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Long Memory in Stock Market Volatility

In: Indian Stock Market

Author

Listed:
  • Gourishankar S. Hiremath

    (Department of Humanities and Social Science, IIT Kharagpur)

Abstract

Long memory in variance or volatility refers to a slow hyperbolic decay in autocorrelation functions of the squared or log-squared returns. The conventional volatility models extensively used in empirical analysis do not account for long memory in volatility. This chapter revisits the Indian stock market by using the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model. For empirical modeling, daily values of 14 indices from the National stock exchange (NSE) and Bombay Stock Exchange (BSE) from June 1997 to March 2010 are used. The results of the study confirm the presence of long memory in volatility of index returns. This shows that FIGARCH model better describes the persistence in volatility than the conventional GARCH models. Against the evidence of fractional behavior of volatility in Indian stock market, it is essential to factor the long memory in derivative pricing and value at risk models.

Suggested Citation

  • Gourishankar S. Hiremath, 2014. "Long Memory in Stock Market Volatility," SpringerBriefs in Economics, in: Indian Stock Market, edition 127, chapter 0, pages 99-110, Springer.
    • Handle: RePEc:spr:spbchp:978-81-322-1590-5_6
    DOI: 10.1007/978-81-322-1590-5_6
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