This paper focuses on portfolio risk forecasting in an asymmetrical framework. Risk is defined by two factors; the … distribution, respectively their joint risk. Statistical analyses state, this model quantifies the distribution tails very … accurately, resulting precise VaR estimates. We also construct minimum-risk portfolios on basis of the volatility forecasts. The …

risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one … uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior … better results in terms of profit per capital engagement and risk than using a standard linear normalization …

"Systematic Downside Risk" (SDR) is defined to characterize this asymmetry in the comovement of betas. This indicator negatively …

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model …

systematic risk is highly nonlinear in extreme scenarios-especially during the subprime crisis. We find that countercyclical …-traditional risk premia by deliberately increasing their systematic risk while the later focus more on minimizing risk. Our results … suggest that the hedge fund strategies' betas respond more to illiquidity uncertainty than to illiquidity risk during crises …

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference … to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of …-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …