The Impact of Futures Contracts On Risk and Returns of the VN30 Index in Vietnam

The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange

Abstract: This study investigated the impact of the introduction of the VN30-Index futures contract on the daily returns anomaly for the Ho Chi Minh Stock Exchange (HOSE). Daily returns of the VN30-Index for the period 6 February 2012 through 31 December 2019 are used in this study to ascertain the new VN30-Index futures contract influence on the day-of-the-week anomaly observed in the HOSE. To test this effect, ordinary least square (OLS), generalized autoregressive conditional heteroskedasticity [GARCH (1,1)] and exponential generalized autoregressive conditional heteroskedasticity [EGARCH (1,1)] regression models were employed. The empirical results obtained from the models support the presence of the day-of-the-week effect for the HOSE during the study period. Specifically, a negative effect was observed for Monday. However, the analysis revealed that the day-of-the-week effect was only present in stock returns for the pre-index futures period, not for the post-index futures period. These findings suggest that the introduction of the VN30-Index futures contract had a significant impact on the daily returns anomaly in Vietnam’s HOSE, providing evidence that the introduction of the index futures contract facilitated market efficiency.

Conditional heteroskedasticity in asset returns: a new approach

Abstract: This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.

GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics

Abstract: The great workhorse of applied econometrics is the least squares model. This is a natural choice, because applied econometricians are typically called upon to determine how much one variable will change in response to a change in some other variable. Increasingly however, econometricians are being asked to forecast and analyze the size of the errors of the model. In this case, the questions are about volatility, and the standard tools have become the ARCH/ GARCH models. The basic version of the least squares model assumes that the expected value of all error terms, when squared, is the same at any given point. This assumption is called homoskedasticity, and it is this assumption that is the focus of ARCH/ GARCH models. Data in which the variances of the error terms are not equal, in which the error terms may reasonably be expected to be larger for some points or ranges of the data than for others, are said to suffer from heteroskedasticity. The standard warning is that in the presence of heteroskedasticity, the regression coefficients for an ordinary least squares regression are still unbiased, but the standard errors and confidence intervals estimated by conventional procedures will be too narrow, giving a false sense of precision. Instead of considering this as a problem to be corrected, ARCH and GARCH models treat heteroskedasticity as a variance to be modeled. As a result, not only are the deficiencies of least squares corrected, but a prediction is computed for the variance of each error term. This prediction turns out often to be of interest, particularly in applications in finance. The warnings about heteroskedasticity have usually been applied only to cross-section models, not to time series models. For example, if one looked at the

The impact of derivatives on stock market volatility: a study of the nifty index

Abstract: This paper studies the volatility implications of the introduction of derivatives on stock market volatility in India using the S&P CNX Nifty Index as a benchmark. To account for non-constant error variance in the return series, a GARCH model is fitted by incorporating futures and options dummy variables in the conditional variance equation. We find clustering and persistence of volatility before and after derivatives, while listing seems to have no stabilisation or destabilisation effects on market volatility. The postderivatives period shows that the sensitivity of the index returns to market returns and any day-of-the-week effects have disappeared. That is, the nature of the volatility patterns has altered during the post-derivatives period.