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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
International Monetary Fund
2003
|
| Schriftenreihe: | IMF Working Papers
Working Paper No. 03/125 |
| Online-Zugang: | UBW01 UEI01 LCO01 SBR01 UER01 SBG01 UBG01 FAN01 UBT01 FKE01 UBY01 UBA01 FLA01 UBM01 UPA01 UBR01 FHA01 FNU01 BSB01 TUM01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (27 p) |
| ISBN: | 1451854846 9781451854848 |
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| spelling | Krichene, Noureddine Verfasser aut Modeling Stochastic Volatility with Application to Stock Returns Krichene, Noureddine Washington, D.C International Monetary Fund 2003 1 Online-Ressource (27 p) txt rdacontent c rdamedia cr rdacarrier IMF Working Papers Working Paper No. 03/125 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 Online-Ausg http://elibrary.imf.org/view/IMF001/04647-9781451854848/04647-9781451854848/04647-9781451854848.xml Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Krichene, Noureddine Modeling Stochastic Volatility with Application to Stock Returns |
| title | Modeling Stochastic Volatility with Application to Stock Returns |
| title_auth | Modeling Stochastic Volatility with Application to Stock Returns |
| title_exact_search | Modeling Stochastic Volatility with Application to Stock Returns |
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| title_full | Modeling Stochastic Volatility with Application to Stock Returns Krichene, Noureddine |
| title_fullStr | Modeling Stochastic Volatility with Application to Stock Returns Krichene, Noureddine |
| title_full_unstemmed | Modeling Stochastic Volatility with Application to Stock Returns Krichene, Noureddine |
| title_short | Modeling Stochastic Volatility with Application to Stock Returns |
| title_sort | modeling stochastic volatility with application to stock returns |
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