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Dynamic Density Forecasts for Multivariate Asset Returns
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DOI：10.1002/for.1192.png)
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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 as well as standard techniques to evaluate multivariate forecasts, we conclude that the augmented joint density provides highly accurate forecasts of the 'negative tail' of the joint distribution. Copyright (C) 2010 John Wiley & Sons, Ltd.
Keyword:
forecasting of joint density
time-varying higher co-moments
method of moments
multivariate value-at-risk
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