Long Memory in Stock Market Volatility:Evidence from India

Gourishankar Hiremath and Kamaiah Bandi

MPRA Paper from University Library of Munich, Germany

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: Fractional integration; Long memory; Volatility; FIGARCH; hyperbolic decay; Indian Stock Market; NSE; BSE. (search for similar items in EconPapers)
JEL-codes: G0 G12 G14 G17 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (6)

Published in Artha Vijnana 4.52(2010): pp. 332-345

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