Modeling Stochastic Volatility with Application to Stock Returns
IMF Working Papers, June 1, 2003
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Bibliographic details
- Authors: Noureddine Krichene
- Published: June 1, 2003
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781451854848.001
Model and Estimation
- A stochastic volatility model was specified where volatility was driven solely by a latent variable called "news".
- The model was estimated for three stock indices.
- A Markov chain Monte Carlo algorithm was used for:
- Estimating Bayesian parameters.
- Filtering volatilities.
Key Findings
- Volatility persistence being close to one was consistent with both volatility clustering and mean reversion.
- Filtering results indicated 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.
Policy and Market Implications
- Potential value for market participants in asset pricing and risk management.
- Potential value for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Content in this bundle
- Modeling Stochastic Volatility with Application to Stck Returns - WP/03/125