Measuring daily systemic risk with intraday data: Evidence from foreign exchange market

Abstract

This study introduces a method for computing daily systemic risk measures using high-frequency data, specifically realized conditional value-at-risk (RCoVaR) and realized marginal expected shortfall (RMES). RCoVaR and RMES are empirical estimators derived from scaling high-frequency returns, offering benefits such as model-independence and adaptability to diverse datasets. To mitigate market microstructure noise (MMN) inherent in high-frequency data, we employ overlapping and subsampling approaches in the estimation of RCoVaR and RMES. Empirical analysis focuses on systemic risk within the foreign exchange market. The results indicate that noise-treated RCoVaR and RMES serve as effective alternatives for daily systemic risk estimation. These techniques also enhance out-of-sample predictive accuracy when employed as predictors within systemic risk forecasting frameworks.