Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 021

FRM Financial Risk Meter

Andrija Mihoci
Michael Althof
Cathy Yi-Hsuan Chen
Wolfgang Karl Hardle

Abstract:
A daily systemic risk measure is proposed accounting for links and mutual
dependencies between financial institutions utilising tail event information. FRM
(Financial Risk Meter) is based on Lasso quantile regression designed to capture
tail event co-movements. The FRM focus lies on understanding active set data
characteristics and the presentation of interdependencies in a network topology.
Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM
indices detect systemic risk at selected areas and identifies risk factors. In
practice, FRM is applied to the return time series of selected financial
institutions and macroeconomic risk factors. Using FRM on a daily basis, we
identify companies exhibiting extreme "co-stress", as well as "activators" of
stress. With the SRM@EuroArea, we extend to the government bond asset class. FRM
is a good predictor for recession probabilities, constituting the FRM-implied
recession probabilities. Thereby, FRM indicates tail event behaviour in a
network of financial risk factors.

Keywords:
Systemic Risk, Quantile Regression, Financial Markets, Risk Management, Network
Dynamics, Recession

JEL Classification:
C00