the asian crisis exchange risk exposure of us multinationals.pdf

8/9/2019 The Asian Crisis Exchange Risk Exposure of US multinationals.pdf

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Managerial FinanceVol. 33 No. 9, 2007pp. 710-740# Emerald Group Publishing Limited0307-4358DOI 10.1108/03074350710776253

The Asian crisis exchange riskexposure of US multinationals

Willem F.C. Verschoor and Aline Muller Nijmegen School of Management, Radboud University Nijmegen,

Nijmegen, The NetherlandsAbstractPurpose – This paper aims to increase understanding of the (time-varying) relationship betweenexchange rates and stock prices at the individual firm level. Rather than analyzing the impact of exchange rate movements on firm value by regressing multinationals’ stock returns on exchange ratechanges, it is proposed to examine the impact of increased exchange rate variability on the stockreturn volatility of US multinationals by focusing on the 1997 Asian financial turmoil.Design/methodology/approach – In a first step, it is investigated whether the enhanceduncertainty about the future performance of US multinationals active in Asia resulted in an increasedstock return variability. The second step separates the impact of increased exchange rate variabilityon the stock return volatility of US multinationals into systematic and diversifiable risk.Findings – It is found that the stock return variability of US multinationals increases significantly inthe aftermath of the financial turmoil. In conjunction with this increase in total volatility, there is alsoan increase in market risk (beta) for US multinationals. Moreover, trade- and service-orientedindustries appear to be particularly sensitive to these changing exchange rate conditions.Practical implications – If the additional risk imparted to exposed firms from increased exchangerate variability is systematic in nature, it will affect the required rate of (equity) return (i.e. investorsdemand higher returns for holding the firm’s shares). Consequently, this effect of exchange ratefluctuations increases the cost of (equity) capital for US multinationals with real foreign operations inthe crisis countries.Originality/value – This paper demonstrates the impact of increased exchange risk on stock returnvolatility and market risk.Keywords Multinational companies, Financial risk, Asia, Exchange rates, Stock returns

Paper type Research paper

1. IntroductionOn July 2nd, 1997 the Thai baht abandoned its peg to the US dollar. The change fromhighly stable exchange rate regimes in Asia to floating regimes was associated with asharp increase in exchange rate variability. Within one year, the Asian stock marketsdeclined on average between 40 and 60 per cent, while the currencies of Indonesia,South Korea and Thailand each lost nearly half of their value[1]. It is a common belief that the most recent Asian financial crisis is more widespread than previous crises, andhence is exerting a greater effect on commodity prices, financial markets and economicactivity throughout the world; the perception has arisen that the crisis has been more

virulent in its impact on the affected local and global economies. Worldwide economicgrowth slowed, commodity prices were brought to a historical low, risk premiums indebt markets increased, both stock market volatility and capital flows enhanced whileconfidence indicators slumped around the globe. Furthermore, the Asian crisis appearsto be more deeply rooted in financial imbalances in the private sector than in the publicsector financial problems that characterized the 1980s debt crisis and the 1994-1995Mexican crisis. The wide currency fluctuations experienced during the 1997 Asiancrisis raised a variety of questions not only about their impact on affected economies,but also about their influence on the potential vulnerability of multinational firms toforeign exchange risk.

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0307-4358.htm


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The purpose of this paper is to increase our understanding of the (time-varying)relationship between exchange rates and stock prices at the individual firm level.Rather than analyzing the impact of exchange rate movements on firm value byregressing multinationals’ stock returns on exchange rate changes, we consider the

foreign exchange risk exposure puzzle from a different angle. Motivated by Bartov et al.(1996) and Chen and So (2002), we examine the impact of increased exchange ratevariability on the stock return volatility of US multinationals by focusing on the 1997Asian financial turmoil. More specifically, we analyze the change in US stock marketrisk in response to the onset – or fear – of an exchange rate regime shift in Asiancountries where these US companies are internationally active. Significant contributionof increased exchange rate variability to systematic risk would imply that the cost of equity capital for these firms increases relative to that of non-multinational (domestic)firms.

The current study complements previous work and makes several maincontributions. Using a sample of 372 US multinational firms, we find that the increasein exchange rate variability around the change from highly stable exchange rateregimes to floating regimes is associated with a statistically significant increase instock returns’ volatility of US multinationals that are engaged in foreign sales activitieswith these turmoil markets compared with the control firms. The breakdown betweensystematic and diversifiable risk shows moreover that the stock market risk (beta) of these US multinationals increases significantly during periods of increased exchangerate uncertainty. Furthermore, we demonstrate that the trade, services, finance,insurance and real estate as well as the agriculture, mining and construction sectorsare particularly sensitive to exchange rate crises’ uncertainty. Finally, it appears thatsmall capitalization firms are especially exposed to changes in the international tradeenvironment.

The remainder of this paper is organized as follows. The first section contains theresearch design. Section two presents the Asian exchange rate movements against theUS dollar and the US multinational firm-level data set. In section 3, we examinethe impact of increased exchange rate variability on stock return volatility of USmultinationals. In section 4, the estimates of the extent to which the riskiness of USmultinationals are exposed to increased exchange rate uncertainty are presented andanalyzed. Section 5 closes with some sensitivity analyses across industries and marketcapitalization classes. The final section concludes our findings.

2. Research designThe financial crisis triggered in Thailand in July, 1997 sent shock waves throughoutSoutheast Asia and the globe. Whether the observed contagion was the result of financial panic or whether it was rational is still a matter of intensive debate that has

been extensively covered in the literature[2]. In this paper, we take the view that theAsian currency crisis was primarily propagated through rational channels andconcentrate on the role played by existing trade relationships in the transmission of thecrisis[3,4]. Although the crisis was felt around the world we focus particularly on thespread of the Asian currency crisis towards US stock markets[5,6].

The change from highly stable exchange rate regimes to floating regimes and theresulting volatile currency movements were an important source of macroeconomicuncertainty for US multinational firms with foreign trade relationships in Asia. Theresulting macroeconomic confusion wholly modified the Asian economic environmentand remodeled capital flows around the world. Empirical studies demonstrate that the


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rise in the volatility of exchange rates – and the subsequent increase in uncertainty andrisk – has significant consequences on trade flows[7]. We expect that the largeexchange rate swings in the aftermath of the Asian currency crisis altered the tradeterms between Asia and the US and that the increased exchange rate variability

contributed to the uncertainty of the economic and financial environment of USmultinationals active in Asia. The rapid expansion of currency crises to world stockmarkets, through an increase in the observed volatility of financial markets and capitalflows around the world, has led academics and investors to re-evaluate the impact of (increased) exchange rate fluctuations on stock markets[8].

In this paper, we empirically explore the (time-varying) relationship between Asianexchange rates and US stock prices at the individual firm level. In a first step, weinvestigate whether the enhanced uncertainty about the future performance of USmultinationals active in Asia resulted into an increased stock return variability. Even if firms that did not entertain trade relationships with Asia countries may have beenindirectly influenced by the economic waves following the crisis, there is no doubt thatmultinational firms that had real operations in the crisis countries were the firsteconomic actors to be affected. We, therefore, expect the increase of the stock returnvariability to be more important for US multinationals with real operations in Asiathan for other US firms. Motivated by Bartov et al. (1996), we hence create a controlsample of firms in the same line of business and of similar size (market capitalization)to test for a different impact of the currency crisis on ‘‘non-Asia oriented’’ firms. Thisenables us further to control for the influence of other confusing forces and for possibleconfounding factors related to industryor firm size.

