Modeling Stochastic Volatility with Application to Stock Returns /
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 consiste...
| Hlavní autor: | |
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| Médium: | Časopis |
| Jazyk: | English |
| Vydáno: |
Washington, D.C. :
International Monetary Fund,
2003.
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| Edice: | IMF Working Papers; Working Paper ;
No. 2003/125 |
| On-line přístup: | Full text available on IMF |
| Shrnutí: | 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. |
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| Popis jednotky: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Fyzický popis: | 1 online resource (27 pages) |
| Médium: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Přístup: | Electronic access restricted to authorized BRAC University faculty, staff and students |