AN EMIRICAL STUDY ON THE IMPACT OF DERIVATIVES ON THE SPOT

MARKET VOLATILITY: A CASE OF NIFTY INDEX

Sibani Prasad Sarangi1 & Uma Shankar Patnaik 2

ABSTRACT

Derivatives trading in India commenced in June 2000 with the introduction of stock index

futures by BSE and NSE. Futures and options are important instruments for risk exposure

through hedging, portfolio diversification and price discovery. This paper provides a

theoretical background to and empirical evidence of the impact of futures and options on

the spot market volatility. This study is based on both closing and opening price returns

as well. The sample data consist of daily opening and closing price returns of S & P CNX

Nifty, Nifty Junior and S & P 500 index from January 1, 1997 to March 31, 2005. Earlier

studies have used different time-series techniques like GARCH, IGARCH, ECM, OLS,

etc. to access the impact of derivatives on the spot market volatility. The present study

uses family of GARCH techniques to capture the time-varying nature of volatility and

volatility clustering phenomenon in the data. The empirical evidence suggests that there

are no significant changes in the volatility of the spot market of the S & P CNX Nifty

Index, but the structure of the volatility has been changed to some extent. However, the

study also found that the new information is assimilated into prices more rapidly than

before, and there is a decline in the persistence of volatility since the inception of futures

trading.

Key Words: S & P CNX Nifty, Index Futures, GARCH

1 Doctoral Fellowr, Department of Economics, University of Hyderabad, Hyderabad e-mail: sibani_sarangi@yahoo.co.in2 Professor, Department of Economics, University of Hyderabad, Hyderabad

1

An Empirical study on the Impact of Derivatives on the Spot Market Volatility:

A case Nifty Index

Sibani Prasad sarangi and Uma Shankar Patnaik

1. INTRODUCTION:

The Indian capital market, which was lying as dormant segment of the financial system,

has undergone a drastic transformation since the mid-eighties involving multidimensional

growth. Since 1970s, a series of significant changes have been taken place in financial

markets throughout the world. The financial market liberlisation, in the early 1990s has

brought major changes in the Indian capital market such as introduction of derivatives,

relaxation rules and regulations for FDI and FII etc. Derivatives were introduced in a

phased manner3 after the recommendation of the L. C. Gupta Committee Report in 1997.

Initially derivatives in India were introduced as a hedging device on June 2000 through

the introduction of stock index futures. This was followed by the introduction of the

index option (June 2000), stock options (July 2001) and stock futures (Nov 2001). It was

introduced due to high volatility of the Indian stock market. The volume in derivatives

markets especially on the futures and options on National Stock Exchange, witnessed a

tremendous increase and recently the turnover is much higher than the turnover in the

cash market.

Increased volatility in asset prices in financial markets, increased integration of national

financial markets with the international markets, improvements in the communication

facilities necessitates the introduction of derivatives in India. Derivatives include futures,

forwards, options and swaps, and these can be combined with each other or with

traditional securities and loan to create hybrid instruments. Futures and options have

become essential instruments of price discovery, portfolio diversification and risk

hedging in recent times on the Indian stock markets. As a part of financial market

3 See Appendix-1

2

reforms, derivatives were introduced in a phased manner4 after the recommendation of

the L. C. Gupta Committee Report in 1997.

Against this background, this paper is laid out into five sections. Section I introduces

derivatives segment of India; section-II presents the theoretical framework of the study;

section-III demonstrates the data employed and methodology of the present study;

section-IV presents the empirical evidence; and section-V provides conclusion and scope

for further research in this area.

2. OVERVIEW OF PAST STUDIES:

The impact of futures and options on the underlying index volatility seems practically an

empirical question. A number of studies have been carried out in this regard across the

countries. Generally, two types of arguments prevail in the existing literature. One school

of thought argued that derivatives trading increases stock market volatility due to high

degree of leverage and hence, destabilizes the market. Further, futures market is likely to

attract uniformed traders due to low transactions costs involved to take positions in the

futures market. The lower level of information of derivatives trades with respect to cash

market traders is likely to increase the asset volatility. On the other hand, another school

of thought claims that futures market plays an important role of price discovery and has

beneficial effect on the underlying cash market. Kumar et al (1995) argued that

derivatives trading helps in price discovery, improves the market debt, enhances market

efficiency and reduces asymmetry information of spot market. This gives rise to the

controversy among the researchers, academicians and investors on the effect of

derivatives on the underlying market volatility and some selected reviews related to

equity futures and options are discussed in the following section.

