Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

Natalia Khorunzhina and Jean-Francois Richard

MPRA Paper from University Library of Munich, Germany

Abstract: The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approximates the corresponding importance sampling variance. All functions of interest are evaluated under Gaussian quadrature rules. Examples include a sequential (filtering) evaluation of the likelihood function of a stochastic volatility model where all relevant densities (filtering, predictive and likelihood) are closely approximated by mixtures.

Keywords: Finite mixture; Distance measure; Gaussian quadrature; Importance sampling; Adaptive algorithm; Stochastic volatility; Density kernel (search for similar items in EconPapers)
JEL-codes: C11 C63 (search for similar items in EconPapers)
Date: 2016-06
New Economics Papers: this item is included in nep-ger and nep-ore
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https://mpra.ub.uni-muenchen.de/72326/1/MPRA_paper_72323.pdf original version (application/pdf)

Related works:
Journal Article: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels (2019)
Working Paper: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kerkels (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:72326

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