Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 008

Financial Risk Meter based on Expectiles

Rui Ren
Meng-Jou Lu
Yingxing Li
Wolfgang Karl Härdle

The Financial Risk Meter (FRM) is an established mechanism 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 coherent and 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 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 capture spillover effects in a

expectiles, EVaR, CoEVaR, expectile lasso regression, network analysis, systemic
risk, Financial Risk Meter

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