# Numerical integration-based Gaussian mixture filters for maximum likelihood estimation of asymmetric stochastic volatility models

*Hiroyuki Kawakatsu*

*Econometrics Journal*, 2007, vol. 10, issue 2, pages 342-358

**Abstract:**
I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state--space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi-maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model. Copyright Royal Economic Society 2007

**Date:** 2007

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**Persistent link:** http://EconPapers.repec.org/RePEc:ect:emjrnl:v:10:y:2007:i:2:p:342-358

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