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...
| מחבר ראשי: | |
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| פורמט: | כתב-עת |
| שפה: | English |
| יצא לאור: |
Washington, D.C. :
International Monetary Fund,
2003.
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| סדרה: | IMF Working Papers; Working Paper ;
No. 2003/125 |
| גישה מקוונת: | Full text available on IMF |
| סיכום: | 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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| תאור פריט: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| תיאור פיזי: | 1 online resource (27 pages) |
| פורמט: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| גישה: | Electronic access restricted to authorized BRAC University faculty, staff and students |