Forecasting Using Time Varying Meta-Elliptical Distributions with a Study of Commodity Futures Prices

We propose a methodological approach to the forecast and evaluation of multivariate distributions with time varying parameters. For reasons related to feasible inference attention is restricted to meta-elliptical distributions. We use our approach for the study of a large data set of 16 commodity prices. Our approach leads to a theory for model validation avoiding common problems caused by discontinuities, time variation of parameters and nuisance parameters

Copula Based Monte Carlo Integration in Financial Problems

A computational technique that transform integrals over RK, or some of its subsets, into the hypercube [0, 1]K can be exploited in order to solve integrals via Monte Carlo integration without the need to simulate from the original distribution; all that is needed is to simulate iid uniform [0, 1] pseudo random variables. In particular the technique arises from the copula representation of multivariate distributions and the use of the marginal quantile function of the data. The procedure is further simplified if the quantile function has closed form. Several financial applications are considered in order to highlight the scope of this numerical technique for financial problem