Long Memory in Stock Market Volatility: Indian Evidences
Artha Vijnana, Vol. 52, No. 4, pp. 332-345, December, 2010
14 Pages Posted: 22 Jun 2011
Date Written: December 31, 2010
Abstract
Long memory in variance or volatility refers to a slow hyperbolic decay in auto-correlation functions of the squared or log-squared returns. GARCH models extensively used in empirical analysis do not account for long memory in volatility. The present paper examines the issue of long memory in volatility in the context of Indian stock market using the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model. For the purpose, daily values of 38 indices from both National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) are used. The results of the study confirm presence of long memory in volatility of all the index returns. This shows that FIGARCH model better describes the persistence in volatility than the conventional ARCH-GARCH models.
Keywords: Long memory, fractional integration, Volatility, Variance, ARCH-GARCH, FIGARCH, Indian Stock Market, BSE, NSE, Long memory, fractional integration, Volatility, hyperbolic decay, ARCH-GARCH, FIGARCH, Indian Stock Market, BSE, NSE
JEL Classification: G14, C 14, C58
Suggested Citation: Suggested Citation