FRM Financial Risk Meter for Emerging Markets

TL;DR

This paper introduces the Financial Risk Meter (FRM), a systemic risk measure tailored for emerging markets, analyzing its behavior during crises and integrating macro factors for improved portfolio risk management.

Contribution

The paper proposes the FRM-EM, a novel systemic risk measure for emerging markets, incorporating macro factors and linking risk behavior to tail-event network dynamics for portfolio optimization.

Findings

FRM peaks during US financial crises and COVID-19 pandemic.

Macro factors influence BRIMST financial institutions with varying sensitivity.

The proposed portfolio model enhances diversification and tail-event risk management.

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 and 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 in-vestment 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