Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities
Feng Li (),
Mattias Villani and
Robert Kohn ()
No 233, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)
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.
Keywords: Bayesian inference; Markov Chain Monte Carlo; Mixture of Experts; Variable selection; Volatility modeling. (search for similar items in EconPapers)
JEL-codes: C11 C53 (search for similar items in EconPapers)
Pages: 27 pages
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:rbnkwp:0233
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