Traditionally, financial risk management has mainly focused on the types of risk that can be identified and measured. Many actuarial and statistical theories and models have been developed in the past, to quantify such risks. However, high-profile events such as Black Monday, the Asian financial...

Quantitative finance combines mathematical finance, financial statistics, financial econometrics and empirical finance to provide a solid quantitative foundation for the analysis of financial issues. The purpose of this special issue on "Recent developments in quantitative finance" is to...

This book brings together domains in financial asset pricing and valuation, financial investment theory, econometrics modeling, and the empirical analyses of financial data by applying appropriate econometric techniques. These domains are highly intertwined and should be properly understood in...

The volume aims at providing an outlet for some of the best papers presented at the 15th Annual Conference of the African Econometric Society, which is one of the “chapters” of the International Econometric Society. Many of these papers represent the state of the art in financial...

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

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...

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...