Arbeitspapier
Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting
This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
- Language
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Englisch
- Bibliographic citation
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Series: Tinbergen Institute Discussion Paper ; No. TI 2023-077/III
- Classification
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Wirtschaft
- Subject
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Filtering
Forecasting
Indirect Inference
Extremum Monte Carlo
Leverage
Bitcoin
- Event
-
Geistige Schöpfung
- (who)
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Blasques, Francisco
Koopman, Siem Jan
Moussa, Karim
- Event
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Veröffentlichung
- (who)
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Tinbergen Institute
- (where)
-
Amsterdam and Rotterdam
- (when)
-
2023
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
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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
- Blasques, Francisco
- Koopman, Siem Jan
- Moussa, Karim
- Tinbergen Institute
Time of origin
- 2023
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