The Impact of Index Futures on Spot Market Volatility in China

Transcription

1 The Impact of Index Futures on Spot Market Volatility in China Shiqing Xie and Jiajun Huang ABSTRACT: Using daily data of the China Securities Index (CSI) 300 between 005 and 0, we employ a set of GARCH models to investigate the impact of index futures trading on the volatility of the spot market in China. Our three main findings are as follows: () the launch of index futures does not decrease the volatility of the spot market; () there is a decrease in sensitivity to new information while sensitivity to historical information increases after introduction of the CSI 300 index futures; and (3) no leverage effect is found either before or after the introduction of the CSI 300 index futures. KEY WORDS: CSI 300, index futures, spot market, volatility. Stock index futures are among the most important instruments for effectively hedging systemic risk in equity markets. Owing to their hedging and price discovery functions, as well as the effect they have on efficiency in asset allocation, and so on, stock index futures are important components of investors portfolios and contribute to the perfection of the market. Considerable academic attention has been paid to recognizing the importance of index futures trading; however, conclusions regarding the influence of futures trading on the underlying spot market are mixed. Supporters of futures markets argue that futures trading can improve the quality of information and enhance pricing efficiency and therefore may reduce the volatility of the spot market to some degree. Conversely, critics argue that uninformed investors, who are attracted by the merits of the futures market, can potentially add noise to the futures market and thus indirectly increase price volatility in the spot market. To enrich and develop its financial markets, China launched its first stock index futures, China Securities Index (CSI) 300 index futures, on April 6, 00, in the Shanghai-based China Financial Futures Exchange. As the markets in China had long been awaiting the arrival of instruments for short selling, investors flooded into the index futures market as soon as it opened. After three days, the trading volume in the index futures market surpassed that of the Shanghai A share spot market. At the time of this writing, index futures have been trading in China for more than two years, and the market has developed significantly. Public concern has now been diverted from index futures themselves to their relationship with the spot market. Consequently, one key question remains: How has futures trading influenced the underlying spot market in China, specifically the volatility of the underlying spot market? We aim to address this question. This paper contributes to existing literature on the influence of index futures on spot market volatility in three key ways. First, despite the abundance of existing literature, little attention has been paid to the Chinese capital market. To our knowledge, the only paper in English that directly addresses the influence of index futures on spot market volatility Shiqing Xie (sxie@pku.edu.cn) is an associate professor of finance in the Department of Finance, School of Economics, Peking University, Beijing, China. Jiajun Huang (james_wongkc@outlook. com) is an analyst at the Bank of China, Macau. Emerging Markets Finance & Trade / January February 04, Vol. 50, Supplement, pp M.E. Sharpe, Inc. All rights reserved. Permissions: ISSN X (print)/issn (online) DOI: 0.753/REE X500S xie.indd 67 3/3/04 8:54:36 AM

2 68 Emerging Markets Finance & Trade in the Chinese capital market is one by Chen et al. (03). As the second largest economy in the world, China is now playing an increasingly vital role in the world economy due to its growing participation in the global market. As evidenced during the 008 global financial crisis and the eurozone s recent debt crisis, China s influence is felt the world over. Moreover, China is putting more effort into pursuing financial liberalization; for example, interest rates are being increasingly liberalized, and China is pushing for further internationalization of the renminbi (RMB). Therefore, it is imperative to include China in studies on the influence of index futures on spot market volatility. Second, we identify several aspects of the influence of index futures on spot price volatility. In addition to the magnitude of the volatility, we also investigate the sensitivity of spot market prices to information and the asymmetry of the volatility in the spot market in an attempt to draw more comprehensive conclusions about the impact of index futures on the underlying spot market in China. Third, we use updated data and extend the test period to two years. Since much of the existing literature was published not long after the introduction of index futures, the time span of the data employed is usually less than one year. Our extended data enable us to draw more robust conclusions. Chen et al. (03) do make a comparison between their panel data approach and the generalized autoregressive conditional heteroskedasticity (GARCH) approach and find that the results in the GARCH model are much more sensitive to the sample period. Nevertheless, we think the sensitivity results mainly from the short prefutures period they employ when conducting the test. The evidence in this paper indicates that the introduction of the CSI 300 index futures did not significantly reduce the overall volatility in the spot market. The reasons for this may be that strict entry barriers and government regulations in the futures market inhibit futures from performing their functions, such as hedging and price discovery, and thus reduce their level of influence on the spot market. Moreover, no obvious leverage effect exists in the volatility of the CSI 300 index either before or after the introduction of index futures. However, we do find that the introduction of the CSI 300 index futures changed the sensitivity of the spot market to both new and historical information; more specifically, sensitivity to new information decreased, and sensitivity to old information increased. Literature Review Whether the introduction of index futures reduces the volatility of the underlying spot market has long been a controversial issue. Many studies have been conducted on developed markets. These can be grouped into three categories according to their conclusions. Some studies claim that the volatility of the spot market decreases after the introduction of index futures. Bessembinder and Seguin (99) conduct an empirical analysis of the Standard & Poor s (S&P) 500 index spot and futures markets to examine equity market volatility and find that the introduction of stock index futures reduces spot market volatility. Moreover, they discover that this impact tends to be more pronounced in the long term. Lee and Ohk (99) also find that the volatility of stock returns in the Hong Kong stock markets decreases during their short test period. Bologna and Cavallo (00) and Chang et al. (999) find similar stabilizing effects of futures trading. However, some investigators find that the spot market becomes even more volatile after the introduction of index futures. Rubinstein (987) argues that many speculators xie.indd 68 3/3/04 8:54:36 AM