The second step separates the impact of increased exchange rate variability on thestock return volatility of US multinationals into systematic and diversifiable risk.Whether some proportion of this enhanced stock return variability is diversifiable ornot has important implications for the firm and investors. In a well-diversifiedportfolio, only systematic risk, which cannot be diversified away, receivescompensation through higher required rates of return. Thus, an increase in a firm’ssystematic risk with respect to the US equity market portfolio leads to an increase inthe required rate of return and an increase in the cost of (equity) capital. To measure thesystematic risk (beta) of US companies we use the augmented market model suggestedby Jorion (1990). The estimation of this multifactor model enables us to analyzewhether increased exchange rate uncertainty influences the sensitivity of USmultinational firms to market risk (beta) and whether the impact of exchange ratemovements on the equity value of these firms rises during periods of increasedexchange rate variability. Intuitively, the contribution of exchange rate uncertainty to afirm’s sensitivity to market risk can be motivated by the impact of exchange ratevolatility on its trade activities – and hence on its business risk – as well as by the

existence of other repercussions to the fundamentals of such firms that are not sharedto the same degree by the market as awhole.Furthermore, the augmented market model allows us to verify whether the 1997

Asian financial crisis affected the foreign exchange risk exposure of US multinationals – defined as the sensitivity of firm value to exchange rate movements. For the pastdecade, many researchers have been empirically investigating the foreign exchangerisk exposure of multinational firms. Despite a multinational’s extensive involvementin international activities and the implication of economic theory, existing literaturehas met with limited success in identifying significant contemporaneous correlationsbetween exchange rate fluctuations and US stock returns[9]. The controversy has


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stimulated the interest of many researchers in similar issues involving other countries,especially those with market characteristics different from the USA while motivatedothers to explain the difficulty in obtaining stable and significant measures of exchange exposure[10, 11].

Overall, the examination of increased exchange rate variability on the stock returnvolatility of US multinationals provides an interesting framework for determining theimportance and time-varying nature of the relation between exchange rates and stockprices of multinational firms. Moreover, this framework provides certain advantagesover existing studies. First, since variances are estimated over a multiperiod window,our analysis does not suffer from the potential temporal instability of the sign of theexposure (Bartov and Bodnar, 1994) and reduces the necessity that the impact of theexchange rate on stock prices be contemporaneous (Bartov et al., 1996). Second,analyzing the impact of large exchange rate swings caused by currency crises, wemitigate the effects of hedging activities because of the relative unexpectedness of these volatile currency movements[12]. Third, it enables us to test the hypothesis thatthe impact of large magnitude currency movements on trade spillovers – hence, on firmvalue – is more significant than the impact of small fluctuations[13]. Under theseconditions and in the light of the evidence discussed above, we expect that USmultinationals with real operations in Asia have greater exposure to exchange rate riskduring the period of increased exchange rate variability. Considering that their tradingactivities were directly influenced by the changing currency environment, this impactshould be most identifiable for these ‘‘Asia-oriented’’ US companies. Finally, asexchange rate movements primarily affect firm value through their impact on tradeflow, US multinationals that produce or consume non-traded goods should be lessaffected by the changes in their currency environment. Similarly, due to the positiveimpact of a US dollar appreciation on US importing activities and its negative effecton US exporting activities, we suppose that US industry sectors that rely heavily onboth exporting and importing activities are less influenced by changes in foreignexchange rates. Industries that primarily serve the consumer sectors are presumedmore exposed to currency fluctuations than institutional-oriented industries. If we limitourselves to examine aggregate results, we ignore these differential effects acrossindustries. In this paper, we, therefore, use an industry-level variation to identify whichtypes of sectors are most affected by the increased exchange rate variability. Tostrengthen our analysis further, we examine our findings across different marketcapitalization categories.

3. Data description3.1 US multinational firm-level data set In this study, we examine how the Asian currency crisis affected US wealth. As

suggested by Forbes (2004), trade linkages (either through bilateral trade orcompetition in third markets) are important transmission channels of exchange rateshocks. Although firms might not entertain trade relationships with the crisiscountries directly, they might be indirectly affected by the economic waves following afinancial crisis; there is no doubt that multinational firms that have real operations inthe crisis countries are the first economic actors to be influenced by these wideexchange rate swings. We therefore only include in our test sample US multinationalswith real production and/or trade operations in Asian countries. To identify thesefirms, we first select multinational companies based on the information provided in the1995 and 1999 versions of the Directory of American Firms Operating in Foreign


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Countries . As we only include listed firms in our study, we check the firms for theirweekly stock market return availability in the University of Chicago Center forResearch in Security Prices (CRSP). We moreover restrict our sample to companieswith at least complete six-month price information both for the pre-crisis and the post-

crisis windows. This reduces our sample to 372 firms. These multinationals formtogether four different test samples depending on the crisis country where they areactive[14].

In order to prevent confounding inferences concerning the causes of the changes inthe stock return variability of the sample firms, we construct for each test sample amatching control sample. In order to create matching control samples consisting of firms in the same line of business and of similar size as each of the sample firms, we gothrough the following procedure. First, within the CRSP database, we identify for eachsample firm all the firms, listed on an US stock market, that were active in the samefour-digit industry sector during the crisis period. Among these firms we then selectthree or four firms within the same market capitalization category as the sample firm.As a result, we pick out a total of 1,360 companies[15]. These control firms are eitherdomestic or multinational companies that have not any direct production or traderelationships with Asia and are called ‘‘non-Asia’’ oriented firms in the remainder of this paper.

3.2 Economic factorsWe use two economic factors in this study: the market risk factor and the exchange raterisk factor. The proxy for the market portfolio is the equally weighted US stock marketindex as provided by the University of Chicago CRSP database. The exchange rate riskfactor is alternately measured as the bilateral continuously compounded exchangewith the US dollar (defined as the local currency for Indonesia, Malaysia, Korea andThailand per US dollar).

3.3 Asian exchange rate fluctuationsThe year 1997 marked the end of pegged exchange rate regimes in Asia. Thailand wasthe first Asian country to break its official parity on July 2, 1997[16]. A couple of dayslater, the Bank Negara Malaysia resigned intervening to smooth the fluctuations of theringgit and let the currency float. Indonesia and Korea were forced to abandon theirpegged exchange rate systems, respectively, in August and December, 1997. Table Iprovides a brief overview over the exchange rate arrangements before and after thecurrency crisis and Figure 1 displays the evolution of these four Asian currenciesagainst the US dollar from July 1995 till January 2000.

Figure 1 shows that the change from highly stable exchange rate regimes to floatingregimes was associated with a huge increase in exchange rate variability. Moreover, the

foreign exchange market became much more volatile during the crisis, whereas thevalue of these Asian currencies nearly halved. This observation is confirmed in Table IIcontaining the descriptive statistics of the Asian exchange rate series before and aftertheir respective regime shifts. Both magnitude and variability of currency movementsdramatically increased around the change in the exchange rate regime.

4. Exchange rate crisis and stock return variabilityTo examine empirically the link between exchange rate uncertainty and stock returnvariability, we measure the variance of the stock returns of our sample and controlfirms over two approximately two-year windows[17]. The first window precedes the


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date of the decision to let the currency float, whereas the second window covers theperiod after this decision was taken. To evaluate the significance of the change in stock

return variability across the two periods, we use the following Chi-squared statistic:2 ð2 N Þ ¼ 2 X

i ¼1 ; N

ln pi

where pi is the p-value for the F -test of the test of the change in variances for firm i fromthe pre-crisis window to the post-crisis window and N is the number of firms includedin the sample. Under the null hypothesis of no change in stock return variance acrossthe two sub-periods, the sample distribution of the F -statistics is random and the teststatistic is asymptotically distributed 2 with 2 N degrees of freedom[18].