With respect to futures trading, most of the studies are related to index futures due to lack

of trading in single-stock futures. Studies by Edwards (1988) on Value-line Index, Cahn

and Karlogi (1991) on Nikkei 225 Index, Leela and Ohk (1992) on Australian All

Ordinaries Index, Darrat and Raman (1995) and Kamara et al. (1995) on S & P 500 Index

4 See Appendix-1

3

found no significant changes in the volatility of the spot market. On the other side,

studies by Lockwood and Linn (1990) on DJIA Index, Brorson (1991), S & P 500 found

increase in the volatility of the spot market. A study by Gulen and Mayhew (2000) is

based on twenty-five countries. It found out an increase in volatility for S & P 500 and

Nikkei 225 indices, and all other countries showed no significant change in volatility.

Similarly, Ibrahim (1999) and Oliva and Armada (2001) did not find any significant

change on the spot market volatility of the Malaysia and Portuguese stock market

respectively. Butterworth (2000) found no significant change in the volatility of the

FTSE-250 index after onset of futures trading. Board, Sandmann and Surcliffe (2001)

show that, contrary to regulatory concern and the results of the other papers,

contemporaneous information less futures market trading has no significant effect on spot

market volatility. Some of the studies show a decrease in the volatility of the underlying

market. These studies include Bessembinder and Seguin (1992) on S & P 500 index,

Homes and Priestly (1998) on DAX 100 and Swiss MI index, Pierluigin Bologana and

Laura Cavallo (2002) on Italian Stock Market. In the case of options, most of the studies

are related to the individual stocks. It shows a decline in the volatility of the spot market

(Conrad 1989), Elfakhani and Choudhury (1995). Antoniou and Holmes (1995) found

that the introduction of stock index futures caused an increase in spot market volatility in

the short run while there was no significant change in volatility in the long run.

Number of studies has been carried out by different academicians to detect the spot

market volatility in case of India. One of the earlier studies by Thenmozhi (2002) showed

a decline in volatility of the spot market by examining S & P CNX Nifty index. Similar

results obtained by O.P. Gupta (2002), Raju and Karnde (2003) while Shengagaram

(2003) did not find any significant change in the spot market volatility. The Volatility of

the Nifty stock futures has been declined except for some stocks after the introduction of

futures. Further, the cash market volatility has also come down after the introduction of

the derivative market, but there are other reasons like microstructure changes and robust

risk management practices, which are responsible for the reduction in the volatility. (G.C.

Nath, 2003).

4

The above literature gives a mixed result about the effect of futures on the volatility of

the underlying market across the countries. The results depend on the indices and

methodology used in the study, because studies examine the same indices arrived at

different conclusions. Most of the studies are related to the developed countries like the

USA, the UK or Japan. But, a very few studies have been conducted in developing

countries like India. In this context, it gives rise to further research in this regard.

The present paper contributes in the following manner. Firstly, this paper seeks to

examine the behaviour of spot market by taking both closing and opening price returns

while most of the earlier studies in the Indian context based on the closing price returns.

Secondly, the present study employs GARCH technique to measure the volatility, and it

also explains the nature of the volatility before and after the introduction of futures and

options.

3. DATA SOURCES

The data for the study consist of daily closing and opening price returns of the S&P CNX

Nifty Index and Nifty Junior Index, which are collected from NSE. The data period is

taken from January 1, 1997 to March 31, 2005. S & P CNX Nifty is well-diversified 50

stocks accounting for 23 sectors of the economy. The data are colleted for closing and

opening prices. The total number of observations is 2065, out of which 857 and 1103

were prior to futures and options trading and remaining 1207 and 962 observations relate

to post-futures and options trading respectively. S&P CNX Nifty is owned and managed

by India Index Services and Products Ltd. (IISL), which is a joint venture of NSE and

CRISIL. This study is based on National Stock Exchange Data because 95 percent of the

trading in derivatives is done at NSE.

4. METHODOLOGY

The present scholarship is based on the stock index price returns. The daily returns based

on closing and opening prices were computed using the following equation.

Rt = Log (Pt / Pt-1)---------------------------------------------------(1)

5

The variance of the returns series is calculated to know the inter-day volatility by using

the following equation:

σ2 = ∑ ( Rt – R )2 / T-1 --------------------------------------------(2)

Several scholars have carried out studies to show the volatility of the spot market before

and after the introduction of the futures trading. Hodgson (1991), Herbest (1992),

Thenmozhi (2002) etc. have measured the volatility by using standard deviation. Many

researchers like Kalok Chand (1991), Antonou and Holmes (1995), Gregory (1996),

Darren Butterworth (2000) use the GARCH model to overcome the problem of

hetroskedasticidy in the observed returns. The present study makes use of both the

methods to focus the volatility of the underlying market.

