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FRM: A Financial Risk Meter Based on Penalizing Tail Events Occurrence

SFB 649 Discussion Paper 2017-003

42 Pages Posted: 17 Feb 2017 Last revised: 20 Feb 2017

Lining Yu

Humboldt University of Berlin

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Lukas Borke

Humboldt University of Berlin

Thijs Benschop

Humboldt University of Berlin

Date Written: February 10, 2017

Abstract

In this paper we propose 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 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.

Keywords: Systemic Risk, Quantile Regression, Value at Risk, Lasso, Parallel Computing

JEL Classification: C21, C51, G01, G18, G32, G38

Suggested Citation

Yu, Lining and Härdle, Wolfgang K. and Borke, Lukas and Benschop, Thijs, FRM: A Financial Risk Meter Based on Penalizing Tail Events Occurrence (February 10, 2017). SFB 649 Discussion Paper 2017-003. Available at SSRN: https://ssrn.com/abstract=2919263 or http://dx.doi.org/10.2139/ssrn.2919263

Lining Yu

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Wolfgang Härdle (Contact Author)

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Unter den Linden 6
Berlin, D-10099
Germany
+49 30 2093 5631 (Phone)
+49 30 2093 5649 (Fax)

Lukas Borke

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Thijs Benschop

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

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