,83

357

314

.95

713,

290

79,4

0011

.13

573

99.9

879

,380

99.9

74

0.68

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Feb-

0217

2,68

766

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.57

488,

230

79,8

2016

.35

660

100.

0079

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004

0.59

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30,1

6018

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Mar

-02

172,

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636

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947

5,96

277

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863

610

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0.00

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17,8

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2001

-200

217

227

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5,93

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.59

5,08

1,20

871

7,65

814

.12

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799

.78

716,

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0.61

0.01

0.00

0128

0,48

117

,880

Apr

-02

213,

011

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289

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May

-02

213,

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4,14

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210

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31,6

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0220

3,91

41,

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26.1

446

3,33

980

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17.2

71,

023

100.

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100.

006

0.62

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17,1

93

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3,68

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28.0

950

2,62

384

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16.7

31,

035

100.

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100.

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0.67

0.00

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0029

,420

16,8

88

Aug

-02

192,

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509

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645

4,43

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,115

11.6

950

910

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21,5

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Sep-

0218

2,52

544

317

.55

468,

940

52,7

1211

.24

443

100.

0052

,712

100.

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0.56

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16,2

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Oct

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202,

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460

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25,9

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Nov

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172,

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443

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1,71

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310

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26,3

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,651

Dec

-02

223,

377

757

22.4

363

8,72

288

,595

13.8

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710

0.00

88,5

9510

0.00

40.

460.

000.

0000

33,9

1415

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Jan-

0323

3,50

281

523

.28

628,

151

91,6

9414

.60

815

100.

0091

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004

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15,3

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r-D

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38

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Dec

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Jan-

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2,18

226

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489,

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Feb-

0212

0,66

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139,

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503,

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2001

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Apr

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une

2000

, Jun

e 20

01, J

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2001

and

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aily

Tur

nove

r(R

s. m

n.)


39

ANNEXURE XIISETTLEMENT STATISTICS IN F&O SEGMENT

(In Rs. million)

* Balance at the end of period

Month/Year Total Settlement

Gurantee Fund*

Jun-00 2.15 0.13 � � 2.28 �

Jul-00 14.64 0.36 � � 15.01 �

Aug-00 7.63 0.32 � � 7.95 �

Sep-00 21.12 1.34 � � 22.46 �

Oct-00 34.16 2.74 � � 36.90 �

Nov-00 46.53 0.74 � � 47.27 �

Dec-00 98.18 6.86 � � 105.04 �

Jan-01 119.37 1.10 � � 120.47 �

Feb-01 161.45 5.08 � � 166.53 �

Mar-01 335.61 0.63 � � 336.23 �

2000-01 840.84 19.29 � � 860.13 �

Apr-01 80.43 0.88 � � 81.31 �

May-01 37.76 1.13 � � 38.88 �

Jun-01 48.52 0.10 14.69 2.75 66.07 �

Jul-01 66.95 1.35 58.76 14.28 141.35 �

Aug-01 45.94 1.36 98.31 50.62 196.22 �

Sep-01 336.87 5.00 156.22 139.09 637.18 �

Oct-01 112.69 1.01 179.61 114.22 407.53 �

Nov-01 283.75 7.09 245.55 202.14 738.52 �

Dec-01 789.41 37.62 174.67 82.14 1083.84 �

Jan-02 1125.28 21.69 305.71 177.55 1630.22 �

Feb-02 1088.70 122.14 244.00 88.57 1543.42 �

Mar-02 1036.18 19.88 170.08 68.10 1294.25 6,480

2001-02 5052.49 219.25 1647.58 939.46 7858.79 6,480

Apr-02 1065.6 41.5 173 86.5 1366.60 6,550

May-02 1665.4 18.40 215.30 143.50 2042.60 6,820

Jun-02 1240.5 34.40 197.00 103.50 1575.40 7,140

Jul-02 1608.8 17.00 236.00 106.70 1968.50 7,250

Aug-02 1021 28.80 204.60 138.90 1393.30 8,190

Sep-02 1198.3 14.40 233.10 134.60 1580.40 8,220

Oct-02 1282.4 77.90 258.00 166.40 1784.70 8,419

Nov-02 1109.3 86.80 337.10 353.40 1886.60 9,840

Dec-02 1640.4 53.30 446.40 168.20 2308.30 10,742

Jan-03 2184.19 29.92 383.92 229.38 2827.41 13,154

Apr-Jan 03 14,015.89 402.42 2,684.42 1,631.08 18,733.81 13,154

MTM Final Premium ExerciseSettlement Settlement Settlement Settlement

Index/Stock Futures Index/Stock Options


40

ANNEXURE XIVTOP 10 SECURITIES ON THE WDM SEGMENT: JANUARY 2003

Security Type Security Name Issue Name No. of Trades Turnover (Rs. mn.) % of Turnover

