Financial risk management is difficult at the best of times, but especially so in the presence of economic uncertainty and financial crises. The purpose of this special issue on "Advances in Financial Risk Management and Economic Policy Uncertainty" is to highlight some areas of research in...

The purpose of the paper is to present the fundamental equation in tourism finance that connects tourism research to empirical finance and financial econometrics. The energy industry, which includes, oil, gas and bio-energy fuels, together with the tourism industry, are two of the most important...

Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of...

This paper investigates whether and how economic policy uncertainty affects corporate debt maturity. Using a cross-country firm-level dataset for France, Germany, Spain, and Italy from 1996 to 2010, we find that an increase in economic policy uncertainty is significantly associated with a...

This paper investigates whether and how economic policy uncertainty affects corporate debt maturity. Using a large firm-level dataset for four European countries, we find that an increase in economic policy uncertainty is significantly associated with a shortened debt maturity. Moreover, the...

We plot aggregated daily stock returns with absolute value less than x against x and show empirically that this produces a typical spoon-shaped pattern which indicates a special type of asymmetry which has not been discussed before. This pattern disappears when individual returns are averaged; it...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...