In order to determine whether the onset of futures trading has any effect on the

underlying spot market volatility, it is necessary to separate the volatility arising from

market wide factors other than futures trading. Previous studies have to filter out the

factors which lead to market wide volatility by regressing the spot market returns against

a proxy variable for which there was no related futures contract, { Antonium and Holmes

(1995), Kamara, et.al (1992), and Greoge, et. al.}. For Indian stock market, Nifty Junior

Index comprises stocks for which no futures contracts are traded. Thus, it serves as a

control variable for us to isolate market wide factors and thereby concentrates on the

residuals volatility in the Nifty as a direct result of introduction of index derivatives

contracts. To remove the effects of world wide price movements on the volatility of the

Nifty index returns, the lagged S & P 500 index returns has been take into consideration.

The futures trading is introduced as a dummy variable which takes the value 0 for pre-

futures and 1 for post-futures.

The co-efficient of the dummy variable determines the changes in the volatility of the

spot market. If it turns out to be negative, then there is a decline in the spot market

volatility with the inception of the futures trading and vice-versa. A zero coefficient

indicates no change in the volatility.

6

The assumption of classical linear regression model is that variance of errors are constant.

It is unlikely that in the context of stock returns data the variance of the errors will be

constant over time. Apart from these the OLS regression neglected the possible

autocorrelation in return and inherent time-varying nature of volatility, and it does not

allow one to explicitly capture the connection between information and volatility. Hence,

it is better to consider a model that allows the above limitations. Thus, the prime

motivations behind the development of conditional volatility models emanated from the

fact that the then existing linear-time series models were inappropriate. Hence, this study

makes use of non-linear models like ARCH.

Autoregressive Conditional Hetroscedastic (ARCH) model was first introduced by Engel

(1982), and it allows the conditional variance to change over the time. In ARCH model,

the variance is modeled as a linear combination of squared past errors of specific lag and

the autocorrelation in volatility modeled by allowing conditional variance of the error

terms, to depend on the immediately previous values of the squared errors.

An ARCH (P) can be specified as

Ψ ψ εt / Ψt ~ ( 0, ht )

ht = α0 + α1 ε2 t -1 + ….+ αp ε2 t –p ----------------(3)

The ARCH model was generalized by Bollesla (1986), and it is called GARCH

(Generalised Autoregressive Conditional Hetroscadasticity). GARCH models explain

variance by two distributed lags: firstly, on past squared residuals to capture high

frequency effects or news about volatility from previous period measured as lag of the

squared residuals from mean equation, and secondly, on lagged values of variance itself

to capture long-term influences. A GARCH (p, q) model is given by the following

equation.

Yt = α0 + α1Xi + εt -------------------------- (4)

εt / Ψt ~ ( 0, ht )

h2t = α0 + α1 ε2

t-1 + -----------(5)

in this case, P is the degree of ARCH, q is the degree of GARCH and Vt is the error term

with white noise process. The size of the parameters α1 and β1 determine the short-term

7

dynamics of the resulting volatility time-series. Large co-efficient of β1 shows that shocks

to conditional variance take a long time to cancel out, so volatility is persistence.

Secondly, the GARCH (1, 1) is estimated for measuring the volatility. The mean and

variance equation are as follows:

---------------------------------------- (6)

----------- (7)

Where is daily return on the S&P CNX Nifty and is the lagged returns and in the

variance equation a dummy variable is introduced for futures and options. To eliminate

the effect of market wide factors in India and the world wide factors Nifty Junior Index

and S & P 500 Index is introduced in the mean equation. The equation is as follows:

---------------- (8)

5. EMPIRICAL DISCUSSIONS

The economic literature in the recent past has experienced an explosion of unit roots for

stationary of time series data as the choice of techniques and procedure for further

analysis and modeling of series depends on their order of integration. Hence, without

taking into account the presence of unit root in the variables, the analysis may produce

spurious results. Therefore, Augmented Dicky- Fuller and Philips-Perron unit root tests

are employed to test the integration of each variable. ADF unit root test is sensitive

towards the lag length included in the regression equation. Hence, the lag length is

chosen on Akaike Information Criterion (AIC). The result of the unit root test is shown in

Table 1. All the return series are stationary at its level and they are significant at 1 percent

level. The same can be seen from the graph which is given at the end of the paper.

