Footnote:
In: Artha Vijnana, Vol. 52, No. 4, pp. 332-345, December, 2010
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 31, 2010 erstellt
Description:
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