GS CG2017 8.07% 2,753 159,222 11.40

GS CG2012 7.40% 2,191 135,667 9.71

GS CG2017 7.46% 2,195 126,925 9.08

GS CG2022 8.35% 1,667 108,680 7.78

GS CG2015 9.85% 1,316 75,200 5.38

GS CG2011 9.39% 1,185 74,500 5.33

GS CG2011A 11.50% 1,082 68,950 4.93

GS CG2013 9.81% 1,136 63,825 4.57

GS CG2017 7.49% 833 48,700 3.49

GS CG2012 11.03% 809 45,850 3.28

Total of top ten securities 15,167 907,519 64.95

Total 21,335 1,397,180 100.00

Month/Year Number of Turnover Average Daily Average TradeTrades (Rs. mn.) Turnover (Rs. mn.) Size (Rs. mn.)

Jun 94 - Mar 95 1,021 67,812 350 66.42

1995-96 2,991 118,677 408 39.68

1996-97 7,804 422,776 1,453 54.17

1997-98 16,821 1,112,633 3,850 66.15

1998-99 16,092 1,054,691 3,650 65.54

1999-00 46,987 3,042,160 10,350 64.74

2000-01 64,470 4,285,818 14,830 66.48

Apr-01 6,606 462,850 23,140 70.07

May-01 12,220 839,820 33,590 68.73

Jun-01 11,936 823,294 32,902 68.98

Jul-01 12,575 846,285 32,549 67.30

Aug-01 11,622 757,842 31,577 65.21

Sep-01 9,526 631,990 25,280 66.34

Oct-01 12,636 808,603 32,344 63.99

Nov-01 15,300 986,740 42,900 64.49

Dec-01 10,135 624,107 26,004 61.58

Jan-02 17,011 1,117,361 42,975 65.68

Feb-02 16,127 1,013,135 44,049 62.82

Mar-02 9,157 559,878 24,343 61.14

2001-02 144,851 9,471,905 32,775 65.39

Apr-02 12,164 773,337 32,222 63.58

May-02 8,662 532,461 21,298 61.47

Jun-02 8,875 544,774 21,791 61.38

Jul-02 14,996 977,250 36,195 65.17

Aug-02 15,483 1,002,256 38,548 64.73

Sep-02 10,439 682,692 28,446 65.40

Oct-02 16,587 1,061,420 42,460 63.99

Nov-02 21,052 1,322,220 55,090 62.81

Dec-02 18,807 1,173,826 48,909 62.41

Jan-03 21,335 1,397,180 51,747 65.50

Apr-Jan 03 148,400 9,467,416 42,265 63.80

ANNEXURE XIIIBUSINESS GROWTH ON THE WDM SEGMENT


41

ANNEXURE XVNSE MIBID/MIBOR RATES

MONTH/ OVERNIGHT* 14 DAY** 1 MONTH RATE*** 3 MONTH RATE** *

DATE AT 9.40 A.M. AT 11.30 A.M. AT 11.30 A.M. AT 11.30 A.M.

