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Modeling Stochastic Volatility with Application to Stock Returns

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  • Mr. Noureddine Krichene

Abstract

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.

Suggested Citation

  • Mr. Noureddine Krichene, 2003. "Modeling Stochastic Volatility with Application to Stock Returns," IMF Working Papers 2003/125, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2003/125
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    Cited by:

    1. M. Berument & Yeliz Yalcin & Julide Yildirim, 2011. "The inflation and inflation uncertainty relationship for Turkey: a dynamic framework," Empirical Economics, Springer, vol. 41(2), pages 293-309, October.
    2. Bednarik, Radek, 2008. "Analýza volatility devizových kurzů vybraných ekonomik [The Analysis of Volatility of Selected Countries' Exchange Rates]," MPRA Paper 15046, University Library of Munich, Germany.

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    WP; mean reversion; second moment;
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