Table: 1 Unit Root Results

ADF Test Statistics Philips-Perron Test

Variable Without

intercept

With

intercept

With

intercept

and trend

Without

intercept

With

intercept

With

intercept

and trend

S&P CNX -19.7678 -19.7911 -19.7955 -45.28114 -45.28834 -45.28401

8

Nifty

Nifty

Junior

-19.3742 -19.42265 -19.42368 -41.70323 -41.71917 -41.71391

S & P 500 -21.9375 -21.95832 -23.03165 -48.3199 -49.32953 -49.38179

Table: 2 Descriptive Statistics

Pre-Futures Post-Futures Pre-Options Post-Options Overall

Mean 0.00049

(0.001083)

0.00029

0.000388

0.00016

(0.000346)

0.018559

(0.001011)

0.000374

(0.000667)

S.D. 0.01894

(0.022374)

0.01453

0.017443

0.01855

(0.022490)

0.013800

(0.015768)

0.016509

(0.019642)

Probability 0.00000 0.00000 0.00000 0.000 0.000000

NOB 858 1207 1103 962 2065

Note: Figures in the parenthesis indicate values for Nifty Junior.

Table-2 provides the descriptive statistics for Nifty and Junior Nifty index returns. The

standard deviation was 0.1894 and 0.0185 for pre-futures and pre-options respectively.

But it has come down to 0.01453 and 0.013 for the same.

The objective of this paper is to see the effect of introduction of futures and options on

the volatility of underlying markets. An analysis is done to explain the same by

regressing the spot market volatility on Nifty Junior returns, S & P 500 Index and Nifty

futures and options by using GARCH technique. In order to overcome the problem of

classical linear regression models and comparison of results, the study uses non-linear

model, GARCH. Table-3 reports results obtained by using the equation 6 and 7 of

GARCH technique. It shows that Nifty Junior return is regressed against its lag value

using GARCH (1, 1) technique with futures trading as a dummy variable. The lag length

chosen is 5. The co-efficient of the dummy variable is -2.84E-05 for the closing price

returns, which is significant at 1 percent level. It indicates that introduction of futures

trading reduces the spot market volatility, but it has a very negligible impact. In the case

of opening price returns, the coefficient of the dummy variable is also negative i.e. -

9

2.69E-05. For options the dummies are -0.0000249 and -0.000241 for closing and

opening prices respectively, and both are significant at 1 percent level (Table-4).

Table 3: Impact of Nifty Futures on the Spot Market (Garch 1,1)

SL. Closing price Return Opening Price Returns

1 Variable Co-efficients Significance Co-efficients Significance

2 Constant 4.67E-05 6.595156 4.66E-05 6.924552

3 S&P CNX Nifty

Index

0.046675 1.929942 0.036811 1.636265

4 ARCH 1 0.146481 11.01967 0.184532 10.74136

5 GARCH 1 0.749047 30.61710 0.715988 29.30478

6 F DUMMY -2.84E-05 -5.927439 -2.69E-05 -5.650780

Table 4: Impact of Nifty Options on the Spot Market (Garch 1, 1)

SL. Closing price Returns Opening Price Returns

1 Variable Co-efficients Significance Co-efficients Significance

2 Constant 4.13E-05 6.42E-06 4.22E-05 6.869729

3 S&P CNX Nifty

Index

0.050973 2.112423 0.038127 1.708572

4 ARCH 1 0.134236 11.39926 0.175273 10.64046

5 GARCH 1 0.759885 32.49142 0.722494 29.60119

6 O DUMMY -2.49E-05 -5.765881 -2.41E-05 -5.654850

Table 5: Impact of Nifty Futures & Options on the Spot Market (Garch 1, 1)

SL. Closing price Returns Opening Price Returns

1 Variable Co-

efficients

Significance Co-

efficients

Significance

2 Constant 5.00E-05 6.565649 4.89E-05 6.957923

3 S&P CNX 0.049704 2.054770 0.037633 1.683201

10

Nifty Index

4 ARCH 1 0.141931 10.43919 0.179239 10.36807

5 GARCH 1 0.741486 27.83160 0.712030 27.83012

6 F DUMMY -1.91E-05 -3.19716 -1.71E-05 -2.619723

7 O DUMMY -1.28E-05 -2.51474 -1.26E-05 -2.233087

Table-5 shows the results of the GARCH (1, 1) process of the S & P CNX Nifty after the

introduction of the nifty index futures and index options simultaneously. For futures and

options the co-efficient of the dummy variable is negative i.e. -1.91E-05 and -1.28E-05

respectively. Though the co-efficients of the dummies are significant at their respective

levels, these do not have any significant impact on the volatility of the spot market, which

is indicated by the low values of the options and futures dummies.