MIBID MIBOR MIBID MIBOR MIBID MIBOR MIBID MIBOR

15-Jun-98 5.10 5.25 � � � � � �10-Nov-98 8.02 8.09 8.30 9.02 � � � �1-Dec-98 8.00 8.06 8.44 8.97 9.20 9.83 10.28 10.9431-Mar-99 10.87 12.97 9.09 10.06 9.44 10.35 10.30 11.2031-Mar-00 14.1 16.52 9.98 10.93 9.9 10.82 9.96 10.9631-Mar-01 10.22 12.18 9.03 9.89 9.08 9.86 9.26 10.2530-Apr-01 7.25 7.39 7.55 8.33 8.15 8.83 8.83 9.5431-May-01 6.79 6.95 7.40 8.04 7.89 8.57 8.41 9.0829-Jun-01 7.20 7.34 7.25 7.85 7.69 8.41 8.16 8.8731-Jul-01 6.91 7.04 7.29 7.88 7.58 8.17 7.99 8.6631-Aug-01 6.92 7.03 7.01 7.40 7.34 7.82 7.82 8.3228-Sep-01 7.77 8.21 7.52 8.14 8.07 8.70 8.33 8.9831-Oct-01 8.47 8.77 7.15 7.72 7.39 8.03 7.61 8.3729-Nov-01 6.42 6.59 6.74 7.23 7.26 7.80 7.77 8.3231-Dec-01 7.80 8.11 7.42 8.04 7.63 8.26 7.88 8.5731-Jan-02 6.51 6.64 6.89 7.40 7.15 7.73 7.73 8.4128-Feb-02 6.94 7.16 6.84 7.33 7.23 7.78 7.79 8.3730-Mar-02 7.44 11.09 7.41 8.06 7.39 8.05 7.63 8.2929-Apr-02 6.41 6.55 6.59 7.06 7.08 7.59 7.55 8.1231-May-02 6.01 6.16 6.64 7.29 7.17 7.79 7.48 8.2428-Jun-02 4.99 5.35 6.04 6.56 6.35 6.98 6.80 7.5031-Jul-02 5.65 5.75 5.80 6.16 6.01 6.42 6.35 6.8431-Aug-02 5.67 5.75 5.73 6.02 5.98 6.34 6.37 6.8128-Sep-02 5.70 5.77 5.73 6.07 5.91 6.32 6.28 6.8131-Oct-02 5.45 5.53 5.50 5.71 5.65 5.87 5.85 6.2330-Nov-02 5.21 5.39 5.45 5.65 5.59 5.82 5.77 6.1031-Dec-02 5.59 5.71 5.50 5.69 5.60 5.90 5.80 6.211-Jan-03 5.57 5.68 5.49 5.70 5.57 5.89 5.75 6.142-Jan-03 5.47 5.56 5.50 5.71 5.57 5.88 5.75 6.183-Jan-03 5.42 5.53 5.47 5.64 5.57 5.85 5.75 6.134-Jan-03 5.32 5.47 5.44 5.66 5.55 5.85 5.70 6.126-Jan-03 5.42 5.52 5.43 5.67 5.52 5.82 5.71 6.097-Jan-03 5.44 5.53 5.41 5.65 5.52 5.80 5.71 6.078-Jan-03 5.42 5.52 5.44 5.67 5.55 5.82 5.73 6.089-Jan-03 5.44 5.53 5.44 5.64 5.58 5.81 5.74 6.0510-Jan-03 5.45 5.56 5.43 5.63 5.56 5.78 5.75 6.0411-Jan-03 5.42 5.53 5.40 5.65 5.52 5.81 5.70 6.0513-Jan-03 5.45 5.54 5.38 5.64 5.50 5.80 5.66 6.0214-Jan-03 5.45 5.54 5.39 5.64 5.50 5.78 5.67 5.9715-Jan-03 5.44 5.53 5.43 5.65 5.49 5.77 5.68 6.0016-Jan-03 5.49 5.58 5.43 5.68 5.52 5.80 5.65 5.9917-Jan-03 5.85 6.05 5.43 5.74 5.50 5.83 5.66 6.0018-Jan-03 5.90 6.07 5.42 5.75 5.55 5.85 5.68 6.0620-Jan-03 5.98 6.17 5.53 5.77 5.57 5.85 5.72 6.0921-Jan-03 5.94 6.16 5.53 5.85 5.62 5.98 5.76 6.1822-Jan-03 5.93 6.13 5.57 5.82 5.65 5.94 5.83 6.1923-Jan-03 5.86 6.04 5.51 5.84 5.60 5.96 5.72 6.1424-Jan-03 5.76 5.94 5.54 5.89 5.63 5.98 5.75 6.1625-Jan-03 5.48 5.64 5.51 5.84 5.59 5.89 5.70 6.1027-Jan-03 5.50 5.60 5.50 5.80 5.58 5.88 5.67 6.1128-Jan-03 5.49 5.59 5.49 5.78 5.59 5.86 5.69 6.0229-Jan-03 5.56 5.68 5.51 5.80 5.58 5.87 5.72 6.1330-Jan-03 5.61 5.74 5.54 5.82 5.60 5.91 5.71 6.1831-Jan-03 6.02 6.20 5.60 5.97 5.67 6.04 5.82 6.30

Note: * Overnight : Disseminated since June 15, 1998** 14 Day : Disseminated since November 10, 1998.*** 1 month : Disseminated since December 1, 1998.*** 3 month : Disseminated since December 1, 1998


42

(No. of cases)

(No. of cases)

