A SumoBrain Solutions Company. Search Expert Search Quick Search. SEARCH RESEARCH MPEP 2. Impact of futures and asymmetric effect on the volatility of Indian stock market. Dec, Source Volume: Abstract The purpose of this article is to investigate the effect of the introduction of stock index futures on the volatility of the spot equity market and to test the impact of the introduction of the stock index futures contracts, a GARCH model is modified along the lines of GJR-GARCH and EGARCH model, especially to take into account the link between information and volatility.

This paper provides the evidence that there is not much change in the volatility pattern after the introduction of futures in the Indian stock market. The impacts of futures trading for the post futures period can be captured by the asymmetric coefficient d , suggest that there is a statistically significant and positive asymmetric effect.

Thus the introduction of futures trading has impact on the asymmetric coefficient. It shows the similar pattern for the pre and post futures period. Empirical research can be further expanded by selecting and analyzing high frequency intraday data and the inclusion of additional economic variables in the conditional variance equation.

Index Futures, volatility, asymmetric effect, futures market JEL Classification: Examining the impact of futures trading on the stock market volatility in a way that allows for consideration of volatility clustering and asymmetric responses to news not only provides important guidance yield insights into the reasons why asymmetric exist. If the market dynamics are the cause of asymmetries, than innovations, such as introduction of futures, may be expected to impact not only the level of volatility in the underlying market, but also on the structure and characteristics of that volatility.

This article seeks to address the issue of the impact that the existence of futures trading has on market volatility. The rest of the article is organized as follows. The next section discusses some theoretical issues and develops the arguments as to the role of futures markets and their potential effects on stock price volatility.

The third section presents the empirical design. The data and results are provided in the forth section and final section concludes. Of them asymmetry is one. Asymmetry volatility refers to a situation in which a negative shock to financial time series is likely to cause volatility to rise by more than a positive shock of the same magnitude. In the case of equity returns, such asymmetries are typically attributed to "leverage effects", whereby a fall in the value of a firm's stock causes the firm's debt to equity ratio to rise.

This leads shareholders, who bear the residual risk of the firm, to perceive their future cash flow stream as being relatively riskier. Asymmetric or leverage volatility models explain that good news and bad news have different predictability on future volatility.

An asymmetry reaction of conditional volatility to the arrival of news is induced both by the sign of past shocks and the size of unexpected volatility Engle and Ng There are factors other than the sign of past shocks that are responsible for the asymmetric behavior of volatilities. To examine this, Fornari and Mele proposed the Volatility--Switching ARCH model that captures asymmetries via the impact of past shocks on the level of volatility, rather than through the unexpected returns.

Not only shocks and noise trading but seasonality in asset behavior also affects stock return. Even if it is found that spot market volatility has increased post futures, this is not necessarily an undesirable consequence of derivative trading, because there may, simultaneously, be a change in the spot market dynamics that removes asymmetries and improve the transmission mechanism for news.

He argued that futures lead the cash index returns, by responding more rapidly to economic events than stock prices. Thus, new market information may be disseminated faster in the futures market compared to the stock market and that futures volatility spills some information over to cash market volatility.

Thus, it is important to use a model to take into account the link between information and volatility and possible asymmetric responses to news, so as to seen whether the introduction of stock index futures trading has increased or decreased spot price volatility and to investigate the extent to which the introduction if stock index futures contracts also affected the nature of volatility in the underlying spot market.

The GJR model is a simple extension of GARCH model, with an additional term added to account for possible asymmetries.

The conditional variance is now given by [[sigma]. The condition for non negativity will be [[alpha]. While the GARCH models successfully capture thick tail returns and volatility clustering, they are not well-suited to capture the "leverage effect", since the conditional variance is a function only of magnitude of the lagged residuals and not their signs.

In the exponential GARCH EGARCH model of Nelson , the conditional variance [[sigma]. More formally, the conditional variance equation is written as ln [[sigma]. In the above equation, the value of g [z. Nelson notes that, "to accommodate the symmetric relation between stock returns and volatility changes the value of g [z. That is why he suggested to express the function of g x as g [z. The model has some advantages over the pure GARCH specification.

First, since log [[sigma]. There is thus no need to artificially impose non-negativity constraints on the model parameters. Second, asymmetries are allowed under the EGARCH formulation, since if the relationship between volatility and return is negative, [[theta]. In the analysis, 12th June will be threshold point that separates pre- and post-stock index futures trading in order for robust inferences to be made. The data set comprises daily closing observations of the spot index rates for the aforementioned market.

It covers the periods 1st June to 26th September for nifty index. The particular period and the set of data for the empirical investigation are chosen in order to give emphasis on the introduction of future how it affect the stock market volatility. The stock index prices are obtained from National Stock Exchange. All prices are transformed to natural logarithms. The descriptive statistics of logarithmic first-differences of the daily nifty and nifty junior spot index prices are reported in Table i and Table 2 respectively, which is divided into two periods.

In Table 2 panel A and B correspond to pre and post futures period of Nifty Junior. The results indicate excess skewness and kurtosis in the price series, departure from normality for the spot prices, while all the variable in the first difference stationary, all having a unit root on their log-levels presentation.

