Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 002

FRM Financial Risk Meter for Emerging Markets

Souhir Ben Amor
Michael Althof
Wolfgang Karl Härdle

The fast-growing Emerging Market (EM) economies and their improved transparency
and liquidity have attracted international investors. However, the external
price shocks can result in a higher level of volatility as well as domestic
policy instability. Therefore, an efficient risk measure and hedging strategies
are needed to help investors protect their investments against this risk. In
this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is
proposed. The FRM@ EM is applied to capture systemic risk behavior embedded in
the returns of the 25 largest EMs’ FIs, covering the BRIMST (Brazil, Russia,
India, Mexico, South Africa, and Turkey), and thereby reflects the financial
linkages between these economies. Concerning the Macro factors, in addition to
the Adrian & Brunnermeier (2016) Macro, we include the EM sovereign yield spread
over respective US Treasuries and the above-mentioned countries’ currencies. The
results indicated that the FRM of EMs’ FIs reached its maximum during the US
financial crisis following by COVID 19 crisis and the Macro factors explain the
BRIMST’ FIs with various degrees of sensibility. We then study the relationship
between those factors and the tail event network behavior to build our policy
recommendations to help the investors to choose the suitable market for
investment and tail-event optimized portfolios. For that purpose, an overlapping
region between portfolio optimization strategies and FRM network centrality is
developed. We propose a robust and well-diversified tail-event and cluster risk-
sensitive portfolio allocation model and compare it to more classical

FRM (Financial Risk Meter), Lasso Quantile Regression, Network Dynamics,
Emerging Markets, Hierarchical Risk Parity

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