Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/192596 
Year of Publication: 
2010
Series/Report no.: 
Discussion Papers No. 614
Publisher: 
Statistics Norway, Research Department, Oslo
Abstract: 
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.
Subjects: 
multivariate stochastic volatility
exchange rates
Ornstein-Uhlenbeck processes
quasi-likelihood
factor models
state space representation
JEL: 
C13
C22
C51
G10
Document Type: 
Working Paper

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