3 January February 04, Volume 50, Supplement 69 use index futures to engage in arbitrage between the spot and futures markets. To create these arbitrage opportunities, speculators may trade tactically, reduce the information contained in the stock price, and ultimately increase the volatility of the spot market. Damodaran (990) and Harris (989) empirically analyze fluctuations in the S&P 500 index spot and futures markets and find that the introduction of stock index futures indeed exacerbates the volatility of the spot market. Lockwood and Linn (990) draw the same conclusion from their analysis of the Dow Jones Industrial Average (DJIA) index. Antoniou and Holmes (995) also find that the spot price volatility of the British FTSE 00 index experiences a significant increase in the postfutures period due to the increase in the rate of flow of information. Other investigators are of the opinion that index futures neither increase nor decrease spot price volatility. Edwards (988) finds that there is no change in the volatility of the Value Line index after the introduction of its futures contract. Becketti and Roberts (990) investigate the impact of S&P 500 futures contracts on stock price volatility and find that stock index futures are related neither to the frequency nor to the size of stock price jumps, thus making no difference to the overall volatility. Antoniou et al. (998) examine the effect in six countries: Germany, Japan, Spain, Switzerland, the United Kingdom, and the United States. They find a significant destabilizing effect only in Germany and Switzerland; futures trading is found to have no effect in the other four countries. Gulen and Mayhew (000) also document no significant effect in twelve of the twenty-five countries they investigate. Chiang and Wang (00) report that the inception of MSCI Taiwan futures trading has no general effects on spot price volatility. Nevertheless, the empirical approaches adopted in these papers vary considerably. Therefore, the relevant literature can be further categorized into three groups according to the empirical techniques adopted. The first group comprises the typical before and after one-time studies (e.g., Becketti and Roberts 990; Brorsen 99; Edwards 988; Hodgson and Nicholls 99), which simply compare the volatility of the index before and after futures are introduced. The second group utilizes the matching-sample cross-section approach (e.g., Bae et al. 004; Chang et al. 999; Galloway and Miller 997; Harris 989), which compares changes in the volatility of the underlying stocks in the index with a matching set of nonconstituent stocks. The last group uses time series analysis to examine changes in volatility. Investigators in this group use mainly the GARCH approach (e.g., Antoniou and Holmes 995; Bologna and Cavallo 00; Lee and Ohk 99; Pok and Poshakwale 004) or time series regression with related explanatory variables (e.g., Darrat and Rahman 995). Since the GARCH approach is still widely used, we have also used this method in this paper. The single existing study on the impact of index futures trading on spot price volatility that also focuses on the Chinese market is conducted by Chen et al. (03). They use a new panel data approach developed by Hsiao et al. (0) and find that the introduction of index futures trading in China reduces the volatility of the stock market. However, one critical issue in their paper is that their sample period only extends up to June 0, when the market was still in its infancy. Hence, the long-term effects of futures trading cannot be detected. In addition, due to the fast development of China s financial market, we would also expect the short-term effects of futures trading to change. In this paper, we attempt to provide additional evidence by extending the period to April 0, which is two years after the inception of the index futures market. This should be long enough for the futures market to have manifested any long-term effects. xie.indd 69 3/3/04 8:54:36 AM