The figures obtained for the standard deviation estimates, provide an initial view of volatility, where by comparing two periods pre- and post-derivatives it seems that volatility has decreased for nifty index and it remains same for nifty junior over time.

Table 2, reports the Ljung-Box statistics, where the results indicate significant linear and non-linear temporal dependencies in the adjusted residual series respectively.

EMPIRICAL RESULTS To assess whether there has been a change in volatility after the inception of futures trading, a GARCH 1,1 and GJR-GARCH 1,1 model of conditional volatility is estimated.

GARCH 1,1 refers to the first order ARCH term and first order GARCH term in the conditional variance equation. It relates to the impact of yesterday's news on today's price changes. In contrast [beta] GARCH reflects the impact of "old news" on price changes. It indicates the level of persistence in information and it effects on volatility. A high [beta] shows persistence of volatility due to old news. The log likelihood value is high We did the ARCH LM test on residual to see if there was any ARCH effect left.

It shows there is no significant ARCH effect in the residual series. We studied the impact of introduction of index Futures on the stock market by restricting the period of study to a year before and after introduction of Futures. During this time no other derivative contracts like index options, futures and option on individual shares etc were traded in the Indian market.

Since this period is restricted to one year before and after futures, we can get a better insight onto the impact of other stock market reforms. The ARCH 1 0. This shows that the market has a long memory and that impact of a shock persists for a long time to come. The dummy variable for the future The value is significant at conventional levels see Table 4.

The diagnostic tests, on the standardized residuals and squared residuals, indicate absence of linear and non-linear dependencies, respectively. Thus the estimated model fits the data very well. The standard diagnostic tests of the residuals from the model confirm the absence of any further ARCH effects.

The results of the coefficients of the lagged variance [[alpha]. This means that news about the volatility for the previous period does not have much importance.

The lagged error term, [?? Hence a higher value in the post futures period implies that recent news have a greater impact on price changes. The results, from Table 5, indicate that this holds, suggesting that information is being impounded in prices more quickly due to the introduction of futures trading. The results of the asymmetric coefficient [gamma] suggest that there is a statistically significant and positive asymmetric effect, which implies that negative shocks elicit a larger response than positive shocks of an equal magnitude.

The impacts of futures trading for the post futures period can be captured by the asymmetric coefficient [gamma] suggest that there is a statistically significant and positive asymmetric effect.

Thus the introduction of futures trading have impact on the asymmetric coefficient. The issue of the impact of futures trading on the spot market volatility is further investigated by estimating the EGARCH model, The results are reported in Table 6. The results of the impact of futures introduction on asymmetric market responses may be assessed via consideration of the asymmetric coefficient [gamma] that captures the nature of bias in the pre and post future period.

Whether the asymmetric coefficient behaves the same way as it was in the GJR-GARCH model.

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The results indicate that the pre-futures asymmetric coefficient is statistically insignificant whereas the post futures asymmetric coefficient is negative and statistically insignificant. This shows that during the post futures period the negative shocks elicit a larger response than positive shocks of an equal magnitude. In the analysis, 12th June will be threshold point that separates pre-and post-stock index futures trading in order for robust inferences to be made. The results suggest that there is the decrease in volatility in the case of nifty where as there is the increase of volatility in the case of nifty junior after the introduction of futures in the derivative market.

A desirable impact on the asymmetry of volatility. This implies that negative shocks elicit a larger response than positive shocks of an equal magnitude. Certainly, other economic variables could be added and checked, leading possibly to stronger results.

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Priestlet , "The Effects of Stock Index Futures Trading on Stock, Index Volatility: An Analysis of the Asymmetric Response of Volatility to News", The Journal of Futures Markets, 18 2 , pp.

Inflation", Econometrica, 50, Ng , "Measuring and Testing the Impact of News on Volatility", Journal of Finance, 43, Fabio Fornari and A. Mele , "Sign and Volatility-switching ARCH Models: Theory and Applications to International Stock Markets", Journal of Applied Econometrics, 12, An New Approach", Econometrica, 59, Pre futures Panel B: Post futures Mean All series are measured in logarithmic first differences.

Figures in the parenthesis. Skew and kurt are the estimated centralized third and fourth moments of the data. J-B is the Jarque-Bera for the normality. ADF is the Augmented Dicket Fuller test. Level and difference corresponding to price series in log levels and log first-differences, respectively.

Q L and [Q. Q statistics on the first L lags of the sample autocorrelation function of the series and of the squared series; these series are distributed as chi- squared The order of the most parsimonious autoregressive models are in squared brackets. Table 3 Estimates of GARCH 1,1 Model for twelve years on Nifty to Variables Description Coefficient Z-statistics [[alpha]. Coefficients Estimates, Nifty-Alt 1 Mean Equation: Residual Diagnostic Skewness 0. Residual Diagnostic Skewness J-B is the Jarque-Bera normality test.

Q 24 and Q2 24 are the Ljung-Box tests for 24th, order serial correlation and Heteroskedasticity. ARCH 4 is the Engel's F- test. See notes in Table 5. Modeling sectoral and welfare impacts of information technology spillover: Analyzing service quality in the hospitality industry: