Please use this identifier to cite or link to this item:
Title: Machine-learning-enhanced systemic risk measure: a two-step supervised learning approach
Authors: Liu, Ruicheng
Pun, Chi Seng
Keywords: Science::Mathematics
Issue Date: 2022
Source: Liu, R. & Pun, C. S. (2022). Machine-learning-enhanced systemic risk measure: a two-step supervised learning approach. Journal of Banking and Finance, 136, 106416-.
Project: NRF2018-SR2001-006
Journal: Journal of Banking and Finance
Abstract: 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.
ISSN: 0378-4266
DOI: 10.1016/j.jbankfin.2022.106416
Rights: © 2022 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

Citations 50

Updated on Nov 30, 2022

Web of ScienceTM
Citations 50

Updated on Nov 27, 2022

Page view(s)

Updated on Dec 3, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.