## 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

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### 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.

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_Source: https://www.imf.org/en/publications/wp/issues/2016/12/30/modeling-stochastic-volatility-with-application-to-stock-returns-16599_
