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...

Asset pricing models assume the risk-free rate to be a key factor for equity prices. Hence, there should be a strong link between monetary policy rate uncertainty and equity return volatility, both in theory and data. This paper uses regression-based projections for realized variance to examine...

In this paper, we try to build an efficient portfolio among four possible portfolios based on the some 31 Casablanca listed shares. Our analysis concerns the risk which arises from the Markowitz mean-variance approach. Our work method will be implemented as following: first of all, we will test...

The management of financial risks, which is a branch of financial theory, is defined as a process that begins with risk factors identification, continues with measurement of risk and concludes with the coverage of that risk. This work focuses on the second phase of management process, namely the...

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...

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...