Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2019
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" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) 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
Discipline
Artificial Intelligence and Robotics | Finance | Finance and Financial Management | Technology and Innovation
Publication
Singapore Economic Review
First Page
1
Last Page
21
ISSN
0217-5908
Identifier
10.1142/S0217590819500668
Publisher
World Scientific
Embargo Period
5-20-2021
Citation
YU, Lining; HARDLE, Wolfgang Karl; BORKE, Lukas; and BENSCHOP, THIJS.
An AI approach to measuring financial risk. (2019). Singapore Economic Review. 1-21.
Available at: https://ink.library.smu.edu.sg/skbi/7
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1142/S0217590819500668
Included in
Artificial Intelligence and Robotics Commons, Finance Commons, Finance and Financial Management Commons, Technology and Innovation Commons