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Dynamic Density Forecasts for Multivariate Asset Returns

Author

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  • Evarist Stoja
  • Arnold Polanski

Abstract

We propose a simple and flexible framework for forecasting the joint density of asset returns. The multinormal distribution is augmented with a polynomial in (time-varying) non-central co-moments of assets. We estimate the coefficients of the polynomial via the Method of Moments for a carefully selected set of co-moments. In an extensive empirical study, we compare the proposed model with a range of other models widely used in the literature. Employing a recently proposed technique to evaluate multivariate forecasts, we conclude that the augmented joint density provides highly accurate forecasts of the negative tail of the joint distribution.

Suggested Citation

  • Evarist Stoja & Arnold Polanski, 2009. "Dynamic Density Forecasts for Multivariate Asset Returns," Bristol Economics Discussion Papers 09/616, School of Economics, University of Bristol, UK.
    • Handle: RePEc:bri:uobdis:09/616
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    Cited by:

    1. Su Xu, 2017. "A VaR assuming Student t distribution not significantly different from a VaR assuming normal distribution," Risk Management, Palgrave Macmillan, vol. 19(3), pages 189-201, August.

    More about this item

    Keywords

    Time-varying higher co-moments; Joint Density Forecasting; Method of Moments; Multivariate Value-at-Risk.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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