Characteristics of Conditional Volatility of BSE SENSEX: The Impact of Information Asymmetry and Leverage

Stock market volatility has attracted growing attentions from scholars, policy makers and practitioners due to its impact on decision making. Present study in this context tries to explore the features of daily returns of BSE SENSEX and the conditional volatility of the same. Highly significant large JB-statistic confirms that the return series is not normally distributed. The return series is leptokurtic and returns are serially correlated. It appears that volatility clustering is present during the study period. Furthermore, GARCH (1, 1) model has been applied to compute conditional variance of the sample data. The empirical results show that above model is a good fit and it has also been established that volatility in this market persists over a long period of time. A large sum of coefficients implies that a large positive or a large negative return leads future forecasts of the variance to be high for a prolonged period. To capture the leverage effect present study has applied TARCH(1, 1) model. From the empirical result it appears that news asymmetry and leverage effect are present in the market. (ProQuest: . denotes formulae omitted.)INTRODUCTIONDispersion of returns of an asset from its mean return is called volatility. Volatility receives a great deal of concern because it can be used as a surrogate to risk. A rise in the volatility could be interpreted as a rise in risk of the concerned investment and investors may transfer funds to less risky assets given an assured return. This move could result in rise in cost of capital of firms (Arestis et al, 2001)). The study conducted by Bekaert (1995) reported that in segmented capital markets volatility is a critical input in the cost of capital.According to Poon and Granger (2003) volatility has a very wide sphere of influence including investment, security valuation, risk management and policy making. They also put emphasis on the importance of volatility forecasting in various things such as options pricing, financial risk management, etc.Moreover, market volatility may also affect consumer spending. According to Garner (1988) stock market crash in 1987 reduced consumer spending in the USA. Furthermore, Gertler and Hubbard (1989) revealed that business investment spending is also influenced by stock return volatility.While there is a general consensus on the definition of stock market volatility, but lesser agreement on how to measure it. There are various models and estimators in the literature for measuring volatility of stock returns. Models and estimators that assume volatility to be constant are called "unconditional volatility". Since the volatility of stock market investments can be defined as the dispersion of investment returns below and above the mean, variance or standard deviation of the return series has been the traditional measure of volatility. The formula of computing standard deviation of daily returns on stock market index is:. where, r = In (I/IM)I1 is the closing value of the stock market index at time t,In is natural logarithmFrom I that is sample standard deviation of time t forecast value Ox is estimated by applying different models, such as:Random Walk:. Historical Average:. When s is used to measure volatility, the users assume that the return series is normally distributed. However, standard deviation, s, is not the correct dispersion measure except for the normal distribution. If returns are independent and identically distributed (iid), then of returns over a long horizon can be derived as a simple multiple of single period s. But, this is clearly not the case for many financial time series because of the characteristics like fat tail distribution, volatility clustering, etc. (Poon and Granger, 2003) . So, models capturing the above mentioned features should be used to measure volatility. Following Poon and Granger (2003), it can be said that the time series models used by the researchers are not based on theoretical foundations but are selected to capture the main features of volatility found with actual returns. …