An empirical comparison of correlation-based systemic risk measures

Despite the growing attention in the last years on the topic of systemic risk, a widely accepted definition of systemic crisis is missing. We use a theoretical scheme to subjectively define a systemic event. This permits the analysis of a financial crisis as a standard binary classification problem, providing an intuitive and useful framework to compare systemic risk measures defined in very different fields. Then we focus the empirical analysis on the comparison of the performance of correlation-based systemic risk measures using the standard tools for the evaluation of binary classifiers as the receiver operating characteristic (ROC) curve and the area under the curve (AUC). We show that the binary classification framework is useful but unable to capture some significant differences among the measures under comparison. The experimental approach, developed on real financial data, is divided in an in-sample exercise, able to evaluate the descriptive power of the different systemic risk measures, and an out-of-sample application to evaluate the capacity of the measures in preventing and predicting systemic events. The forecasting ability of a measure can be fundamental for policy makers and investors respectively to stabilize market fluctuations and to reduce the losses.