Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach

• Published in: Journal of banking & finance Vol. 136; p. 106416
• Main Authors: Liu, Ruicheng, Pun, Chi Seng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Conditional capital shortfall; Cross-sectional measures; Machine learning; Marginal expected shortfall (MES); SRISK; Systemic risk measure; Marginal expected shortfall (MES); SRISK; Cross-sectional measures; Conditional capital shortfall; Systemic risk measure; Machine learning
• ISSN: 0378-4266; 1872-6372
• DOI: 10.1016/j.jbankfin.2022.106416

•Brings machine learning algorithms into systemic risk measurement.•Allows for the incorporation with any (e.g. macro-economic) variables.•Allows for the hierarchical structure of the systemic event and return dependence.•Compares between econometric and algorithmic approaches in predicting SRISK.•Examines the importance of the explanatory variables in estimating MES or SRISK. This paper explores ways to improve the existing systemic risk measures by incorporating machine learning algorithms into the measurement. We aim to overcome the shortcomings of existing methods that rely on restricted modeling and are difficult to tap into various data resources. To this end, this paper unifies a dynamic quantification framework for systemic risk and links it to a two-step supervised learning problem, which allows for hierarchical structure of the systemic event and the return dependence. We leverage the generalization and predictive powers of machine learning to statistically model the tail events and the co-movements of the equity returns during the shocks to the macro-economy. Our results show that most machine learning algorithms enhance the systemic risk measure’s predictive power. Numerous comparative and sensitivity backtesting studies for United States and Hong Kong markets are conducted, from which we recommend the best machine learning algorithm for systemic risk measurement.