Arbeitspapier
Discrete-time stochastic volatility models and MCMC-based statistical inference
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.
- Language
-
Englisch
- Bibliographic citation
-
Series: SFB 649 Discussion Paper ; No. 2008,063
- Classification
-
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Asset Pricing; Trading Volume; Bond Interest Rates
- Subject
-
Stochastic volatility
Markov chain Monte Carlo
Metropolis-Hastings algorithm Jump Processes
Kapitalertrag
Volatilität
Stochastischer Prozess
Markovscher Prozess
Monte-Carlo-Methode
Bayes-Statistik
Theorie
Schätzung
Aktienindex
Wechselkurs
Deutschland
USA
- Event
-
Geistige Schöpfung
- (who)
-
Hautsch, Nikolaus
Ou, Yangguoyi
- Event
-
Veröffentlichung
- (who)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (where)
-
Berlin
- (when)
-
2008
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Hautsch, Nikolaus
- Ou, Yangguoyi
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Time of origin
- 2008
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