We present a simple stochastic representation for the joint distribution of sample estimates of three scalar parameters and two vectors of portfolio weights that characterize the minimum-variance frontier. This stochastic representation is useful for sampling observations efficiently, deriving...

Heavy tails and volatility clusters are both stylized facts of financial returns that destabilize markets. The former are extreme events by definition and the latter can accelerate adverse market developments. This work disentangles the two sources and examines which one does the greater damage...

When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while...

Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean...

We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate both the statistical and the economic validity of the measures. The option-implied measure of Bollerslev and Todorov (2011b) (BT11Q) performs the best overall. While some other tail risk...

We use Markov chain Monte Carlo (MCMC) sampling to draw model coefficients to generate LGD distributions. We find that applying this Bayesian method on a sophisticated model that accounts for the bi-modal distribution of the LGDs, such as the zero-one-inflated beta (ZOIB) model, can generate LGD...