A Multivariate Stochastic Volatility Model
نویسندگان
چکیده
Anastasios Plataniotis and Petros Dellaportas [email protected] [email protected] Department of Statistics, Athens University of Economics and Business, Greece Summary: We introduce a broad class of multivariate stochastic volatility models where transformed eigenvalues and Givens rotation angles are assumed to be AR(1) processes. This decomposition retains the required positive definite structure of a fully time-varying covariance matrix. We provide detailed guidelines for a nested Laplace estimation procedure that exploits an efficient numerical maximization strategy. The methodology is implemented for a real financial dataset with 100 stocks and its forecasting power is compared to other existing multivariate volatility models. There is evidence that our models are superior in terms of both fitting and predicting to other specifications that model time-varying covariance matrices. Some key words: Givens angles, MCMC, Nested Laplace Approximation, Spectral decomposition
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