Dynamic density forecasts for multivariate asset returns
AbstractWe 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 30 (2011)
Issue (Month): 6 (September)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
forecasting of joint density ; time‐varying higher co‐moments ; method of moments ; multivariate value‐at‐risk ;
Other versions of this item:
- Evarist Stoja & Arnold Polanski, 2009. "Dynamic Density Forecasts for Multivariate Asset Returns," Bristol Economics Discussion Papers 09/616, Department of Economics, University of Bristol, UK.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull 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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