AN AI APPROACH TO MEASURING FINANCIAL RISK

Lining Yu, Wolfgang Karl Hã„rdle, Lukas Borke () and Thijs Benschop ()
Additional contact information
Lining Yu: Ladislaus von Bortkiewicz Chair of Statistics, C.A.S.E. - Center for Applied Statistics and Econometrics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Wolfgang Karl Hã„rdle: ��C.A.S.E. - Center for Applied Statistics and Econometrics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany‡Wang Yanan Institute for Studies in Economics, Xiamen University, 422 Siming Road, Xiamen 361005, P. R. China§Sim Kee Boon Institute for Financial Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, Singapore¶Department of Mathematics and Physics, Charles University Prague, Ke Karlovu 2027/3, 12116 Praha 2, Czech
Lukas Borke: Ladislaus von Bortkiewicz Chair of Statistics, C.A.S.E. - Center for Applied Statistics and Econometrics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Thijs Benschop: Ladislaus von Bortkiewicz Chair of Statistics, C.A.S.E. - Center for Applied Statistics and Econometrics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

The Singapore Economic Review (SER), 2023, vol. 68, issue 05, 1529-1549

Abstract: AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly-traded financial institutions. We demonstrate the suitability of this AI-based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword  FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.

Keywords: Systemic risk; quantile regression; value at risk; lasso; parallel computing; financial risk meter (search for similar items in EconPapers)
JEL-codes: C21 C51 G01 G18 G32 G38 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217590819500668
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:serxxx:v:68:y:2023:i:05:n:s0217590819500668

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217590819500668

Access Statistics for this article

The Singapore Economic Review (SER) is currently edited by Euston Quah

More articles in The Singapore Economic Review (SER) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().