, which is called G-bounds. Constructed G-bounds evaluate risk in the financial markets more carefully than models based on …, the closer the risk of losses on the stock market to the corresponding risk of loss for a normal distribution, the higher …

a risk-free asset. We show that the method behaves similarly to Kelly on Geometric Brownian Motion in that it …

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 … application to model risk assessment …

portfolio assets are more than two, the value at risk tends to become erratic while repeated computations generate different …

risk. Calibrations show that the impact of model uncertainty could be enormous, lowering the equilibrium risk-free rate and … increasing the equity risk premium. This finding contributes to a potential explanation for the equity risk premium and risk … is necessary to explain the equity risk premium puzzle. In this paper, the equilibrium wealth and expected utility under …

volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more … volatilities. A utility-based framework designed to evaluate the economic gains from risk modeling highlights the interplay between … parsimony of model specification, transaction costs, and speed of trading in the practical implementation of the different risk …

, heavy tails, and nonellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio … optimization with active risk monitoring. The former selects optimal portfolio weights. The later, independently, initiates market … leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk …