Arbeitspapier
Multivariate stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes: A quasi-likelihood approach
This paper extends the ordinary quasi-likelihood estimator for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck (OU) processes to vector processes. Despite the fact that multivariate modeling of asset returns is essential for portfolio optimization and risk management -- major areas of financial analysis -- the literature on multivariate modeling of asset prices in continuous time is sparse, both with regard to theoretical and applied results. This paper uses non-Gaussian OU-processes as building blocks for multivariate models for high frequency financial data. The OU framework allows exact discrete time transition equations that can be represented on a linear state space form. We show that a computationally feasible quasi-likelihood function can be constructed by means of the Kalman filter also in the case of high-dimensional vector processes. The framework is applied to Euro/NOK and US Dollar/NOK exchange rate data for the period 2.1.1989-4.2.2010.
- Language
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Englisch
- Bibliographic citation
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Series: Discussion Papers ; No. 614
- Classification
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Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
General Financial Markets: General (includes Measurement and Data)
- Subject
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multivariate stochastic volatility
exchange rates
Ornstein-Uhlenbeck processes
quasi-likelihood
factor models
state space representation
- Event
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Geistige Schöpfung
- (who)
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Raknerud, Arvid
Skare, Øivind
- Event
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Veröffentlichung
- (who)
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Statistics Norway, Research Department
- (where)
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Oslo
- (when)
-
2010
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Raknerud, Arvid
- Skare, Øivind
- Statistics Norway, Research Department
Time of origin
- 2010
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