Figure Exchange rates of the

dollar against Ascurrenc

Table Crisis dates a

exchange rarrangeme

Country Crisis date

Depreciationa againstUSD at the crisis

date (%)

Declared exchangerate regime

before the crisis

Exchange rateregime into which

the country switchedafter the crisis

Thailand July 1997 24.34 Basket peg Independently floatingMalaysia July 1997 4.19 Managed float Managed floatb

Indonesia August 1997 16.78 Crawling band Independently floatingKorea December 1997 45.64 Exchange rate band Independently floating

Notes: aNominal depreciation against the US dollar during the crisis month; bMalaysia switched tothe conventional peg arrangement in September 1998, a year after the currency crisis startedSource: IMF Annual Report on Exchange Arrangements and Exchange Restrictions, variousnumbers


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The first four columns of Table III report the summary statistics of the cross-sectionaldistribution of firm-level stock return variances before and after the crisis date for thetest samples. The significance levels of the 2 statistics reveal that the hypothesis of nochange in the return variances of US multinational firms active in the crisis countries isstrongly rejected. The last four columns describe the corresponding analysis for thecontrol samples. We can observe that for the control firms we also reject the nullhypothesis in favor of the alternative hypothesis that the volatility of stock returns of

the control firms was higher in the post-crisis sub-period than in the pre-crisis sub-period. As suggested by Bartov et al. (1996), this may be due to the fact that large shocksin exchange rate markets are correlated with other forms of increased macroeconomicuncertainty and may therefore affect all firms independently of their foreigninvolvement. Furthermore, Asian large currency swings caused price and income effectsthat not only affect direct bilateral trade linkages, but also price competition and incomerepercussion in third markets, thereby indirectly influencing the entire US stock market.

Table III also reports the results of a non-parametric Wilcoxon signed-rank test.This test, that verifies the null hypothesis of no shift in the Median variance of stockreturns after the crisis, has the advantage of being less sensitive to outliers than the

Table II.Descriptive statistics of Asian exchange ratemovements against theUS dollar

Thailand Malaysia Indonesia Korea

Before the currency regime shift Mean 4.86E-05 0.000321 0.001462 0.003603Median 0.000539 9.86E-05 0.000671 0.001233Maximum 0.040652 0.009189 0.050408 0.067925Minimum 0.045832 0.014002 0.006542 0.01229SD 0.006955 0.002931 0.006082 0.010141Skewness 1.137771 0.818569 6.15655 4.331855Kurtosis 30.66901 8.304157 49.33361 25.47884Observations 104 104 90 102

After the currency regime shift Mean 0.003781 0.008349 0.009272 0.0003Median 0.000728 0.008121 0.005001 0.0028Maximum 0.119739 0.134524 0.384237 0.253863Minimum 0.085482 0.089084 0.202118 0.129046SD 0.028887 0.031673 0.079235 0.040609

Skewness 0.556685 0.367082 1.397956 3.125792Kurtosis 6.934636 7.140746 9.102251 21.36434Observations 105 61 103 107

Notes: This table provides summary statistics on weekly log price changes of four Asiancurrencies – the Thai baht, the Malaysian ringgit, the Indonesian rupiah and the Korean won. Thesample periods are respectively, for Thailand from 5 July 1995 till 25 June 1997 (before the regimeshift) and from 2 July 1997 till 30 June 1999 (after the regime shift); for Malaysia from 19 July1995 till 9 July 1997 (before the regime shift of 1997) and from 16 July 1997 till 26 August 1998(after the regime shift of 1997 and before Malaysia returned to a conventional peg arrangement inSeptember 1998); for Indonesia from 22 November 1995 till 6 August 1997 (before the regimeshift) and from 13 August 1997 till 28 July 1999 (after the regime shift); and for Korea from 6December 1995 till 12 November 1997 (before the regime shift) and from 19 November 1997 till 1December 1999 (after the regime shift)Data source: Global Financial Data


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the relative change in the total stock return variability of the control sample, while thenon-parametric rank-based Wilcoxon test examines the equality of the medians.

Table IV provides explicit results regarding the relative firm-level stock returnvariances for test and control samples. The results of Table IV suggest that for both the

Thai and the Korean crises, there is no statistically significant difference in the changeof stock return volatility between the test and the control sample. The analyses of theMalaysian and Indonesian crises, however, reveal that the stock return volatilities of the test sample firms increased significantly more than the volatilities of their controlsample counterparts[19].

Table IV.Stock return variabilityacross exchange rateregimes – Cross-sectionaldistributions of relativefirm level stock returnvariances

Test sample Control sample Comparison of thedistributions

Post-crisisvariance/Pre-crisis

variance

Post-crisisvariance/Pre-crisis

varianceTest

statistic Significance

Panel A: Thailand, July 1997 Mean 1.99888 1.95056 H0 . equality of means 0.162 0.8717Median 1.84199 1.77375Maximum 4.62023 4.78443 H0 . equality of medians 0.672 0.5018Minimum 0.56918 0.46071

Panel B: Malaysia, July 1997 Mean 2.13407 1.92710 H0 . equality of means 1.572 0.1167Median 2.01649 1.70584Maximum 6.54202 5.65564 H0 . equality of medians 2.630 0.0085*Minimum 0.18151 0.47039

Panel C: Indonesia, August 1997 Mean 2.13216 1.84041 H0 . equality of means 2.728 0.0067*Median 2.02807 1.75569Maximum 4.84700 5.03572 H0 . equality of medians 2.135 0.0328*Minimum 0.53732 0.47944

Panel D: Korea, December 1997 Mean 1.98823 1.97008 H0 . equality of means 0.670 0.5034Median 1.86350 1.82318Maximum 4.80327 5.22044 H0 . equality of medians 0.731 0.4647Minimum 0.76287 0.23744

Notes: The summary statistics describe the distributions of the relative changes in firm-levelvariances from the pre-crisis period to the post-crisis period. The test statistics report a t -test onthe difference in means between the distributions of the test and control sample ratios and aWilcoxon rank-sum test on the shift in median values between the distributions. The significancelevels are for the rejection of the null hypothesis that the distributions of test firms’ ratios areequal to the distributions of control firms’ ratios. *indicates 5 per cent significance level. Thesample periods are, respectively: for Thailand from 5 July 1995 till 25 June 1997 (before theregime shift) and from 2 July 1997 till 30 June 1999 (after the regime shift); for Malaysia from 19 July 1995 till 9 July 1997 (before the regime shift of 1997) and from 16 July 1997 till 26 August1998 (after the regime shift of 1997 and before Malaysia returned to a conventional pegarrangement in September 1998); for Indonesia from 22 November 1995 till 6 August 1997 (beforethe regime shift) and from 13 August 1997 till 28 July 1999 (after the regime shift); and for Koreafrom 6 December 1995 till 12 November 1997 (before the regime shift) and from 19 November1997 till 1 December 1999 (after the regime shift)


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Overall, these findings suggest that the stock return variability of multinationals ispositively related to exchange rate variability. Correspondingly, the occurrence of acurrency crisis has a significant positive impact on the stock return variability of bothour test and control samples. Whereas US domestic and multinational firms without

real operations in the crisis countries seem to be affected by the financial repercussionscaused by the increased exchange rate uncertainty, it appears nevertheless that USmultinationals with close trading and production activities in the crisis countries aremore sensitive to exchange rate risk in the aftermath of a crisis.

5. Exchange rate variability, currency exposure and market risk (beta)For international investors and financial managers it could have important implicationwhether the documented increase in the total stock return variability of USmultinationals resulting from the financial turmoil can be diversified away or whetherit causes additional systematic risk. As in efficient markets only systematic riskreceives compensation through an increase in the required rate of return, thebreakdown between systematic and diversifiable risk provides an insight into therelative cost of capital of US multinationals active in the country crisis and other ‘‘non-Asia’’ oriented multinational firms.

