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Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension

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  • João Pedro Coli de Souza Monteneri Nacinben

    (Department of Economics, FEARP-University of São Paulo, Ribeirão Preto 14040-905, Brazil
    These authors contributed equally to this work.)

  • Márcio Laurini

    (Department of Economics, FEARP-University of São Paulo, Ribeirão Preto 14040-905, Brazil
    These authors contributed equally to this work.)

Abstract

This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related to chain convergence can also arise. In light of these challenges, this research aims to establish a computationally efficient approach for estimating multivariate stochastic volatility models. We propose a multifactor formulation estimated using the INLA methodology, enabling an approach that leverages sparse linear algebra and parallelization techniques. To evaluate the effectiveness of our proposed model, we conduct in-sample and out-of-sample empirical analyses of stock market index return series. Furthermore, we provide a comparative analysis with models estimated using MCMC, demonstrating the computational efficiency and goodness of fit improvements achieved with our approach.

Suggested Citation

  • João Pedro Coli de Souza Monteneri Nacinben & Márcio Laurini, 2024. "Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension," Econometrics, MDPI, vol. 12(1), pages 1-28, February.
    • Handle: RePEc:gam:jecnmx:v:12:y:2024:i:1:p:5-:d:1341433
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    References listed on IDEAS

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