extensive number of robustness checks. Overall, downside cash flow risk is priced most consistently across different samples … ability. The downside cash flow risk premium is mainly attributable to small stocks. The risk premium for large stocks appears … much more driven by a compensation for symmetric, cash flow related risk. Finally, we multiply our premia estimates by …

extensive number of robustness checks. Overall, downside cash flow risk is priced most consistently across different samples … ability. The downside cash flow risk premium is mainly attributable to small stocks. The risk premium for large stocks appears … much more driven by a compensation for symmetric, cash flow related risk. Finally, we multiply our premia estimates by …

We show that if an agent is uncertain about the precise form of his utility function, his actual relative risk aversion … may depend on wealth even if he knows his utility function lies in the class of constant relative risk aversion (CRRA … their risk aversion parameter invest less in risky assets than wealthy investors with identical risk aversion uncertainty. …

Contemporary financial stochastic programs typically involve a trade-offbetween return and (downside)-risk. Using …. We find that the model can be tuned easily using Value-at-Risk (VaR) related benchmarks. In the multi-stage setting, we … formally prove that the optimal solution consists of a sequence of myopic (single-stage) decisions with risk …

We show that if an agent is uncertain about the precise form of his utility function, his actual relative risk aversion … may depend on wealth even if he knows his utility function lies in the class of constant relative risk aversion (CRRA … their risk aversion parameter invest less in risky assets than wealthy investors with identical risk aversion uncertainty …

about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help … to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are …

Contemporary financial stochastic programs typically involve a trade-offbetween return and (downside)-risk. Using …. We find that the model can be tuned easily using Value-at-Risk (VaR) related benchmarks. In the multi-stage setting, we … formally prove that the optimal solution consists of a sequence of myopic (single-stage) decisions with risk …

dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … dynamics of higher-order moments, and to the other preferred choice of forecasting distribution. We apply our method to Value-at-Risk … volatility forecasting of stock returns and exchange rates. …