Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities
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.
|Date of creation:||01 Oct 2009|
|Contact details of provider:|| Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden|
Phone: 08 - 787 00 00
Fax: 08-21 05 31
Web page: http://www.riksbank.com/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hhs:rbnkwp:0233. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lena Löfgren)
If references are entirely missing, you can add them using this form.