IDEAS home Printed from https://ideas.repec.org/p/hhs/rbnkwp/0233.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

  • Li, Feng & Villani, Mattias & Kohn, Robert, 2009. "Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities," Working Paper Series 233, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0233
    as

    Download full text from publisher

    File URL: http://www.riksbank.se/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2009/wp233.pdf
    Download Restriction: no

    More about this item

    Keywords

    Bayesian inference; Markov Chain Monte Carlo; Mixture of Experts; Variable selection; Volatility modeling.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://edirc.repec.org/data/rbgovse.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.