We study the impact of parameter and model uncertainty on the left-tail of predictive densities and in particular on VaR forecasts. To this end, we evaluate the predictive performance of several GARCH-type models estimated via Bayesian and maximum likelihood techniques. In addition to individual...

We consider a logistic transform of the monthly US unemployment rate. For this time series, a pseudo out-of-sample forecasting competition is held between linear and nonlinear models and averages of these models. To combine predictive densities, we use two complementary methods: Bayesian model...

Heuristic optimization methods and their application to finance are discussed. Two illustrations of these methods are presented: the selection of assets in a portfolio and the estimation of a complicated econometric model

This short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus avoids...

A novel approach for stress-testing (portfolios of) financial assets is presented. The technique extends the parametric Entropy Pooling approach to skewed and thick-tailed markets. The technique rests on a copula-marginal decomposition for the entropy together with several approximation schemes...

We propose a methodology to perform macroeconomic stress-testing on the probability of default of a given borrowers' population (i.e., aggregate probability of default) through simulation from a vector error correction model and entropy pooling (Meucci, 2008)

There is extensive empirical evidence that funds of hedge funds (FoHFs) quickly change their investment bets as a function of the changing market conditions. In this chapter, we first analyze the stability of risk exposure and performance of FoHFs during the period January 2005-June 2011. We...