Furthermore, it is highly presumable that the sensitivity of US multinationals tofluctuations in exchange rates changed after the exchange rate regime shifts. As wasdiscussed in section 2, the magnitude and variability of Asian exchange ratefluctuations grew significantly in the aftermath of the crisis. While Asian currencieswere mostly restricted within relatively narrow bandwidths before the crisis, theunexpected large currency swings after the crisis had severe economic repercussionson trade and capital flows. The huge consequences of these fluctuations on US tradeactivities lead us to believe in an enhanced vulnerability of US multinationals to Asianforeign exchange rate risk in the aftermath of the crisis.

To empirically address these issues, we estimate for each firm, the sensitivity of itsstock returns to US stock market risk as well as to Asian foreign exchange ratemovements. The exchange exposure of a firm can be measured by the followingaugmented market model:

R it ¼ i þ i R mt þ i X t þ "it ð2Þ

where R it designates the total return of firm i in period t , R mt the overall US stockmarket return in period t , i firm i ’s return sensitivity to US market fluctuations, X t therate of return on the Asian currenciy vis-a`-vis the US dollar (measured as the Asianexchange price of the US dollar), i firm i ’s exposure to exchange rate changesindependent of the effect these variations have on the overall market, and " it denotesthe white noise error term. Hence i is the exchange rate exposure measure because it

describes the sensitivity of stock returns to unanticipated changes in exchange rates.An appreciation of the US dollar makes exporting goods more expensive in terms of the foreign (Asian) currencies, and this maylead to a fall in foreign demand and foreignsales revenue. On the other hand, the importing firm will benefit from an appreciationof the US dollar, as its imports become cheaper in terms of the US dollar. Thus, the i coefficient should be positive for net-importers and negative for net-exporters[20].Furthermore, changes in exchange rates alter the US dollar value of Asian-denominated fixed assets and liabilities; US multinational firms with net exposedAsian denominated liabilities will gain with a strengthening US dollar, while firmswith net exposed Asian denominated assets lose.


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One of the most important features of financial weekly time series is the presence of heteroskedasticity. Indeed, the hypothesis of constant variance, we implicitly make inthe above-mentioned model (Equation (2)), is in most cases rejected for commonfinancial weekly time series – like exchange rate and stock returns series[21]. As the

presence of heteroskedasticity invalidates the test statistics, we suggest adding aGARCH(1, 1) specification to the basic regression model each time we detectheteroskedasticity in the firm-level stock return series. The choice of a GARCH(1, 1)specification is supported by many empirical studies showing that the GARCH(1, 1)specification is valuable for modeling the variance generating process of financial timeseries. Thus, the regression model we use in these cases can be described as:

R it ¼ i þ i R mt þ i X t þ "it "i ;t ¼ i ;t ðhi ;t Þ12 ð3Þ

hi ;t ¼ i þ "2i ;t 1 þ vhi ;t 1 ð4Þ

where hi ,t denotes the conditional variance of the residuals and i ,t the white noise errorterm.

Considering each exchange rate regime shift separately, we estimate the augmentedmarket model firm by firm; first, over the pre-crisis window – the estimated coefficientsare labeled 1, 1 and 1, respectively – and, secondly, over the post-crisis window – the estimated coefficients are labeled 2, 2 and 2, respectively. The results displayedin Table V enable us hence to verify whether the above documented increase of totalstock return variability causes additional systematic risk for our sample companiesand whether the sensitivity of US stock returns to fluctuations in Asian currenciesincreased in the aftermath of a crisis[22].

Table V shows the evolution of cross-sectional mean, median, first and third quartilevalues of the ordinary least squares estimators of the 1 and 2 coefficients for the twosub-sample periods. The summary statistics of the 1 coefficients reveal that for mostsamples the mean market risk of the test sample companies was lower than the meanmarket risk of the corresponding control sample firms before the crisis. This findingtends to support the argument that test sample firms benefited from greatergeographic diversification advantages than ‘‘non-Asia’’ oriented US multinationalfirms before the currency crisis exploded. This deduction, however, should been takencarefully as the control samples include not only US domestic firms but also USmultinational firms that are not active in the crisis countries and could, hence, profitfrom the diversification impact of other foreign activities.

Furthermore, the evidence indicates that 2 coefficients tend to be systematicallylarger than 1 coefficients across test samples while they remain unchanged or loweracross control samples. The comparison of the relative changes in market risk acrosstest and control samples suggests moreover that the sensitivity of US multinationals to

stock market risk increased significantly more for test firms than for control firms. Themean and median values of these relative changes suggest, indeed, that the market riskof US multinationals with real operations in the Asian crisis economies increasedsignificantly by approximately 20-25 per cent while it decreased by 10-20 per cent forcontrol firms. The test sample that seems most affected by the currency crisis regroupsUS multinational firms that were active in Indonesia. Their market beta increased froma cross-sectional mean value of 0.9722 during the fixed exchange rate period to 1.0457during the floating exchange rate period. This outcome may be attributable to the factthat, relative to other Asian markets, Indonesia experienced the most volatile currencymovements after its currency crisis (see Figure 1).


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Table VChanges in risk acr

exchange rate regimeCross-sectio

distribution of firm-lechanges in market r

and exchange rexpos

B e f o r e c r i s i s :

1

A f t e r c r i s i s :

2

R e l a t i v e c h a n g e

M a r k e t r i s k

N

M e a n

M e d i a n

Q 1

Q 3

M e a n

M e d i a n

Q 1

Q 3

1 /

2

s i g n .

P a n e l A : T h a i l a n d , J

u l y 1 9 9 7

T e s t s a m p l e ( S D )

8 8

1 . 0 7 5 8

0 . 9 8 6 4

0 . 6 4 2 6

1 . 3 6 2 6

1 . 0 9 1 2

1 . 0 4 0 2

0 . 7 1 5 7

1 . 4 5 0 1

M e a n

1 . 2 0 0

0 . 0 6 8 6

0 . 0 5 6 0

M e d i a n

1 . 0 1 1

C o n t r o l s a m p l e ( S D )

3 2 4

1 . 1 8 9 9

0 . 9 6 8 9

0 . 6 8 9 3

1 . 5 2 4 1

1 . 0 0 7 1

0 . 9 4 3 9

0 . 6 3 7 7

1 . 2 8 6 6

M e a n

0 . 9 4 6

0 . 0 0 0 0 a

0 . 0 3 9 3

0 . 0 2 8 9

M e d i a n

0 . 8 7 3

0 . 0 0 0 6

b


1


2

C u r r e n c y r i s k e x p o s u r e

N

M e a n

M e d i a n

Q 1

Q 3

n *

M e a n

M

e d i a n

Q 1

Q 3

n *


8 8

0 . 3 7 2 3

0 . 0 4 5 6

1 . 1 6 3 3

0 . 4 8 7 1

1 7

0 . 0 3 5 1

0 . 0 6 1 1

0 . 1 3 5 3

0 . 0 2 7 5

3 5

0 . 1 9 9 0

1 9 . 3

2 %

0 . 0 1 9 0

3 9 . 7 7

%


3 2 4

0 . 0 3 7 9

0 . 0 4 2 4

0 . 4 4 7 8

0 . 3 3 1 0

4 1

0 . 0 5 7 3

0 . 0 5 7 6

0 . 1 4 1 3

0 . 0 3 7 3

5 0

0 . 0 4 5 1

1 2 . 6

5 %

0 . 0 0 8 7

1 5 . 4 3

%

P a n e l B : M a l a y s i a

, J u l y 1 9 9 7


1


2

M a r k e t r i s k

N

M e a n

M e d i a n

Q 1

Q 3

M e a n

M e d i a n

Q 1

Q 3

1 /

2

s i g n .