4 70 Emerging Markets Finance & Trade Empirical Strategy and Data Empirical Strategy Based on an extended form of the ARCH model (Engle 98), the GARCH model, which was proposed by Bollerslev in 986, and other models such as integrated GARCH (IGARCH), GARCH-in-mean (GARCH M), threshold GARCH (TGARCH), exponential GARCH (EGARCH), and power ARCH (PARCH), were developed to deal with different features of financial time series. Given that the daily price or return of a stock index is a typical time series and that we are primarily concerned with magnitude, sensitivity to information, and the leverage effect of volatility, we will employ GARCH M and Glosten Jagannathan Runkle GARCH (GJR-GARCH) models in this paper. The GARCH M model was proposed by Engle et al. in 987. This model adds a conditional variance term to the mean equation. A GARCH M(p, q) model is presented as k Rt = λ0 + λirt i + ρσt + εt i= εt = σtvt v t ~... iid N ( 0, ) q p σt = α0 + αε i t i+ βσ j t j, i= j= where α 0, β 0, and Σ q i=α i + Σ p j=β j to ensure the stationarity. Capital asset pricing model theory predicts that the price of financial assets will be proportionate to the risk. In other words, the greater the risk underlying an asset, the higher the return on the asset. Since the conditional variance term σ t represents the volatility and therefore the risk of the asset, the parameter of this term is seen as reflecting the relationship between the expected fluctuations σ t and the return R t. Most existing studies adopt the basic GARCH model to investigate the impact of index futures trading on the spot market (e.g., Antoniou and Holmes 995; Pok and Poshakwale 004). However, this fails to take into account the correlation between risk and return in the stock index, which may leave a bias in the residual and its conditional variance. However, the GARCH M model is designed to allow for precisely this connection. Thus, we employ the GARCH M model instead of the basic GARCH model in this paper. The general approach when examining the effect of the introduction of index futures on volatility is to add a dummy variable to compare the volatility before and after the commencement of trading. We introduce the dummy variable F, indicating the trading of index futures in the variance equation, in our GARCH M model. The variance equation of GARCH M(p, q) can then be presented as q σ = α + αε + βσ + θf, () t 0 i t i j t j i= j= where F equals 0 and before and after the introduction of index futures, respectively. If the estimation results show that the coefficient θ is significantly greater than 0, the introduction of stock index futures has amplified the volatility of the spot market. Conversely, if the coefficient is significantly less than 0, there has been a reduction in volatility. If neither is true, if θ is equal to 0 in the economic or statistical sense, this would suggest that index futures have no significant impact on spot prices. Leverage effect, which also has an impact on volatility, refers to the idea that a decline in prices (bad news) results in more fierce fluctuations in asset prices than does an increase p () xie.indd 70 3/3/04 8:54:37 AM

5 January February 04, Volume 50, Supplement 7 in prices (good news). The basic GARCH model does not distinguish between the two kinds of news and is, therefore, unable to describe the leverage effect. However, some augmented GARCH models, including EGARCH, TGARCH, and GJR-GARCH, have been developed to characterize asymmetric volatility in the financial market. Since most of the previous studies (e.g., Alexakis 007; Antoniou et al. 998) use the GJR-GARCH model, we also adopt this model in order to deploy our analysis. The GJR-GARCH model was proposed by Glosten et al. in 993. The variance equation of GJR-GARCH(p, q) is represented by q ( ) + σ = α + α + δ N ε β σ, (3) i 0 i i t i t i j t j i= j= where N t i = if e t i < 0; N t i = 0 if e t i > 0. The term δ i N t i in the model denotes the impact of different past shocks on the current conditional variance. If δ is positive in the estimation results, a negative leverage effect exists. Specifically, when a negative shock appears (ε t i < 0), the conditional variance tends to increase. When a positive shock appears (ε t i > 0), the conditional variance tends to decrease. p Data We focus on the Chinese CSI 300 index and its corresponding futures contract. Daily closing prices of the CSI 300 index from April 5, 005, to April 3, 0, are collected from the China Stock Market and Accounting Research database. This totals,700 observations. Given that April 6, 00, is the day when trading in index futures of the CSI 300 index was officially launched, comparing the volatility of the CSI 300 on either side of this date will be our main task in this analysis. Since price series follow a random walk process and are not stationary, we use the daily returns of the index to examine the volatility. Return is calculated as follows: R t = ln(p t ) ln(p t ). (4) After differencing,,699 observations of the daily returns are left. To assist in our explanation, ultimately the returns series we use is multiplied by 00. In addition, as we will examine some other features of the volatility in the spot market before and after the introduction of stock index futures, the collected data are divided into two subperiods: the prefutures period and the postfutures period. The prefutures period is from April 6, 005, to April 5, 00, with,6 observations; the postfutures period is from April 6, 00, to April 3, 0, with 484 observations. Table presents descriptive statistics of stock index returns during three periods: the whole period and the two subperiods stated above. The skewness for all three periods is negative but small in absolute value, indicating that the distribution is slightly skewed to the left. The kurtosis of the three periods is much greater than that of a normal distribution, implying that stock index returns show evidence of leptokurtosis and a fat-tailed distribution. To get more sound conclusions, we use the Jarque Bera test to check for normality within the results shown in Table. The statistics are all significant at the percent level, which violates the null hypothesis. Therefore, the returns from the CSI 300 index are not normally distributed whether you look at the whole period or the pre- and postfutures periods separately. This is consistent with the features of most financial time series. Unless otherwise stated, hereinafter we assume the residuals obey a generalized error distribution. xie.indd 7 3/3/04 8:54:37 AM

