  A plethora of static and dynamic models exist to forecast Value-at-Risk and
other quantile-related metrics used in financial risk management. Industry
practice tends to favour simpler, static models such as historical simulation
or its variants whereas most academic research centres on dynamic models in the
GARCH family. While numerous studies examine the accuracy of multivariate
models for forecasting risk metrics, there is little research on accurately
predicting the entire multivariate distribution. Yet this is an essential
element of asset pricing or portfolio optimization problems having non-analytic
solutions. We approach this highly complex problem using a variety of proper
multivariate scoring rules to evaluate over 100,000 forecasts of
eight-dimensional multivariate distributions: of exchange rates, interest rates
and commodity futures. This way we test the performance of static models, viz.
empirical distribution functions and a new factor-quantile model, with commonly
used dynamic models in the asymmetric multivariate GARCH class.
