FRM Financial Risk Meter

• Main Authors: MIHOCI, Andrija, ALTHOF, Michael, CHEN, Cathy Yi-Hsuan, HARDLE, Wolfgang Karl
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Systemic Risk; Quantile Regression; Lasso; Financial Markets; Risk Management; Network Dynamics; Recession; Finance; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/skbi/4; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1003&context=skbi
• Institution: Singapore Management University

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.