6 7 Emerging Markets Finance & Trade Table. Descriptive statistics and normality test of the CSI 300 return sequences Mean Standard deviation Skewness Kurtosis Jarque Bera p-value Whole period Prefutures period Postfutures period Empirical Results Stationarity and ARCH Test We employ the augmented Dickey Fuller (ADF) test to assess the stationarity of the CSI 300 return sequences. Lag order is determined by the Schwarz information criterion (SC). The values of the ADF test statistics are far less than the critical value at the percent significance level, indicating that the CSI 300 index daily return sequences are stationary in all three periods. Therefore, we can model the sequences directly without the need for differencing. To test the plausibility of the GARCH model, we examine whether an ARCH effect exists. To do this, we must first estimate the mean equations. We find that the return sequence of the postfutures period is a white noise sequence; the return sequence of the whole period and the prefutures period are not. According to the Akaike information criterion (AIC) and the SC, the AR() model is chosen to capture the characteristics of these two sequences. We use the residuals of the estimated AR() models as the prefutures and whole period sequences; the postfutures sequence is taken from the difference between the original sequence and the mean of the postfutures returns. We then conduct ARCH-LM (Lagrange multiplier) tests to detect whether an ARCH effect exists. The LM statistics are significant at the percent level from the lag order of one for all the three sequences, indicating that an ARCH effect is evident. Therefore, we can assure the appropriateness of the GARCH model in this paper. Do Index Futures Reduce Spot Market Volatility? GARCH M(, ) is usually used to describe financial time series due to its precision. To ensure the accuracy of the model, we estimate GARCH M(, ), GARCH M(, ), GARCH M(, ), and GARCH M(, ) and select the best GARCH M model according to the AIC and SC. The estimation results are reported in Table. As our previous analysis has determined that the daily return series of the CSI 300 for the whole period can be estimated using an AR() model, the mean equations of the GARCH M models are the AR() model added to a conditional variance term. Both AIC and SC indicate that GARCH M(, ) is the best model for the whole period. The estimation results of the GARCH M(, ) model are reported fully in Table 3. The coefficient on the dummy variable, F, is negative but not significant even at the 0 percent level. Therefore, the preliminary results show that we cannot reject the null hypothesis that the introduction of index futures has no impact on the volatility of the spot market in China. This finding contradicts Chen et al. (03), who argue that spot market volatility is significantly reduced by futures trading. However, as disclosed in the xie.indd 7 3/3/04 8:54:37 AM

