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Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities

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Author Info

  • Li, Feng

    ()
    (Department of Statistics, Stockholm University)

  • Villani, Mattias

    (Research Department, Central Bank of Sweden)

  • Kohn, Robert

    (Economics, The University of New South Wales,)

Abstract

A general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate dependent mixture weights. The four parameters of the components, the mean, degrees of freedom, scale and skewness, are all modelled as functions of the covariates. Inference is Bayesian and the computation is carried out using Markov chain Monte Carlo simulation. To enable model parsimony, a variable selection prior is used in each set of covariates and among the covariates in the mixing weights. The model is used to analyse the distribution of daily stock market returns, and shown to more accurately forecast the distribution of returns than other widely used models for financial data.

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Bibliographic Info

Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 233.

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Length: 27 pages
Date of creation: 01 Oct 2009
Date of revision:
Handle: RePEc:hhs:rbnkwp:0233

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Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
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Related research

Keywords: Bayesian inference; Markov Chain Monte Carlo; Mixture of Experts; Variable selection; Volatility modeling.;

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Cited by:
  1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.

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