To remove the market wide factors Junior Nifty was introduced as a proxy variable in the

mean equation of the GARCH technique. The market wide factors are information news

releases relating to economic conditions like inflation rates, growth forecast, exchange

rate, monetary policy, etc. Further, there might be any change in the volatility due to the

world-wide market factors like change in the volatility of the US stock market. That is

why S & P 500 index has taken to capture the World wide fluctuations. The co-efficients

of the dummy variable is -2.94E-05 for closing price returns and -1.79E-06 for the

opening price returns for futures. Similarly, in the case of options, the co-efficients of the

dummies are -2.56E-05 and -2.00E-06 for closing price returns and opening price returns

respectively. After controlling the market-wide and world-wide factors, the coefficients

of dummies still remain negative, and are closer to zero indicating that it has a negligible

effect on the volatility of the spot market.

Table 6: Impact of Nifty Futures after Controlling Market Wide Factors

SL. Closing price Returns Opening Price Returns

1 Variable Co-efficients Significance Co-efficients Significance

2 Constant 4.79E-05 6.586080 6.35E-06 5.011332

3 S&P CNX Nifty 0.040086 1.649163 0.029894 2.410215

11

Index

4 Junior Nifty 0.067003 3.778669 0.685480 92.93078

5 S & P 500 0.001368 0.061603 0.001214 0.078232

6 ARCH 1 0.150325 10.58469 0.093469 7.795230

7 GARCH 1 0.742966 29.06139 0.855002 46.62763

8 F DUMMY -2.94E-05 -5.947178 -1.79E-06 -2.587291

Table 7: Impact of Nifty Options after Controlling Market Wide Factors

SL. Closing price Returns Opening Price Returns

1 Variable Co-efficients Significance Co-efficients Significance

2 Constant 0.000797 2.615001 6.77E-06 5.159395

3 S&P CNX Nifty

Index

0.044533 1.831996 0.029766 2.410020

4 Junior Nifty 0.066731 3.644826 0.687650 93.17332

5 S & P 500 -0.000873 -0.038314 0.013897 0.828292

6 ARCH 1 0.137723 10.94410 0.098601 7.907747

7 GARCH 1 0.753913 30.63286 0.845147 44.16185

8 O DUMMY -2.56E-05 -5.748425 -2.00E-06 -2.787367

Table-8 presents the results for Nifty index volatility after introduction of futures and

options simultaneously, thereby controlling market wide factors and world wide factors.

The futures dummies are -0.0000199 and -0.0000011 for closing and opening price

returns respectively. Similarly, the options dummies are -0.0000131 and -0.0000012

respectively for closing and opening prices. The sign of the co-efficients of the dummies

shows that volatility has declined in the post-derivatives segment, but it has a very little

impact on the underlying market.

Table 8: Volatility after controlling Futures and Options simultaneously

Closing price Returns Opening Price Returns

1 Variable Co-efficients Significance Co-efficients Significance

2 Constant 0.000808 2.672726 -0.000103 -0.497148

12

3 S&P CNX Nifty

Index

0.043207 1.776883 0.030339 2.457887

4 Junior Nifty 0.067406 3.721878 0.686702 91.09954

5 S & P 500 0.001451 0.063619 -0.024593 -1.496204

6 ARCH 1 0.145774 9.990090 0.097386 7.742749

7 GARCH 1 0.734977 26.32821 0.845757 42.96302

8 F DUMMY -1.99E-05 -3.273382 -1.10E-06 -0.881796

9 O DUMMY -1.31E-05 -2.543172 -1.25E-06 -1.033443

To address the structure of the volatility after the introduction of futures, the whole

period is divided into pre-futures and post-futures. Similarly, for options the whole period

has been divided into options pre-options and post-options, and GARCH (1, 1) technique

has estimated separately for each sub-sample. This will allow us to compare the nature of

the volatility in each period. GARCH equation has two effects: one is ARCH effect and

second one is GARCH effect. ARCH is the coefficient of the square of the error term,

and shows the effect of recent news to the market whereas GARCH is the coefficient of

the lagged variance term and captures the effect of the old news in the market.