7 January February 04, Volume 50, Supplement 73 Table. GARCH-M models of the CSI 300 Models AIC SC GARCH-M(,) GARCH-M(,) GARCH-M(,) GARCH-M(,) Table 3. Detailed results of the GARCH-M(, ) of the CSI 300 Coefficients Value Standard deviation t-value p-value Mean equation: R t = λ 0 + λ R t + ρσ t + ε t λ λ ρ Variance equation: σ t = α 0 + α ε t + β σ t + θf α α β θ preceding literature review, our finding is consistent with many other studies from other countries (e.g., Becketti and Roberts 990; Edwards 988). Cox (976) proposes that as there are relatively lower margin requirements and lower transaction costs in the futures market, the introduction of futures can attract more traders and result in more information pathways. If this is the case, it is plausible to expect that the trading of index futures will reduce spot price volatility. However, as the index futures markets in China face relatively strict entry requirements, high margin levels, and other specific government-imposed regulations, it is not surprising to find no evidence of this outcome. Notably, the positive and significant (at the 95 percent confidence level) coefficient of the risk premium term in the regression results suggests that there is a relationship between risk and return in the equity stock index. This again confirms the appropriateness of our utilization of the GARCH M model. Compared to previous studies that neglect this phenomenon, our conclusions are much more sound and robust. Do Index Futures Change Sensitivity to Information in the Spot Market? Although the results in Table 3 show that the introduction of the CSI 300 index futures does not have any obvious impact on spot price volatility, we can also examine whether the sensitivity to information of prices in the spot market has altered during the pre- and postfutures periods. This analysis is important given that sensitivity to information is the most critical element of market efficiency. Even if volatility does not change in magnitude, the sensitivity of prices to different kinds of information, that is, the structure of the volatility, may be influenced. xie.indd 73 3/3/04 8:54:37 AM

8 74 Emerging Markets Finance & Trade Table 4. Variance equations of the GARCH-M(, ) models for pre- and postfutures periods α 0 α β Prefutures 0.085* (.6587) Postfutures ( ) *** ( ) ( ) *** (64.670) *** (48.609) Notes: The cells show coefficient estimates with the t-values in parentheses. * p < 0.; ** p < 0.05; *** p < 0.0. As Antoniou and Holmes (995) claim, ARCH terms in the GARCH model represent new information obtained from the past few days, whereas the GARCH term represents historical information. Therefore, we can use the coefficients on these two terms to investigate the sensitivity of the spot market to new and historical information as well as being able to make a comparison of the pre- and postfutures periods. Two GARCH M(, ) models, which describe the two periods, are estimated and reported in Table 4. Since we are only interested in the variance equations, the estimation results of the mean equations are not presented here. According to the estimation results in Table 4, α, the coefficient reflecting sensitivity to new information, falls from to after the introduction of the CSI 300 index futures. This indicates that the Chinese spot market s sensitivity to new information has been weakened. However, it is generally believed that the introduction of stock index futures can improve the speed and quality of the dissemination of information, and therefore, it is normally expected that the sensitivity of the market to the new information would be heightened after this change. Our contradictory result may be due to the low frequency of the data we used. Currently, there are numerous ways to spread information incredibly fast. This means that news that emerged the previous day may not be considered fresh information by the following day. Therefore, both the contribution to the flow of information and spot price volatility would tend to be less affected by information from the previous day. Alternatively, β, the coefficient which represents sensitivity to historical information, rises from to after the introduction of index futures. This indicates that the Chinese stock market became more sensitive to historical information following the introduction of trading of index futures. In addition, when we look at the sum of α and β, we can see that it falls slightly from to This suggests a decline in the persistence of volatility or, to put it another way, the persistence of shocks. The coefficient α 0, which is a factor, like the sum of α and β, that affects the unconditional variance, increases from 0.09 to However, the sum of a and b decreases more significantly. Thus, we can see that the unconditional variance in total drops substantially. Do Index Futures Influence the Leverage Effect in the Spot Market? One stylized fact in the financial market is the so-called leverage effect: good and bad news may cause asymmetric reactions in the equity market. In addition to the magnitude of volatility and sensitivity to information, the introduction of index futures may also xie.indd 74 3/3/04 8:54:37 AM

