Arbeitspapier
Financial Risk Meter based on expectiles
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 network.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2021-008
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Thema
-
expectiles
EVaR
CoEVaR
expectile lasso regression
network analysis
systemicrisk
Financial Risk Meter
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Ren, Rui
Lu, Meng-Jou
Li, Yingxing
Härdle, Wolfgang
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Ren, Rui
- Lu, Meng-Jou
- Li, Yingxing
- Härdle, Wolfgang
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2021
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