Table 9:

Volatility before and after the introduction of the futures (Closing Price returns)

Part-A

Parameters Pre-Futures Post-futures Pre-Options Post-Options

Constant 2.25E-05

(3.163719)

3.18E-05

(2.656031)

2.74E-05

(3.455743)

3.71E-05

(2.287125)

ARCH (1) 0.065162

(4.337653)

0.12237

(3.130740)

0.053302

(4.405131)

0.092624

(2.286776)

GARCH (1) 0.894716

(51.97215)

0.780181

(32.18146)

0.90285

(54.43926)

0.807116

(22.84734)

Volatility before and after introduction of the futures (Opening Price Returns)

Part-B

13

Part-A of the Table-9 reports the results of the structure of volatility in pre and post-

futures regime. The estimates show that co-efficient ARCH was 0.06516 and 0.53302

before the introduction of the futures and options trading respectively and 0.12237 and

0.092624 after the introduction of the futures and options. It shows an increase in the

coefficient of the ARCH both in case of futures and options, which indicate that there is

an increase in the impact of the recent news on spot market volatility in the post-

derivatives phase. The co-efficient of the GARCH is 0.8947 in pre-futures and 0.90285 in

pre-options and declined to 0.78081 in post-futures and 0.807116 in post-options. It

indicates that the effect of the old news has declined in the post-futures period. Similarly,

Part-B of the Table-9presents the results of opening price returns. The GARCH co-

efficient shows a decline for both futures and options as in the case of closing price

returns. But, the ARCH co-efficient in the pre-futures was 0.1035 and it declined to

0.0930 in the post-futures. The overall results show that in the post futures and options

period, there has been an increase in the recent news to the spot market and at the same

time, a decline in the GARCH co-efficient shows that the impact of old news has

declined in the post-derivatives scenario. It may lead to the increase in the efficiency of

the market.

6. CONCLUSIONS

Parameters Pre-Futures Post-futures Pre-Options Post-Options

Constant 4.68E-05

(3.873138)

8.07E-05

(3.120490)

4.77E-05

(4.141328)

1.02E-04

(3.238615)

ARCH (1) 0.103590

(6.033575)

0.093001

(4.015565)

0.085160

(6.030230)

0.131523

(4.386409)

GARCH (1) 0.863265

(40.69594)

0.805642

(17.09872)

0.876160

(44.34288)

0.739383

(13.83962)

14

This paper examines the impact of futures and options trading in S & P CNX Nifty

futures contract on the underlying market using GARCH techniques. Further, it is based

on both closing as well as opening price returns. The results reported for the S & P CNX

Nifty index indicate that the existence of futures and options market made little impact on

the underlying level of volatility as measured by the GARCH technique for both closing

as well as opening price returns. The results of both closing and opening price returns for

futures explores that the surge of recent information to the stock market has increased

while the impact of the old news has declined in the post-derivatives scenario. A similar

result has been obtained for options as well. It indicates that the recent information

absorbs more rapidly than the old news in the stock market.

Appendix-1

15

Chronology of Events leading to Derivatives Trading in India

1956: Enactment of the securities contracts (Regulation) Act which prohibited all options in securities.

1969: Issue of Notifification which prohibited forward trading in securities.

1995: Promulgation of the Securities Laws (Amendment) Ordinance which withdrew prohibitions on options.

1996: Setting Up of L.C. Gupta Committee to develop regulatory framework for derivatives trading in India.

1998: Constitutions of J. R. Varma Group to develop measures for risk containment for derivatives.

1999: Enactment of the Securities Laws (Amendment) Act which defined derivatives as securities.

2000Withdrawal of 1969 notification

May 2000: SEBI granted approval to NSE and BSE to commence trading of derivatives

.June 2000Trading in Index futures commenced.

June 2001Trading in index options commenced. Ban on all deferral products imposed.

July 2001Trading in stock options commenced. Rolling settlement introduced for active derivatives.

Nov 2001Trading in stock futures commenced.

16

Returns of Nifty Index

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 88 175 262 349 436 523 610 697 784 871 958 1045 1132 1219 1306 1393 1480 1567 1654 1741 1828 1915 2002

Time

Ret

urns

Series1

Nifty JuniorIndex

-0.15

-0.1

-0.05

0

0.05

0.1

1 87 173 259 345 431 517 603 689 775 861 947 1033 1119 1205 1291 1377 1463 1549 1635 1721 1807 1893 1979 2065

Time

Ret

urns

Junior

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