Flexible Modeling of Conditional Distributions using Smooth Mixtures of Asymmetric Student T Densities
2010 (English)In: Journal of Statistical Planning and Inference, ISSN 0378-3758, Vol. 140, no 12, 3638-3654 p.Article in journal (Refereed) Published
A general model is proposed for flexibly estimating the density of a continuous response variableconditional on a possibly high-dimensional set of covariates. The model is a finite mixture ofasymmetric student-t densities with covariate-dependent mixture weights. The four parameters ofthe components, the mean, degrees of freedom, scale and skewness, are all modeled as functionsof the covariates. Inference is Bayesian and the computation is carried out using Markov chainMonte Carlo simulation. To enable model parsimony, a variable selection prior is used in each setof covariates and among the covariates in the mixing weights. The model is used to analyze thedistribution of daily stock market returns, and shown to more accurately forecast the distributionof returns than other widely used models for financial data.
Place, publisher, year, edition, pages
2010. Vol. 140, no 12, 3638-3654 p.
Bayesian inference, Markov Chain Monte Carlo, Mixture of Experts, Variable selection, Volatility modeling
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:su:diva-43073DOI: 10.1016/j.jspi.2010.04.031ISI: 000281982700008OAI: oai:DiVA.org:su-43073DiVA: diva2:353496