Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

Natalia Khorunzhina, Jean-Francois Richard

Research output: Working paperResearch

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.
Original languageEnglish
Place of PublicationMünchen
PublisherMunich Personal RePEc Archive
Number of pages23
Publication statusPublished - 2016
SeriesMPRA Paper
Number72326

Keywords

  • Finite mixture
  • Distance measure
  • Gaussian quadrature
  • Importance sampling
  • Adaptive algorithm
  • Stochastic volatility
  • Density kernel

Cite this

Khorunzhina, N., & Richard, J-F. (2016). Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels. München: Munich Personal RePEc Archive. MPRA Paper, No. 72326
Khorunzhina, Natalia ; Richard, Jean-Francois. / Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels. München : Munich Personal RePEc Archive, 2016. (MPRA Paper; No. 72326).
@techreport{6439bbb06ce8418a98d30a3287a7d7be,
title = "Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels",
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, Finite mixture, Distance measure, Gaussian quadrature, Importance sampling, Adaptive algorithm, Stochastic volatility, Density kernel",
author = "Natalia Khorunzhina and Jean-Francois Richard",
year = "2016",
language = "English",
series = "MPRA Paper",
publisher = "Munich Personal RePEc Archive",
number = "72326",
address = "Germany",
type = "WorkingPaper",
institution = "Munich Personal RePEc Archive",

}

Khorunzhina, N & Richard, J-F 2016 'Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels' Munich Personal RePEc Archive, München.

Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels. / Khorunzhina, Natalia; Richard, Jean-Francois.

München : Munich Personal RePEc Archive, 2016.

Research output: Working paperResearch

TY - UNPB

T1 - Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

AU - Khorunzhina, Natalia

AU - Richard, Jean-Francois

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Finite mixture

KW - Distance measure

KW - Gaussian quadrature

KW - Importance sampling

KW - Adaptive algorithm

KW - Stochastic volatility

KW - Density kernel

KW - Finite mixture

KW - Distance measure

KW - Gaussian quadrature

KW - Importance sampling

KW - Adaptive algorithm

KW - Stochastic volatility

KW - Density kernel

M3 - Working paper

T3 - MPRA Paper

BT - Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

PB - Munich Personal RePEc Archive

CY - München

ER -