FRM Financial Risk Meter for Emerging Markets

Amor, Souhir Ben; Althof, Michael; Härdle, Wolfgang Karl

doi:10.2139/ssrn.3785488

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Another example from existing literature is Ben Amor et al (2021). They applied FRM in emerging markets and propose a portfolio allocation mode based on TEs.…”

Section: Introductionmentioning

confidence: 99%

A Financial Risk Meter for China

2021

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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 quantlet.com.

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“…Another example from existing literature is Ben Amor et al (2021). They applied FRM in emerging markets and propose a portfolio allocation mode based on TEs.…”

Section: Introductionmentioning

confidence: 99%

A Financial Risk Meter for China

2021

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Understanding Smart Contracts: Hype or Hope?

Zinovyeva

Reule²,

Härdle

2021

SSRN Journal

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From M-Score to F-Score: Moderating the Relationship between Earnings Management and Stock Performance

Martens,

Andersson

2024

Preprint

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Earnings management and value relevance are critical due to their impact on financial decisions. This study examines the relationship between earnings manipulation as measured by the Beneish M-Score and Dechow F- Score and excess stock returns in the Vietnamese market from 2004 to 2019. The analysis uses robust econometric techniques such as stochastic generalized method of moments (SGMM) to address endogeneity. The findings show a significant inverse relationship between the M-Score and excess returns, while the F- Score shows a nuanced, positive post-crisis relationship. This suggests investors in frontier markets may tolerate higher accounting discretion during economic uncertainty due to limited information sources. The results imply that it is desirable to improve financial reporting quality and transparency. In addition, the results show that investors would benefit from incorporating manipulation scores into risk assessments, avoiding firms with high M-scores, and from recognizing that firms with high F-scores may be more resilient during crises.

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FRM Financial Risk Meter for Emerging Markets

Cited by 3 publications

References 13 publications

A Financial Risk Meter for China

A Financial Risk Meter for China

Understanding Smart Contracts: Hype or Hope?

From M-Score to F-Score: Moderating the Relationship between Earnings Management and Stock Performance

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