Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 022

Ruting Wang
Michael Althof
Wolfgang Karl Härdle

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

FRM (Financial Risk Meter), Lasso Quantile Regression, Financial Network, China,
Shapley value

JEL Classification:
C30, C58, G11, G15, G21