9 7

1 . 1 0 9 7

0 . 9 3 8 0

0 . 6 0 8 6

1 . 4 3 2 0

1 . 1 9 3 6

1 . 1 1 4 8

0 . 7 3 1 5

1 . 5 5 1 1

M e a n

1 . 2 8 7

0 . 0 7 3 9

0 . 0 5 9 5

M e d i a n

1 . 1 8 8


3 6 0

1 . 3 1 0 5

1 . 0 4 2 3

0 . 7 4 3 1

1 . 6 7 9 0

1 . 1 7 3 3

1 . 0 7 0 2

0 . 7 2 2 4

1 . 4 7 8 7

M e a n

0 . 9 1 1

0 . 0 0 2 6 a

0 . 0 4 2 5

0 . 0 3 2 7

M e d i a n

0 . 7 9 2

0 . 0 0 0 5

b

( C o n t i n u e d )


13/32

MF33,9

722

Table V.


1


2


N

M e a n

M e d i a n

Q 1

Q 3

n *

M e a n

M

e d i a n

Q 1

Q 3

n *


9 7

0 . 3 0 0 0

0 . 0 1 1 8

1 . 2 4 8 4

0 . 8 4 1 2

1 6

0 . 1 0 0 0

0 . 0 0 6 5

0 . 2 5 4 0

0 . 0 3 3 4

4 9

0 . 4 1 5 9

1 6 . 4

9 %

0 . 0 3 3 4

5 0 . 5 2

%


3 6 0

0 . 3 8 3 1

0 . 2 9 2 1

0 . 6 6 8 5

1 . 2 7 1 0

5 6

0 . 0 3 0 5

0 . 0 3 1 5

0 . 1 2 8 5

0 . 0 6 4 1

4 4

0 . 1 0 9 1

1 5 . 5

6 %

0 . 0 1 0 3

1 2 . 2 2

%


1


2

M a r k e t r i s k

N

M e a n

M e d i a n

Q 1

Q 3

M e a n

M e d i a n

Q 1

Q 3

1 /

2

s i g n .

P a n e l C : I n d o n e s i a

, A u g u s t 1 9 9 7


7 1

0 . 9 7 2 2

0 . 8 3 1 7

0 . 6 1 2 4

1 . 2 1 0 6

1 . 0 4 5 7

0 . 9 4 2 8

0 . 6 5 0 7

1 . 4 1 2 8

M e a n

1 . 2 0 6

0 . 0 6 2 2

0 . 0 5 9 1

M e d i a n

1 . 0 4 3


2 5 6

1 . 1 6 5 7

0 . 9 8 3 9

0 . 7 5 1 9

1 . 5 0 2 9

1 . 0 7 2 5

0 . 9 6 8 7

0 . 7 0 8 8

1 . 3 4 0 0

M e a n

0 . 9 7 4

0 . 0 0 0 0 a

0 . 0 3 6 9

0 . 0 3 0 3

M e d i a n

0 . 9 2 5

0 . 0 1 0 4

b


1


2


N

M e a n

M e d i a n

Q 1

Q 3

n *

M e a n

M

e d i a n

Q 1

Q 3

n *


7 1

0 . 2 9 6 5

0 . 4 5 8 9

0 . 7 4 6 9

1 . 3 3 5 6

1 4

0 . 0 0 6 9

0 . 0 0 0 7

0 . 0 5 1 0

0 . 0 0 9 7

2 4

0 . 2 3 9 1

1 9 . 7

2 %

0 . 0 0 7 6

3 3 . 8 0

%


2 5 6

0 . 0 2 6 0

0 . 0 1 7 2

0 . 3 7 1 4

0 . 3 8 3 4

2 9

0 . 0 0 8 8

0 . 0 1 0 0

0 . 0 4 4 8

0 . 0 2 4 3

2 3

0 . 0 4 3 9

1 1 . 3

3 %

0 . 0 0 3 6

8 . 9 8 %

P a n e l D : K o r e a , D

e c e m b e r 1 9 9 7


1


2

M a r k e t r i s k

N

M e a n

M e d i a n

Q 1

Q 3

M e a n

M e d i a n

Q 1

Q 3

1 /

2

s i g n .


1 1 6

1 . 0 8 1 1

0 . 9 9 7 4

0 . 7 5 8 8

1 . 5 9 6 1

1 . 0 8 2 2

1 . 0 3 8 5

0 . 7 2 1 7

1 . 5 5 3 8

M e a n

1 . 0 4 0

0 . 0 5 7 8

0 . 0 5 3 2

M e d i a n

0 . 9 5 3


4 2 0

1 . 3 3 1 2

1 . 1 5 7 6

0 . 8 0 6 3

1 . 7 2 6 6

1 . 1 7 5 8

1 . 0 5 7 6

0 . 7 1 1 2

1 . 5 5 6 3

M e a n

0 . 9 3 1

0 . 0 0 0 0 a

0 . 0 3 3 5

0 . 0 2 9 5

M e d i a n

0 . 8 6 5

0 . 0 0 0 1

b



14/32


15/32

MF33,9

724

The empirical results in Table V show moreover the evolution of the Asian foreignexchange risk exposures of US multinationals. We find that US multinationals with realoperations in Asia were significantly more vulnerable to fluctuations in Asian exchangerates after the exchange rate regime shift from fixed to floating exchange rates. While

only 72 test sample firms experienced economically significant exposure effects to theAsian currencies during the fixed exchange rate period, 170 firms experienced exposureeffects during the floating exchange rate period[23]. It seems likely that the increasedexchange rate variability faced by US multinationals had led to a higher percentage of firms with significant exchange risk exposure. This finding tends to support the viewthat the foreign exchange risk exposure effect is time-variant (Bartov and Bodnar, 1994).Hence, these findings also suggest that US multinationals did not increase their hedgingactivities when the volatility of the Asian currencies was highest[24].

Overall, the results are consistent with our expectations and corroborate the findingsof Bartov et al. (1996) and Chen and So (2002)[25]. Following the sharp increase inexchange rates’ variability due to the currency crisis, the stock market risk of USmultinationals active in these emerging economies increased significantly. While all of the firms experienced an increase in stock return volatility following the exchange rateregime shifts, there is a significant difference in the nature of this increased volatilityacross the different samples of firms. The increased exchange rate variability increasesthe US multinational’s systematic risk with respect to the US equity market portfolio. Inaddition, the negative change in market risk experienced by many control firms duringthe post-crisis period moreover suggests that the relative shift in market risk betweensample and control firms is even larger than reported by the 2 coefficients for samplefirms. Thus, US multinational firms saw their beta rising as a result of the Asianfinancial turmoil and, correspondingly, are faced with higher equity financing costs. Theevidence moreover suggests time-variation in exposure at the individual firm level.

6. Sensitivity analysis6.1 Across industriesDifferent industries present different types of import and export patterns as well asdifferent competitive environments for their firms. The impact of an increase inexchange rate uncertainty may therefore affect some industries differently than others.If, for example, a sharp depreciation in the crisis country increases the competitivenessof its exports, this has a negative impact on firms competing with those exportswhereas it may have a positive influence on foreign firms using those exports as inputsin their production process. In order to specifically identify which types of industriesare most affected by the crises that originate elsewhere, this paper uses an industry-level variation for the firm-by-firm analysis.