ANNEXURE XVIDISPOSAL OF INVESTOR GRIEVANCES: JANUARY 2003

I) Complaints against Trading Members/Companies

Sl. No. Details of complaints Trading Members Companies

Apr-02 Dec-02 Jan-03 Apr-02 Dec-02 Jan-03

1 Received during the month 33 22 21 42 39 522 Carried forward from earlier month 187 147 140 299 268 2513 Resolved 55 29 25 52 56 474 Pending at the end of the month 165 140 136 289 251 256

II. List of Trading Members/Companies with more than 5 complaints pending for more than 2 months

Sl. No. Name of the Trading Member/Companies No. of Complaints on January 31, 2003 Remarks

Companies1 Vatsa Corporation Ltd 65 Suspended

2 Mafatlal Finance Ltd. 17 Suspended

3 Enkay Texofood Industries Ltd 9 Suspended

4 Essar Oil Ltd. 6 �

5 Pal Peugeot Ltd 6 Suspended

6 Vikas WSP Ltd. 6 Withdrawn from trading

ANNEXURE XVIISTATUS REPORT OF ARBITRATION MATTERS : JANUARY 2003

YEAR-WISE STATISTICS OF CASES RESOLVED/PENDING

Year Total No. of Cases Withdrawn Awards Pending

1998 164 2 161 11999 : CM 153 5 144 41999 : WDM 2 0 1 12000 : CM 149 5 142 22000 : WDM 1 0 1 02001 : CM 342 19 313 102001 : WDM 0 0 0 02001 : F&O 1 0 1 02002 : CM 275 7 197 712002 : WDM 0 0 0 02002 : F&O 5 0 2 32003:CM 12 0 0 122003:WDM 0 0 0 02003:F&O 0 0 0 0Total 1,104 38 962 104

STAGE-WISE BREAK-UP OF CASES

Regions On hand Received Awards Awards Scheduled Awaiting Pendingat the during passed / awaited for response for

beginning the cases hearing from more thanof the month withdrawn respondents 4 monthsmonth during the

month

Mumbai : CM 64 6 14 22 23 11 19 : WDM 1 0 0 0 0 1 1 : F&O 3 0 0 2 1 0 0

Delhi 33 2 7 8 18 2 7Kolkotta 2 0 0 0 2 0 1Chennai : CM 16 4 6 4 7 3 5Total 119 12 27 36 51 17 33


43

ANNEXURE XVIIILIST OF CITIES AND VSATs AT THE END OF JANUARY 2003

*Indicates cities which have a Regional Stock Exchange.** Cities activated in January 2003.

STATE / UTs LIST OF TOWNS AND CITIES NO. OF NO. OFCITIES VSATs

Andhra Pradesh Anantpur, Amalapuram, Adoni, Bhimavaram, Bhadrachalam, Chittor, Chirala, Cuddapah, Eluru,Gudiwada, Gujuwaka, Guntur, Hindupur, *Hyderabad,Jagtial, Kakinada, Kamareddy, Karimnagar,Khammam, othagudem, Kurnool, Kukatpally, Mancherial, Mandapeth, Markapur, Madanpalle,Mirialguda, Nalgonda, Nandyal, Narsapur, Narsaraopeta, Nirmal, Nellore, Nizamabad, Ongole, Palakol,Parvatipuram, Piduguralla, Proddatur, Rajamundry, Ramagandum, Secundarabad, Srikakulam, Suryapet,Tadepalligudem**, Tanuku, Tenali, Tirupathi, Tuni, Vijayawada, Vizag, Vizianagaram, Warangal.

Assam *Guwahati, Jorhat, Silchar, Tinsukia.

Bihar Begusarai, Bhagalpur, Gaya, Jamshedpur, Muzzaffarpur,*Patna.

Chhattisgarh Bilaspur, Korba, Raipur, Raigarh.

Delhi *Delhi.

Goa Margao,Mapusa, Panaji.

Gujarat *Ahmedabad, Amreli, Anand, *Baroda, Bharuch, Bhavnagar, Bhuj, Botad, Dahod, Deesa, Dhoraji,Dhrangadhra, Ghandhidham, Gandhinagar, Gondal, Jamnagar, Junagadh, Kadi, Khambat, Mahuva,Mehsana, Modasa, Morbi, Nadiad, Navsari, Okha, Patan, Petlad, Porabnder, *Rajkot, Savarkundla,Surat, Surendranagar, Unjha, Valsad, Veraval, Vapi, Una.

