The relationship between risk and return is one of the most studied topics in finance. The majority of the literature … is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the …-realized variance. We find strong robust evidence of volatility feedback in monthly data. Once volatility feedback is accounted for …

suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging …. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory presented in several earlier …

measures, resulting from the new model, can be used to implemennt joint risk scenario analysis. …

In this paper, we extend the standard Gaussian stochastic-volatility Bayesian VAR by employing the generalized …

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 …

risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and …We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and … Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not …

greater risk-free rate volatility. But raising the prior uncertainty on dividend growth rates has ambiguous effects on the … a parsimonious set of prior parameters, the model generates a sizeable equity premium and a low risk-free rate even with … a power utility function, low risk aversion, and absence of persistence in growth rates. Raising the prior uncertainty …

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of … Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the … [increasingly] Bayesian—continues to be a key methodological foundation of risk management and regulation-related risk modeling …

We quantify crash risk in currency returns. To accomplish this task, we develop and estimate an empirical model of …% (and can be as high as 40%) of total currency risk, as measured by the entropy of exchange rate changes, over horizons of … smiles and that jump risk is priced …

inference based on their lower dimensional representation. We apply DSFM to estimate the dynamic structure of risk neutral …