In order to perform this industry-level analysis, both test and control samples are

sub-divided in eight different industry sectors. Results shown in Table VI reveal thatUS multinational firms within the Wholesale and Retail Trade, the Finance, Insuranceand Real Estate as well as the Services industries experienced a sharp increase insystematic risk with respect to the US equity market portfolio during the period of increased exchange rate variability. It is quite comprehensive that these sectors werehardest hit by the Asian financial turmoil. These sectors consist mainly of US firmsthat export ‘‘finished’’ goods and services towards Asian markets. As such theysuffered both from their relative loss in competitiveness compared to Asian firms andfrom the decrease in the Asian demand due to the income effect. As suggested inTable VI, US multinationals in the Agriculture, Mining and Construction industries


16/32


72

Table VChanges in risk acr

exchange rate regimeAnalysis of cro

sectional distributionfirm-level change

market risk aexchange risk expos

across industr

B e f o r e c r i s i s

A f t e r c r i s i s

1

1

2

2

N

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

P a n e l A : T h a i l a n d , J

u l y 1 9 9 7

T e s t s a m p l e

A g r i c u l t u r e , m

i n i n g a n d c o n s t .

1

0 . 4 2 6 1

–

0 . 0 4 8 3

–

0

0 . 4 7 2 9

–

0 . 0 6 0 6

–

0

–

–

0 . 0 0 %

–

–

0 . 0 0 %

B a s i c m a n u f a c t u r i n g

5

0 . 7 9 2 9

0 . 6 6 6 5

0 . 0 7 8 4

0 . 4 3 6 1

0

0 . 7 5 6 3

0 . 8 4 5 3

0 . 0 0 1 4

0 . 0 0 5 3

0

0 . 3 2 8 0

0 . 4 9 9 0

0 . 0 0 %

0 . 1 3 0 5

0 . 0 6 7 5

0 . 0 0 %

C h e m i c a l s , p l a s t i c s a n d p e t r o l e u m r e l a t e d

1 1

0 . 7 9 7 3

0 . 8 8 1 0

0 . 7 7 1 2

0 . 6 6 4 0

3

0 . 9 0 4 5

0 . 8 7 2 5

0 . 0 1 3 0

0 . 0 0 2 7

9

0 . 0 9 6 9

0 . 4 5 7 7

2 7 . 2

7 %

0 . 1 6 8 3

0 . 0 3 9 4

8 1 . 8

2 %

M a n u f a c t u r i n g ( m e t a l s r e l a t e d )

3 1

1 . 3 0 0 0

1 . 1 1 2 1

0 . 5 0 8 4

0 . 0 4 4 4

4

1 . 1 9 5 1

1 . 1 5 7 3

0 . 0 8 6 5

0 . 1 1 0 9

8

0 . 1 2 6 6

0 . 2 9 9 7

1 2 . 9

0 %

0 . 0 7 9 2

0 . 0 2 9 9

2 5 . 8

1 %

T r a n s p o r t a t i o n , c o m . a

n d u t i l i t i e s

6

1 . 1 1 4 8

0 . 8 0 2 9

0 . 3 9 1 3

0 . 0 5 6 0

1

0 . 9 7 9 1

1 . 1 5 9 9

0 . 1 4 7 8

0 . 1 0 5 0

3

0 . 4 5 5 2

0 . 6 3 1 3

1 6 . 6

7 %

0 . 2 8 2 0

0 . 0 7 5 1

5 0 . 0

0 %

W h o l e s a l e a n d r e t a i l t r a d e

8

0 . 8 0 6 2

0 . 4 5 4 9

0 . 3 4 6 4

0 . 1 6 5 3

1

0 . 9 3 0 6

0 . 9 7 7 5

0 . 1 3 6 0

0 . 0 9 8 7

2

0 . 3 4 8 9

0 . 3 7 9 0

1 2 . 5

0 %

0 . 2 7 9 3

0 . 1 1 8 2

2 5 . 0

0 %

F i n a n c e , i n s u r a n c e a n d r e a l e s t a t e

1 1

0 . 9 5 7 8

0 . 9 3 5 3

0 . 3 9 6 4

0 . 1 7 4 6

2

1 . 0 6 5 5

0 . 8 9 8 5

0 . 1 5 8 2

0 . 1 2 3 3

3

0 . 1 3 7 2

0 . 7 7 8 4

1 8 . 1

8 %

0 . 2 0 0 1

0 . 0 3 4 2

2 7 . 2

7 %

S e r v i c e s

1 5

1 . 1 6 7 1

1 . 0 8 6 6

0 . 9 0 0 6

0 . 0 5 1 9

6

1 . 3 1 6 2

1 . 2 1 0 7

0 . 0 7 0 0

0 . 0 1 0 3

1 0

0 . 1 7 0 3

0 . 6 6 6 8

4 0 . 0

0 %

0 . 1 2 6 1

0 . 0 5 1 6

6 6 . 6

7 %

C o n t r o l s a m p l e



3

0 . 8 9 4 1

0 . 9 5 3 4

0 . 5 7 2 5

0 . 3 8 0 3

1

0 . 8 6 6 9

0 . 6 0 3 8

0 . 0 0 2 8

0 . 0 0 3 9

1

0 . 2 7 7 7

0 . 6 4 1 1

3 3 . 3

3 %

0 . 4 4 7 6

0 . 1 2 1 1

3 3 . 3

3 %


2 0

0 . 7 5 2 9

0 . 6 5 1 8

0 . 1 5 5 4

0 . 2 4 8 5

1

0 . 7 1 4 7

0 . 5 9 0 0

0 . 0 5 3 6

0 . 0 5 2 1

1

0 . 0 8 2 6

0 . 0 9 1 8

5 . 0 0 %

0 . 0 9 9 3

0 . 0 1 9 2

5 . 0 0 %


4 0

0 . 7 5 0 4

0 . 6 9 2 5

0 . 1 9 4 1

0 . 1 4 4 6

6

0 . 8 0 8 0

0 . 6 9 9 6

0 . 0 7 4 1

0 . 0 8 9 1

7

0 . 0 5 6 4

0 . 0 6 7 5

1 5 . 0

0 %

0 . 0 7 6 2

0 . 0 1 7 9

1 7 . 5

0 %


1 1 6

1 . 4 3 5 2

1 . 1 8 0 8

0 . 1 5 5 0

0 . 1 1 2 2

1 2

1 . 1 4 6 1

1 . 0 9 2 7

0 . 0 4 9 9

0 . 0 5 7 1

2 4

0 . 0 7 7 1

0 . 0 8 2 0

1 0 . 3

4 %

0 . 0 5 0 3

0 . 0 1 5 7

2 0 . 6

9 %



2 4

1 . 0 0 5 6

0 . 9 8 6 2

0 . 1 8 7 5

0 . 1 7 2 9

5

0 . 9 3 3 6

0 . 9 0 5 5

0 . 0 7 7 2

0 . 0 6 1 4

1

0 . 0 7 9 9

0 . 1 8 1 7

2 0 . 8

3 %

0 . 1 0 4 2

0 . 0 2 7 9

4 . 1 7 %



17/32

MF33,9

726

Table VI.



1

1

2

2

N

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .


3 4

1 . 1 0 7 9

0 . 8 8 1 5

0 . 3 4 0 4

0 . 3 6 4 5

5

0 . 8

3 2 0

0 . 7 8 2 8

0 . 0 5 4 1

0 . 0 4 1 6

2

0 . 1 1 8 7

0 . 1 5 2 5

1 4 . 7

1 % 0

. 0 7 6 3

0 . 0 2 0 1

5 . 8 8 %


3 9

0 . 9 3 9 5

0 . 8 0 9 8

0 . 0 0 8 0

0 . 0 3 8 0

3

0 . 9

6 2 9

0 . 9 7 2 1

0 . 1 0 8 6

0 . 1 0 8 1

7

0 . 0 8 5 4

0 . 0 7 3 0

7 . 6 9 % 0

. 0 7 4 7

0 . 0 2 1 1

1 7 . 9

5 %

S e r v i c e s

4 8

1 . 5 1 7 4

1 . 4 9 4 6

0 . 0 3 5 4

0 . 0 7 2 3

8

1 . 1

6 4 4

1 . 0 9 1 2

0 . 0 1 6 4

0 . 0 2 1 9

7

0 . 0 9 2 0

0 . 1 5 2 9

1 6 . 6

7 % 0

. 0 7 4 0

0 . 0 3 1 2

1 4 . 5

8 %

P a n e l B : M a l a y s i a

, J u l y 1 9 9 7

T e s t s a m p l e



4

1 . 2 6 4 0

1 . 2 5 0 9

0 . 5 2 4 7

0 . 6 1 0 6

0

1 . 6

1 7 1

1 . 8 7 9 4

0 . 1 0 4 7

0 . 1 0 6 8

0

0 . 1 4 7 3

0 . 6 1 7 6

0 . 0 0 % 0

. 5 4 7 8

0 . 1 1 3 4

0 . 0 0 %


5

0 . 6 8 8 0

0 . 6 3 7 9

0 . 5 6 1 3

0 . 5 1 4 8

2

0 . 6

9 4 6

0 . 5 8 4 5

0 . 0 0 9 5

0 . 0 0 7 0

3

0 . 1 3 1 2

0 . 7 1 9 7

4 0 . 0

0 % 0

. 0 9 1 6

0 . 0 7 9 9

6 0 . 0

0 %


1 5

0 . 6 1 9 9

0 . 5 9 3 2

0 . 0 9 4 5

0 . 0 3 3 3

1

0 . 7

1 7 6

0 . 6 8 0 0

0 . 0 9 9 5

0 . 0 0 4 0

7

0 . 0 7 7 5

0 . 5 1 0 2

6 . 6 7 % 0

. 0 8 5 8

0 . 0 6 6 8

4 6 . 6

7 %


4 1

1 . 2 4 6 1

1 . 1 4 4 2

0 . 9 6 7 7

0 . 0 6 1 9

9

1 . 2

8 5 8

1 . 2 4 3 0

0 . 0 3 6 5

0 . 0 0 8 8

2 0

0 . 1 1 2 1

0 . 8 0 2 9

2 1 . 9

5 % 0

. 0 8 6 1

0 . 0 4 9 4

4 8 . 7

8 %



6

1 . 1 8 8 2

1 . 2 5 7 3

0 . 4 7 2 3

- 0 . 3

3 1 4

0

1 . 0

9 4 6

1 . 1 4 1 1

0 . 2 6 0 5

0 . 2 1 6 5

4

0 . 2 7 8 4

0 . 4 0 1 7

0 . 0 0 % 0

. 2 0 5 9

0 . 1 3 6 5

6 6 . 6

7 %


1

1 . 2 8 7 9

–

0 . 8 2 6 4

–

0

1 . 4

0 0 9

–

0 . 4 0 6 0

–

1

–

–

0 . 0 0 %

–

–

1 0 0 . 0 %


9

1 . 0 6 6 4

1 . 0 1 9 3

0 . 6 8 9 3

0 . 6 7 9 4

2

1 . 2

2 9 7

1 . 0 4 4 9

0 . 2 8 0 8

0 . 3 2 5 5

5

0 . 0 8 8 4

0 . 6 0 4 4

2 2 . 2

2 % 0

. 2 0 4 4

0 . 0 6 1 0

5 5 . 5

6 %

S e r v i c e s

1 6

1 . 2 9 6 4

1 . 0 1 0 1

0 . 0 0 2 8

0 . 1 5 4 0

2

1 . 4

5 7 2

1 . 1 6 1 8

0 . 2 1 4 0

0 . 0 5 9 1

9

0 . 2 5 2 8

0 . 7 2 3 2

1 2 . 5

0 % 0

. 1 1 6 4

0 . 1 2 2 8

5 6 . 2

5 %




1 2

0 . 9 8 0 4

0 . 9 8 0 7

1 . 1 4 5 9

1 . 3 4 5 9

1

1 . 2

6 2 2

1 . 2 7 4 3

0 . 1 0 7 3

0 . 0 1 9 3

0

0 . 0 9 1 8

0 . 4 7 7 8

8 . 3 3 % 0

. 2 1 6 8

0 . 0 6 4 2

0 . 0 0 %


1 9

0 . 8 3 0 5

0 . 7 3 3 0

0 . 2 5 8 5

0 . 4 0 3 0

2

0 . 8

4 9 7

0 . 8 5 3 4

0 . 0 0 0 6

0 . 1 1 6 9

2

0 . 0 8 7 1

0 . 2 6 1 5

1 0 . 5

3 % 0

. 0 9 1 2

0 . 0 2 1 4

1 0 . 5

3 %



18/32


72

Table V



1

1

2

2

N

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .


5 6

0 . 8 4 6 0

0 . 7 0 1 0

0 . 1 1 4 2

0 . 1 3 6 6

7

0 . 9 2 0 8

0 . 8 3 7 6

0 . 0 5 1 6

0 . 0 2 0 3

8

0 . 0 6 5 8

0 . 2 1 2 7

1 2 . 5

0 % 0

. 0 6 4 0

0 . 0 1 9 5

1 4 . 2

9 %


1 4 7

1 . 5 2 6 2

1 . 3 6 7 0

0 . 6 5 1 1

0 . 4 2 3 3

2 6

1 . 3 2 2 5

1 . 2 4 6 6

0 . 0 1 7 5

0 . 0 1 9 9

1 5

0 . 0 7 0 0

0 . 1 7 7 2

1 7 . 6

9 % 0

. 0 5 1 4

0 . 0 1 6 1

1 0 . 2

0 %



2 3

1 . 1 6 6 0

0 . 9 9 2 2

0 . 8 1 6 5

0 . 8 8 9 0

6

1 . 1 6 8 8

1 . 0 4 6 3

0 . 0 0 5 4

0 . 0 5 5 7

1

0 . 1 3 4 0

0 . 3 4 5 7

2 6 . 0

9 % 0

. 1 9 9 9

0 . 0 4 6 2

4 . 3 5 %


3

1 . 0 7 4 3

1 . 0 4 1 1

0 . 2 9 8 5

0 . 3 6 1 8

0

0 . 9 8 5 8

0 . 9 5 7 2

0 . 0 1 4 6

0 . 0 3 8 5

0

0 . 1 2 4 5

0 . 8 7 1 7

0 . 0 0 % 0

. 1 6 9 5

0 . 0 3 6 4

0 . 0 0 %


3 7

0 . 8 3 2 4

0 . 8 4 6 9

0 . 4 4 8 0

0 . 4 2 1 2

7

0 . 9 2 4 4

0 . 9 1 3 2

0 . 0 5 5 9

0 . 0 3 2 5

1 1

0 . 0 4 3 8

0 . 1 1 5 2

1 8 . 9

2 % 0

. 0 5 8 4

0 . 0 2 5 1

2 9 . 7

3 %

S e r v i c e s

6 3

1 . 7 7 2 7

1 . 5 4 9 6

0 . 1 3 3 0

0 . 0 7 4 6

7

1 . 2 8 8 6

1 . 1 7 3 9

0 . 0 3 6 6

0 . 0 1 3 2

7

0 . 1 1 7 8

0 . 3 7 7 6

1 1 . 1

1 % 0

. 0 7 0 7

0 . 0 3 3 2

1 1 . 1

1 %

P a n e l C : I n d o n e s i a

, A u g u s t 1 9 9 7

T e s t s a m p l e



2

0 . 6 9 6 2

0 . 6 9 6 2

0 . 5 5 1 4

0 . 5 5 1 4

0

1 . 0 6 6 7

1 . 0 6 6 7

0 . 0 0 2 4

0 . 0 0 2 4

1

0 . 0 1 1 9

0 . 3 8 2 5

0 . 0 0 % 0

. 5 7 7 4

0 . 1 6 0 4

5 0 . 0

0 %


1

0 . 4 7 9 9

–

3 . 3 9 8 9

–

0

0 . 4 7 0 0

–

0 . 0 1 1 5

–

0

–

–

0 . 0 0 %

–

–

0 . 0 0 %


1 8

0 . 8 2 9 9

0 . 8 0 2 0

0 . 0 3 8 7

0 . 4 2 1 6

3

0 . 9 0 1 7

0 . 8 7 2 7

0 . 0 0 2 0

0 . 0 0 5 2

5

0 . 0 6 6 5

0 . 3 6 6 0

1 6 . 6

7 % 0

. 1 1 1 5

0 . 0 1 5 1

2 7 . 7

8 %


2 0

1 . 1 8 7 9

1 . 0 8 2 9

1 . 1 8 6 1

1 . 1 1 5 2

5

1 . 1 5 2 3

1 . 0 8 9 7

0 . 0 2 4 5

0 . 0 2 2 6

8

0 . 1 4 5 7

0 . 4 5 1 5

2 5 . 0

0 % 0

. 0 8 4 5

0 . 0 1 1 4

4 0 . 0

0 %



8

0 . 9 9 1 6

0 . 8 3 0 9

1 . 4 6 1 3

1 . 3 1 7 4

2

1 . 0 0 2 8

0 . 9 5 1 7

0 . 0 0 5 8

0 . 0 4 4 4

1

0 . 2 2 8 0

0 . 8 6 8 9

2 5 . 0

0 % 0

. 1 8 2 0

0 . 0 3 4 5

1 2 . 5

0 %


7

0 . 7 4 2 3

0 . 6 0 7 0

0 . 6 8 3 5

1 . 1 0 2 1

0

0 . 9 8 5 1

0 . 6 1 4 6

0 . 0 4 6 3

0 . 0 0 7 2

3

0 . 2 0 4 2

0 . 7 9 0 5

0 . 0 0 % 0

. 4 9 5 9

0 . 0 3 8 7

4 2 . 8

6 %

F i n a n c e , I n s u r a n c e a n d r e a l e s t a t e

8

1 . 1 0 6 0

0 . 9 4 2 1

0 . 6 6 4 4

0 . 4 1 6 6

2

1 . 2 5 5 8

1 . 4 1 9 1

0 . 0 4 7 0

0 . 0 5 0 8

3

0 . 1 9 6 0

0 . 4 7 3 2

2 5 . 0

0 % 0

. 2 0 7 9

0 . 0 2 0 4

3 7 . 5

0 %



19/32

MF33,9

728

Table VI.



1

1

2

2

N

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

M e a n

M e d i a n

M e a n

M e d i a n

S i g n .

S e r v i c e s

7

0 . 9 2 5 9

1 . 1 0 4 4

0 . 5 8 1 2

0 . 4 1 0 9

2

1 . 0 5 6 4

0 . 9 4 2 8

0 . 0 0 6 5

0 . 0 0 2 6

3

0 . 1 9 3 6

0 . 9 9 9 1

2 8 . 5

7 % 0

. 1 5 7 6

0 . 0 0 8 2

4 2 . 8

6 %




8

0 . 6 7 3 7

0 . 6 8 6 8

0 . 0 3 6 6

0 . 0 9 4 1

1

0 . 7 8 7 3

0 . 7 0 4 3

0 . 0 3 4 4

0 . 0 3 8 2

0

0 . 0 9 5 0

0 . 1 7 1 2

1 2 . 5

0 % 0

. 1 7 9 7

0 . 0 1 7 1

0 . 0 0 %


4

0 . 6 2 4 5

0 . 6 1 1 8

0 . 1 9 3 0

0 . 0 6 4 7

0

0 . 5 7 2 2

0 . 4 7 5 3

0 . 0 1 9 2

0 . 0 1 0 7

1

0 . 0 9 4 2

0 . 3 2 0 4

0 . 0 0 % 0

. 0 3 0 3

0 . 1 8 8

2 5 . 0

0 %


6 7

1 . 0 2 6 5

0 . 8 7 5 7

0 . 0 4 1 6

0 . 0 9 0 0

1 0

1 . 0 2 7 3

0 . 8 9 9 8

0 . 0 1 0 6

0 . 0 1 2 7

4

0 . 0 5 8 0 1 . 1 3 6 5

0 . 0 7 4 8

1 4 . 9

3 % 0

. 0 6 2 8

0 . 0 0 6 3

5 . 9 7 %


6 1

1 . 3 3 1 1

0 . 0 0 2 0

0 . 0 1 5 8

6

1 . 2 4 5 9

1 . 1 2 1 7

0 . 0 1 0 2

0 . 0 0 2 5

8

0 . 0 6 8 6 0 . 9 1 4 8

0 . 1 0 5 1

9 . 8 4 % 0

. 0 6 2 1

0 . 0 0 8 3

1 3 . 1

1 %



3 1

1 . 0 4 6 4

0 . 2 1 4 6

0 . 2 7 4 8

3

0 . 9 4 7 4

0 . 8 8 0 2

0 . 0 0 1 5

0 . 0 1 2 3

3

0 . 1 0 9 9 0 . 9 3 2 7

0 . 1 2 2 3

9 . 6 8 % 0

. 0 8 1 5

0 . 0 1 2 3

9 . 6 8 %


2 5

1 . 2 4 0 8

0 . 1 8 3 5

0 . 2 1 3 3

1

1 . 0 1 6 9

0 . 9 0 0 2

0 . 0 1 3 3

0 . 0 1 1 5

1

0 . 2 1 6 4 0 . 8 9 8 0

0 . 1 7 7 2

4 . 0 0 % 0

. 1 3 8 0

0 . 0 1 1 6

4 . 0 0 %


3 1

0 . 9 7 3 1

0 . 0 6 9 8

0 . 1 8 0 5

4

0 . 9 0 9 0

0 . 8 6 1 6

0 . 0 2 7 5

0 . 0 2 4 4

4

0 . 0 7 3 7 1 . 5 4 7 3

0 . 0 7 0 2

1 2 . 9

0 % 0

. 0 5 7 4

0 . 0 0 7 1

1 2 . 9

0 %

S e r v i c e s

2 9

1 . 6 2 0 0

0 . 0 4 6 4

0 . 0 3 7 5

4

1 . 3 1 6 5

1 . 3 6 2 5

0 . 0 0 4 7

0 . 0 1 4 1

2

0 . 1 1 7 6

0 . 1 6 5 4

1 3 . 7

9 % 0

. 0 7 3 3

0 . 0 1 3 4

6 . 9 0 %

P a n e l D : K o r e a , D

e c e m b e r 1 9 9 7

T e s t s a m p l e



0

–

–

–

–

–

–

–

–

–

–

–

–

–

–


6

0 . 5 8 4 0

0 . 6 7 1 2

0 . 0 4 3 1

0 . 0 2 2 9

2

0 . 5 3 4 0

0 . 5 8 4 7

0 . 0 1 2 3

0 . 0 2 7 2

3

0 . 0 9 5 4

0 . 0 4 9 9

3 3 . 3

3 % 0

. 1 2 8 1

0 . 0 9 7 4

5 0 . 0

0 %


1 7

0 . 7 1 0 3

0 . 7 8 8 1

0 . 1 8 7 9

0 . 0 1 5 6

4

0 . 7 9 9 0

0 . 6 9 7 5

0 . 0 7 7 0

0 . 0 7 9 8

1 1

0 . 0 7 7 6

0 . 1 4 6 7

2 3 . 5

3 % 0

. 0 9 6 7

0 . 0 6 5 4

6 4 . 7

1 %



20/32


72

Table V



1

1

2

2

N

M e a n

M e d i a n

M

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