Automated Likelihood Based Inference for Stochastic Volatility Models

Skaug Hans J. () and Jun Yu
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Skaug Hans J.: Department of Mathematics, University of Bergen

No 15-2009, Working Papers from Singapore Management University, School of Economics

Abstract: In this paper the Laplace approximation is used to perform classical and Bayesian analyses of univariate and multivariate stochastic volatility (SV) models. We show that implementation of the Laplace approximation is greatly simplified by the use of a numerical technique known as automatic differentiation (AD). Several algorithms are proposed and compared withsome existing methods using both simulated data and actual data in terms of computational,statistical and simulation efficiency. It is found that the new methods match the statistical efficiency of the existing classical methods and substantially reduce the simulation inefficiency in some existing Bayesian Markov chain Monte Carlo (MCMC) algorithms. Also proposed are simple methods for obtaining the filtered, smoothed and forecasted latent variable. The new methods are implemented using the software AD Model Builder, which with its latent variable module (ADMB-RE) facilitates the formulation and fitting of SV models. To illustrate the flexibility of the new algorithms, several univariate and multivariate SV models are fitted using exchange rate data.

Keywords: Laplace approximation; Automatic differentiation; Simulated maximum likelihood; Importance sampling; Bayesian MCMC. (search for similar items in EconPapers)
JEL-codes: C13 C22 E43 G13 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2009-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in SMU Economics and Statistics Working Paper Series

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Related works:
Working Paper: Automated Likelihood Based Inference for Stochastic Volatility Models (2007)
Working Paper: Automated Likelihood Based Inference for Stochastic Volatility Models (2007)
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