Risk parity is a portfolio construction technique that scales sections of a portfolio—e.g., stocks, bonds, currencies, commodities—so that forecasted contributions to net portfolio risk match the budget. Because risks are measured from a point-estimate of covariance, the method is subject to...

We analyze the ``convex level sets" (CxLS) property of risk functionals, which is a necessary condition for the notions of elicitability, identifiability and backtestability, popular in the recent statistics and risk management literature. We put the CxLS property in the multi-dimensional...

We consider the general class of spectrally positive Lévy risk processes, which are appropriate for businesses with continuous expenses and lump sum gains whose timing and sizes are stochastic. Motivated by the fact that dividends are paid periodically in real life, we study periodic dividend...

We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control and hence in the decision making process, could potentially offset the uncertainty inherent in the environment and yield better outcomes. This methodology is suitable for the social sciences...

This paper examines the robustness of Lehmann's ranking of experiments (Lehmann, 1988) for decisionmakers who are uncertainty-averse à la Cerreia-Vioglio et al. (2011). We show that, assuming commitment, for all uncertainty-averse indices satisfying some mild assumptions, Lehmann's...

We address the problem of minimizing the risk of an exposure (e.g., cash holdings) to a small number of defaultable counterparties based on spectral risk measures, in particular the expected shortfall. The resulting risk-minimal allocation turns out to be economically implausible in a number of...

We develop scenario-based stochastic programming models for hedging the risks of international portfolios using options. The models provide an increasing level of integration in managing market and foreign exchange (FX) risks. We start with a single-stage model with currency options for...

Robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance when making well-informed risk management decisions. In this paper, we quantify for any given distortion risk measure its robustness to distributional uncertainty by...