Arbeitspapier
A financial risk meter for China
This paper develops a new risk meter specifically for China - FRM@China - to detect systemic financial risk as well as tail-event (TE) dependencies among major financial institutions (FIs). Compared with the CBOE FIX VIX, which is currently the most popular financial risk measure, FRM@China has less noise. It also emitted a risk signature much earlier than the CBOE FIX VIX index in the 2020 COVID pandemic. In addition, FRM@China uses a single quantile-lasso regression model to allow both the assessment of risk transfer between different sectors in which FIs operate and the prediction of systemic risk. Because the risk indicator in FRM@China is based on penalization terms, its relationship with macro variables are unknown and non-linear. This paper further expands the existing FRM approach by using Shapley values to identify the dynamic contribution of different macro features in this type of "black box" situation. The results show that short-term interest rates and forward guidance are significant risk drivers. This paper considers the interaction among FIs from mainland China, Hong Kong and Taiwan to provide an enhanced regional tool set for regulators to evaluate financial policy responses. All quantlets are available on quantlet.com.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2021-022
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models; Multiple Variables: General
Financial Econometrics
Portfolio Choice; Investment Decisions
International Financial Markets
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- Thema
-
FRM (Financial Risk Meter)
Lasso Quantile Regression
Financial Network
China
Shapley value
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Wang, Ruting
Althof, Michael
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, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Wang, Ruting
- Althof, Michael
- Härdle, Wolfgang
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2021
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