9 January February 04, Volume 50, Supplement 75 Table 5. GJR-GARCH(, ) for the pre- and postfutures periods α 0 α β δ Prefutures (.6864) Postfutures ** (.98045) *** ( ) ( ) *** ( ) *** ( ) ( 0.560) ( ) Notes: The cells show coefficient estimates with the t-values in parentheses. * p < 0.; ** p < 0.05; *** p < 0.0. have an impact on the leverage effect in the spot market. To illustrate this, we employ the previously introduced GJR-GARCH in our analysis. The estimation results of the GJR-GARCH models for both the prefutures and postfutures periods are presented in Table 5. Table 5 shows that in both periods, δ is insignificant in the economic and statistical sense. This result implies that the leverage effect did not exist in the spot market before the introduction of index futures and that the introduction of futures did not alter market dynamics. However, Antoniou et al. (998) believe that if the futures market can attract noise traders from the spot market, then volatility asymmetries that are observed in the spot market before the initiation of futures trading will not be detected in the market after the futures are introduced, or they should be present in the futures market itself. In addition, Chiang and Wang (00) find that although futures trading has little impact on the level of stock market volatility in Taiwan, asymmetries in volatility increase after the introduction of index futures. They ascribe the increased asymmetries to the large proportion of noninstitutional investors. Our findings, which uncover no such effect of futures trading, are in obvious contrast with their results. Empirically, the low frequency data we use may account for the contradiction. Conclusion The introduction of the CSI 300 index futures ends the inability to short sell in China s financial market and boosts further development of the securities market. With regard to the impact of the introduction of futures trading on the volatility of the underlying spot market, previous studies, which focus on different markets and are based on different methods, have diverse conclusions. With our study, we aim to enrich the existing literature and specifically investigate the newly initiated index futures in China. To do this, we use data from the CSI 300 index between 005 and 0 and employ GARCH models to test whether the introduction of index futures has reduced spot price volatility, influenced sensitivity to information, or had an impact on the leverage effect in the spot market. Our paper has three main findings. First, the introduction of the CSI 300 index futures does not have a significant effect on the magnitude of spot price volatility. The insignificant dummy variable representing the introduction of index futures in the GARCH M model suggests that the introduction neither increases nor decreases the volatility of the spot market. One possible reason for this is that the mechanisms of stock index futures trading and the relevant market regulations are still underdeveloped. For example, functions such as hedging and price discovery are not yet mature, and thus, spot market volatility may not have been xie.indd 75 3/3/04 8:54:37 AM

10 76 Emerging Markets Finance & Trade significantly affected. On the other hand, we find that there is a positive risk premium on the return of the stock index, indicating that in one respect, there is pricing efficiency in the Chinese stock market. Second, the introduction of the CSI 300 index futures alters the sensitivity of the spot market to new information and to historical information. The coefficients on the ARCH term in the GARCH model fall after the introduction of index futures, implying a decline in sensitivity to new information. This result contradicts theoretical expectations. However, given that it has been only two years since the trading of index futures began, we can attribute this result to a higher-than-expected enthusiasm of investors for opening new accounts and investors potential ignorance of new information. The increase in the GARCH term coefficients shows amplification in sensitivity to historical information, leading to the conclusion that market reactions to historical information become faster after the launch of index futures. Third, we find no evidence of a leverage effect in the volatility of the CSI 300 index either before or after the introduction of index futures. The leverage effect is a commonly observed phenomenon in many equity markets. Previous studies written in Chinese claim that the asymmetry of the volatility in the Chinese stock market is not as evident as that seen in developed markets when they were in the early phase of development. This is attributed to the lack of a short mechanism in the Chinese equity market. Our study shows that, even if stock index futures are introduced to function as a short instrument, there is no apparent difference in investors reactions to good or bad news. In sum, the introduction of the CSI 300 index futures does not significantly reduce the overall volatility in the spot market or influence the nature of that volatility. The only difference that has occurred is the spot market s sensitivity to new and historical information. We find that most of our conclusions are different from previous studies thanks to two factors: () the time interval is much longer in our study and () we use daily data rather than high-frequency data. The former aspect makes our conclusions more reliable, but the latter reason may mean that our conclusions do not reflect short activities in the spot market. Therefore, further research could use more high-frequency data to get more robust conclusions. In addition, our paper does not intend to examine at length why futures trading does not play an important role in influencing spot market volatility. Further research could investigate aspects such as price discovery (Kang and Ryu 00), investor structure, and transaction motive of both the Chinese stock market and index futures market to explore the reasons behind this phenomenon. References Alexakis, P On the Effect of Index Futures Trading on Stock Market Volatility. International Research Journal of Finance and Economics : 7 0. Antoniou, A., and P. Holmes Futures Trading, Information and Spot Price Volatility: Evidence for the FTSE-00 Stock Index Futures Contract Using GARCH. Journal of Banking & Finance 9, no. : 7 9. Antoniou, A.; P. Holmes; and R. Priestley The Effects of Stock Index Futures Trading on Stock Index Volatility: An Analysis of the Asymmetric Response of Volatility to News. Journal of Futures Markets 8, no. : Bae, S.C.; T.H. Kwon; and J.W. Park Futures Trading, Spot Market Volatility, and Market Efficiency: The Case of the Korean Index Futures Markets. Journal of Futures Markets 4, no. : Becketti, S., and D.J. Roberts Will Increased Regulation of Stock Index Futures Reduce Stock Market Volatility? Economic Review 75, no. 6 (November December): xie.indd 76 3/3/04 8:54:37 AM

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