This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better...

This paper analyzes the relation between correlation risk and the cross-section of hedge fund returns.Legal framework and investment mandate imply that hedge funds can be severely exposed tocorrelation risk: Hedge funds ability to enter long-short positions can be useful to reduce marketbeta,...

Hansen/Jagannathan (1991) show how to use security market data to restrict the admissible region for means and standards deviations of intertemporal marginal rates of substitution of consumers. They als characterise the duality betwenn the mean-standard deviation frontier for valid stochastic...

The aim of the paper is to study empirically the influence of higher moments of the return distribution on conditional value at risk (CVaR). To be more exact, we attempt to reveal the extent to which the risk given by CVaR can be estimated when relying on the mean, standard deviation, skewness...

In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient...