In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks...

Under the new Basel bank capital framework, a bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that a bank may use the internal models, including the loan-level risk parameter estimates such as PD and LGD, to...

Under the new Basel bank capital framework, each bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that each bank may use its internal risk models for the loan-level risk parameter estimates such as probability...

We introduce a novel application of support vector machines (SVM), an important machine learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, forecasting a condition in the present time because the full information will not be available until later, is...

While machine learning and its many variants are becoming established tools in quantitative finance, their application in a risk measurement context is less developed. This paper uses a scheme from probability theory and statistics – Gaussian Processes – and applies the corresponding...

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the...