Haryana Ambala, Bahadurgarh, Bhiwani, Faridabad, Fatehabad, Ganaur, Gangapur, Gurgaon, Gohana, Hissar,Jagadhri, Jind, Kaithal, Karnal, Kurukshetra, Panipat, Panchkula, Pehowa, Rewari, Rohtak, Sirsa, Sonepat,Tohana, Yamuna Nagar.

Himachal Pradesh Shimla.

Jammu & Kashmir Jammu, Srinagar.

Jharkhand Bokaro, Dhanbad,Deogarh, Dumka, Giridih, Ranchi.

Karnataka *Bangalore, Bellary, Belgaum, Bijapur, Chickmagalur, Challakere, Devanagere, Hubli, Hassan, Kumta,*Mangalore, Manipal, Mulky, Mysore, Sagar, Sirisi, Shimoga, Udupi.

Kerala Alappuzha (Alleppey), Angamaly, Attinagal, Calicut, Chalakudy,, Ernakulam, Guruvaryur, Irinjalakuda,Kalpetta, Kayamkulam, Kanjirapally, Kannur, Kasargod, Kodungallore,Kochi, Kozhencherry, Kollam,Kottayam, , Mavelikara, Muddakayam, Muvattupuzha, Ottapalam, Pala, Palakkad, Pathanamthitta,Payannu, Ranni, Sultan Bathery, Thalassery, Thiruvalla, Thodupuzha, Trichur, Thiruvananthapuram,Taliparamba, Vatanapalii.

Madhya Pradesh Bhilai, Bhopal, Chhindwada, Gwalior, *Indore, Jabalpur, Jhabua, Katni, Nagda, Neemuch, Piparia,Ratlam, Rewa, Satna, Ujjain.

Maharashtra Aurangabad, Ahmednagar, Akola, Amravati, Beed, Bhusawal,Chandrapur, Dhule, Jalg aon, Kolhapur,Kopargaon, Malegaon, *Mumbai, Nagpur, Nashik, *Pune, Satara, Sangli, Solapur, Ichalkaranji**

Manipur Imphal

Orissa Berhampur,*Bhubaneshwar, Cuttack, Rayagada, Rourkela, Sambalpur .

Punjab Abohar, Amritsar, Betala, Bhatinda, Barnala, Budhlada, Chandigarh, Ferozepur, Gurdaspur,Hoshiyarpur, Jagraon, Jalandhar, Kapurthala, Kotakpura, Khanna, *Ludhiana, Gobindgarh, Mansa,Mondi Moga, Mohali, Pathankot, Patiala, Rajpura, Sangrur.

Rajasthan Ajmer, Alwar, Beawar, Bharatpur, Bhilwara, Bikaner, Chittorgarh, Falna, Gulabpura, Jodhpur, Jhunjhunu,Kota, Hindaun, Hanumangarh, Makrana, Newai, Pratapgarh, Sujangarh, Sadar Sahar, Salasar, Udaipur,Anoopgarh**, Jaipur*, Shekhawali**

Tamil Nadu *Chennai, *Coimbatore, Dharapuram, Erode, Gudiyatham, Gobichettipalayam, Hosur, Karaikal,Karaikudi, Karur, Kumbakonam, Madurai, Nagercoil, Namakkal, Neyveli, Ranipeth, Salem, Sivakasi,Thanjavur, Tirunelveli, Trichy, Tuticorin,Theni, Virudhunagar, Vellore, Rajapalayam

Union Territory Pondicherry

Uttar Pradesh Agra, Aligarh, Allahabad, Banda, Bareilly, Bhahraich, Bulandshahar, Dhampur, Faizabad, Firozabad,Gorakhphur, Ghaziabad, Gujrala, Haldwani, Hapur, Hatras, Jhansi, Kashipur, Kotdwara, *Kanpur,Lucknow, Mathura, Meerut, Moradabad, Mussoorie, Muzzafarnagar, Najibabad, Modinagar, Rampur,Renukoot, Rishikesh, Roorkee, Sahibabad, Saharanpur, Shahjahanpur, Varanasi, Kesjanj

Uttaranchal Dehradun, Haridwar, Nainital, Rudrapur.

West Bengal Asansol, Burdwan, Durgapur, Jalpaiguri, Kolkata*, Purulia, Raniganj, Siliguri.

Total

52 217

4 9

6 27

4 12

1 422

3 9

38 212

24 87

1 1

2 11

7 21

18 94

32 93

15 80

20 692

1 1

5 13

23 105

24 118

25 160

1 5

36 200

4 18

8 193

354 2,800


44

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