Oil price experienced crashes during the period of the 2008 global financial crisis and the recent period of oil price crisis in 2014. Hedging oil price risk becomes more important for energy industry investors. In this paper, we investigate the out-of-sample dynamic hedging performance by using...

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...

One of the reasons why investors were not prepared for heavy losses in the stock markets that occurred after the beginning of sub-prime mortgage crisis in the U.S. lies in the curious fact that many practitioners were led to believe that there are so many independent agents participating in the...

In the late 90's, after severe financial and economic crisis, accompanied by inflation and exchange rate instability, Eastern Europe emerged into two groups of countries with radically contrasting monetary regimes (Currency Boards and Inflation targeting). The task of our study is to compare...

Investors have increasing interests in sophisticated yet transparent analytic tools to handle model uncertainty, tail risk and market dynamics. This paper demonstrates how macroeconomic factor models, based on Bayesian model averaging (BMA), can help address the challenges in some specific...

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so-called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions....

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I...

We propose a decomposition to distinguish between Knightian uncertainty (ambiguity) and risk, where the first measures the uncertainty about the probability distribution generating the data, while the second measures uncertainty about the odds of the outcomes when the probability distribution is...

Employing a time-varying volatility transmission model, this study examines the impact of asymmetric information and uncertainty on the interactions across energy and foreign exchange markets. The results show that the ARCH coefficients monitoring the impact for the "own" shocks (currency on...

In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify the pro-cyclicality of the current way financial...