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An Ai Approach To Measuring Financial Risk

Author

Listed:
  • 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 HÄRDLE

    (��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)

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.

Suggested Citation

  • Lining Yu & Wolfgang Karl Hã„Rdle & Lukas Borke & Thijs Benschop, 2023. "An Ai Approach To Measuring Financial Risk," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 68(05), pages 1529-1549, September.
    • Handle: RePEc:wsi:serxxx:v:68:y:2023:i:05:n:s0217590819500668
    DOI: 10.1142/S0217590819500668
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    More about this item

    Keywords

    Systemic risk; quantile regression; value at risk; lasso; parallel computing; financial risk meter;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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