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Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search

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  • Loddo, Antonello
  • Ni, Shawn
  • Sun, Dongchu
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Abstract

We propose a Bayesian stochastic search approach to selecting restrictions on multivariate regression models where the errors exhibit deterministic or stochastic conditional volatilities. We develop a Markov chain Monte Carlo (MCMC) algorithm that generates posterior restrictions on the regression coefficients and Cholesky decompositions of the covariance matrix of the errors. Numerical simulations with artificially generated data show that the proposed method is effective in selecting the data-generating model restrictions and improving the forecasting performance of the model. Applying the method to daily foreign exchange rate data, we conduct stochastic search on a VAR model with stochastic conditional volatilities.

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File URL: http://pubs.amstat.org/doi/abs/10.1198/jbes.2010.08197
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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 3 ()
Pages: 342-355

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Handle: RePEc:bes:jnlbes:v:29:i:3:y:2011:p:342-355

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Cited by:
  1. Roberto Casarin & Marco Tronzano & Domenico Sartore, 2013. "Bayesian Markov Switching Stochastic Correlation Models," Working Papers 2013:11, Department of Economics, University of Venice "Ca' Foscari".

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