Modeling Stochastic Volatility with Application to Stock Returns

Author/Editor:

Noureddine Krichene

Publication Date:

June 1, 2003

Electronic Access:

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Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary:

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.

Series:

Working Paper No. 2003/125

Subject:

Asset prices Bayesian models Monetary operations Stock markets Vector autoregression

English

Publication Date:

June 1, 2003

ISBN/ISSN:

9781451854848/1018-5941

Stock No:

WPIEA1252003

Pages:

27

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