Stock Market Volatility: Roots and Results

Roy, Malay Kanti; Karmakar, Madhusudan

doi:10.1177/0256090919950104

Cited by 16 publications

(9 citation statements)

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“…From both the figures it appears that the year 1992 has the highest volatility in the period under study. In fact, this period experienced the highest volatility in the history of the Indian stock market (Roy and Karmakar, 1995) and this coincided with the initial years of liberalization of the Indian economy after a long era of control. What are the possible sources for the time-varying volatility?…”

Section: Volatility Shiftingmentioning

confidence: 98%

Modeling Conditional Volatility of the Indian Stock Markets

Karmakar¹

2005

Vikalpa: The Journal for Decision Makers

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Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroskedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-ofsample forecast accuracy. The paper also investigates whether there is any leverage effect in Indian companies. The estimation of volatility is made at the macro level on two major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is then evaluated in terms of its forecasting accuracy on these two indices. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroskedastic behaviour of the Indian stock market at the micro level. The vanilla GARCH (1, 1) model has been fitted to both the market indices. We find: a strong evidence of time-varying volatility a tendency of the periods of high and low volatility to cluster a high persistence and predictability of volatility. Conditional volatility of market return series from January 1991 to June 2003 shows a clear evidence of volatility shifting over the period where violent changes in share prices cluster around the boom of 1992. Though the higher price movement started in response to strong economic fundamentals, the real cause for abrupt movement appears to be the imperfection of the market. The forecasting ability of the fitted GARCH (1, 1) model has been evaluated by estimating parameters initially over trading days of the in-sample period and then using the estimated parameters to later data, thus forming out-of-sample forecasts on two market indices. These out-of-sample volatility forecasts have been compared to true realized volatility. Three alternative methods have been followed to measure three pairs of forecast and realized volatility. In each method, the volatility forecasts are evaluated and compared through popular measures. To examine the information content of forecasts, a regression-based efficiency test has also been performed. It is observed that the GARCH (1, 1) model provides reasonably good forecasts of market volatility. While turning to 50 individual underlying shares, it is observed that the GARCH (1, 1) model has been fitted for almost all companies. Only for four companies, GARCH models of higher order may be more successful. In general, volatility seems to be of a persistent nature. Only eight out of 50 shares show significant leverage effects and really need an asymmetric GARCH model such as EGARCH to capture their volatility clustering which is left for future research. The implications of the study are as follows: The various GARCH models provide good forecasts of volatility and are useful for portfolio allocation, performance measurement, option valuation, etc. Given the anticipated high growth of the economy and increasing interest of foreign investors towards the country, it is important to understand the pattern of stock market volatility in India which is time-varying, persistent, and predictable. This may help diversify international portfolios and formulate hedging strategies.

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Section: Volatility Shiftingmentioning

confidence: 98%

Modeling Conditional Volatility of the Indian Stock Markets

Karmakar¹

2005

Vikalpa: The Journal for Decision Makers

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“…There is relatively less empirical research on stock return volatility in the emerging markets. In the Indian context, Roy and Karmakar (1995) focused on the measurement of the average level of volatility as the sample standard deviation and examined whether volatility has increased in the early 1990s; Goyal (1995) used conditional volatility estimates as suggested by Schwert (1989) to study the nature and trend of stock return volatility and the impact of carry forward system on the level of volatility; Reddy (1997-98) analysed the effects of market microstructure, e.g., establishment of the National Stock Exchange (NSE) and the introduction of Bombay Stock Exchange Online Trading (BOLT) system on the stock return volatility measured as the sample standard deviation of the closing prices; Kaur (2002) analysed the extent and pattern of stock return volatility during 1990-2000 and examined the effect of company size, day-ofthe-week, and FII investments on volatility measured as the sample standard deviation.…”

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confidence: 99%

Time Varying Volatility in the Indian Stock Market

Kaur

2004

SSRN Journal

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“…Many researchers have explored this attention-grabbing field and have tried to develop models to capture the volatility at different periods of time to estimate future volatility of stock markets for developed markets as well as emerging economies. In the Indian context, Roy and Karmakar (1995) investigated the heteroskedastic behaviour of the Indian stock market using 'vanilla' GARCH (1, 1) model for a period of about 24 years from January 1980 to June 2003. The study reports that volatility was highest for the year 1992 particularly because of strong economic fundamentals and imperfections in the market.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Impact of Global Financial Crisis on the Indian Stock Market through GARCH Models

Mathur

Chotia

Rao

2016

Asia-Pacific Journal of Management Research and Innovation

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The purpose of this article is to examine the impact of Global Financial Crisis on the Indian stock market which has been an issue of immense interest to time series analysts and econometricians around the world. We have conducted empirical analysis on daily stock returns of the top 20 companies listed on Bombay Stock Exchange (BSE) for the period 2001-2012. The study shows the presence of autoregressive conditional heteroskedasticity (ARCH) effect and volatility clustering during the study period. The BSE Sensex also depicts asymmetric volatility effect. Therefore, the standard generalised autoregressive conditional heteroskedasticity (GARCH) (0, 1) model provides the best description of return dynamics. Application of GARCH to daily stock returns of individual companies suggests that the companies illustrate high volatility for the period 2007-2009. Also, the returns from the portfolio of 20 companies reveal high volatility for the period 2007-2009. Hence, we prove that the Indian economy too has felt the impact of the global financial crisis. Finally, we conclude with some challenges for future research in this area.

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Stock Market Volatility: Roots and Results

Cited by 16 publications

References 0 publications

Modeling Conditional Volatility of the Indian Stock Markets

Modeling Conditional Volatility of the Indian Stock Markets

Time Varying Volatility in the Indian Stock Market

Modelling the Impact of Global Financial Crisis on the Indian Stock Market through GARCH Models

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