FRM Financial Risk Meter for Emerging Markets

47 Pages Posted: 16 Feb 2021

See all articles by Souhir Ben Amor

Souhir Ben Amor

Humboldt University of Berlin

Michael Althof

Humboldt University of Berlin - Institute for Statistics and Econometrics

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Date Written: February 10, 2021

Abstract

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 -9 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 approaches.

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

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

Suggested Citation

Ben Amor, Souhir and Althof, Michael and Härdle, Wolfgang K., FRM Financial Risk Meter for Emerging Markets (February 10, 2021). Available at SSRN: https://ssrn.com/abstract=3785488 or http://dx.doi.org/10.2139/ssrn.3785488

Souhir Ben Amor

Humboldt University of Berlin

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Michael Althof (Contact Author)

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Spandauer Str. 1
Berlin, D-10178
Germany

HOME PAGE: http://irtg1792.hu-berlin.de

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Chiao Tung University ( email )

No. 1001 Ta Hsueh Road
Hsinchu 300
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

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