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Activity Number: 385
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #305544
Title: Multivariate Stochastic Volatility Models Based on Non-Gaussian Ornstein-Uhlenbeck Processes: A Quasi-Likelihood Approach
Author(s): Arvid Raknerud*+ and Øivind Skare
Companies: and University of Oslo
Address: Ugleveien 3, 0853 Oslo, _, , Norway
Keywords: multivariate stochastic volatility ; exchange rates ; Ornstein-Uhlenbeck processes ; quasi-likelihood ; factor models ; state space representation

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.

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