Portfolio risk estimation in volatile markets requires employing fat-tailed models for financial returns combined with … copula functions to capture asymmetries in dependence and an appropriate downside risk measure. In this survey, we discuss … how these three essential components can be combined together in a Monte Carlo based framework for risk estimation and …

In this paper, we study a stochastic optimal control for max-min utility admitting volatility ambiguity. By standard …

first-order approximated solution built by perturbation methods accounts for risk. We show that risk matters economically in … a real business cycle (RBC) model with habit formation and capital adjustment costs and that neglecting risk leads to …

benchmark for all decreasing absolute risk-averse investors, using Quadratic Programming. The method is applied to standard data … the performance of Mean-Variance optimization by tens to hundreds of basis points per annum, for low to medium risk levels …. The improvements critically depend on imposing the complex condition of Decreasing Absolute Risk Aversion in addition to …

We study consumption-portfolio and asset pricing frameworks with recursive preferences and unspanned risk. We show that … with recursive preferences and unspanned risk. Our setting is not restricted to affine asset price dynamics. Numerical …

expected utility. The first part of our discussion focus on the relationship between central moments, different order integrals … and stochastic dominance as well as relationship between central moments, different order reversed integrals and risk … different form of expected utility. Part of our results could be viewed as a generalization of theorems in Chan, et al. (2012 …

We establish a connection between continuous-time recursive utility and the notion of consistency studied, in … that the optimal time-consistent solution to this time-global problem is closely related to the standard recursive utility …-time recursive utility was developed for Brownain markets, and a generalization is complicated. Our approach contributes with insight …