Modeling Stochastic Volatility with Application to Stock Returns

June 1, 2003

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Format: Chicago

Noureddine Krichene. Modeling Stochastic Volatility with Application to Stock Returns, (USA: International Monetary Fund, 2003) accessed November 8, 2024

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.

Subject: Asset prices, Bayesian models, Monetary operations, Stock markets, Vector autoregression

Keywords: Mean reversion, Second moment, WP

Publication Details

Pages:

27
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2003/125
Stock No:

WPIEA1252003
ISBN:

9781451854848
ISSN:

1018-5941

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