P. Srinivasan1, K. Srinivasan2 and Malabika Deo3

1Department of Economics (SOM),Pondicherry Central University, Kalapet,
Puducherry - 605 014, India.
2&3Department of Commerce (SOM), Pondicherry Central University, Kalapet,
Puducherry - 605 014, India
2E-mail: ksrinivasan1979@gmail.com, , 3E-mail: malavika_deo@yahoo.co.in,

Abstract

The paper investigates the impact of underlying spot market volatility after the introduction of futures and options trading in India by using standard EGARCH (1,1) model.  The dataset was retrieved from National Stock Exchange (NSE) website for daily closing price series of S&P CNX Nifty spot index for the period from 1st January 1996 to 31st March 2009. The findings suggest that, both futures and options trading reduce the volatility of spot market after the introduction of futures and options trading in India.  Besides, the results of futures and options markets reveal that there were no asymmetric effects present in Indian spot market. This finding are quite interesting, since noise trading is the cause of asymmetric responses, the Nifty spot index was not significantly affected by such market participants.  Hence, the present study suggests that the introduction of futures and options trading have improved the speed and quality of information flowing in spot market.  This enhances the overall market depth, increases market liquidity and ultimately reduces informational asymmetries and therefore compresses spot market volatility in India.    

Keywords: S&P CNX Nifty1, Volatility2, Asymmetric Effect3, EGARCH Model4.


1 The most widely used indicator of the Indian stock market is S&P CNX Nifty (Nifty) which is a well diversified index of National Stock Exchange (NSE) comprising 50 highly liquid stocks represent around 23 sectors of the economy and it accounts for 60% of the total market capitalization of NSE.

2 In general terms, volatility may be described as a phenomenon, which characterizes changeableness of a variable under consideration. Volatility is associated with unpredictability and uncertainty. In literature on stock market, the term is synonymous with risk, and hence high volatility is thought of as a symptom of market disruption whereby securities are not being priced fairly and the capital market not functioning as well as it should be. As a concept volatility is simple and intuitive. It measures the variability or dispersion about a central tendency.

3 Statistically, the asymmetric effect implies an unexpected drop in stock price due to bad news increases volatility more than an unexpected increase in price due to good news of similar magnitude.

4 In order to capture the asymmetric response of volatility to news or ‘leverage effect’, Nelson (1991) proposed Exponential GARCH model which allows the conditional volatility to have asymmetric relation with past data.  This model expresses the conditional variance of a given variable as a non-linear function of its own past values of standardized innovations that can react asymmetrically to good and bad news (Drimbetas, Sariannidis and Porfiris, 2007).

 

 

 

1 The most widely used indicator of the Indian stock market is S&P CNX Nifty (Nifty) which is a well diversified index of National Stock Exchange (NSE) comprising 50 highly liquid stocks represent around 23 sectors of the economy and it accounts for 60% of the total market capitalization of NSE.
2 In general terms, volatility may be described as a phenomenon, which characterizes changeableness of a variable under consideration. Volatility is associated with unpredictability and uncertainty. In literature on stock market, the term is synonymous with risk, and hence high volatility is thought of as a symptom of market disruption whereby securities are not being priced fairly and the capital market not functioning as well as it should be. As a concept volatility is simple and intuitive. It measures the variability or dispersion about a central tendency.
3 Statistically, the asymmetric effect implies an unexpected drop in stock price due to bad news increases volatility more than an unexpected increase in price due to good news of similar magnitude.
4 In order to capture the asymmetric response of volatility to news or ‘leverage effect’, Nelson (1991) proposed Exponential GARCH model which allows the conditional volatility to have asymmetric relation with past data.  This model expresses the conditional variance of a given variable as a non-linear function of its own past values of standardized innovations that can react asymmetrically to good and bad news (Drimbetas, Sariannidis and Porfiris, 2007).