Financial Risk Meter based on Expectiles

25 Pages Posted: 1 Apr 2021 Last revised: 26 Oct 2021

See all articles by Rui Ren

Rui Ren

Humboldt University of Berlin - School of Business and Economics

Meng-Jou Lu

National Chiao-Tung University

Yingxing Li

Xiamen University

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Date Written: March 21, 2021

Abstract

The Financial Risk Meter (FRM) is an established quantitative tool that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying the FRM distribution across various entities every day. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and to capture spillover effects in a network.

Keywords: Expectiles, EVaR, CoEVaR, expectile lasso regression, network analysis, systemic risk, Financial Risk Meter

JEL Classification: C01, C58, G01, G11, O31

Suggested Citation

Ren, Rui and Lu, Meng-Jou and Li, Yingxing and Härdle, Wolfgang K., Financial Risk Meter based on Expectiles (March 21, 2021). Available at SSRN: https://ssrn.com/abstract=3809329 or http://dx.doi.org/10.2139/ssrn.3809329

Rui Ren (Contact Author)

Humboldt University of Berlin - School of Business and Economics ( email )

Spandauer Str. 1
Berlin, D-10099
Germany

Meng-Jou Lu

National Chiao-Tung University

1001 Da Hsueh Road
East District
Hsinchu 300, 30050
Taiwan

Yingxing Li

Xiamen University ( email )

Xiamen, Fujian 361005
China

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

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

Unter den Linden 6
Berlin, D-10099
Germany

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