Long Memory in Stock Market Volatility:Evidence from India
AbstractLong 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 48519.
Date of creation: 2010
Date of revision:
Publication status: Published in Artha Vijnana 4.52(2010): pp. 332-345
Fractional integration; Long memory; Volatility; FIGARCH; hyperbolic decay; Indian Stock Market; NSE; BSE.;
Find related papers by JEL classification:
- G0 - Financial Economics - - General
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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