IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2102.05398.html
   My bibliography  Save this paper

FRM Financial Risk Meter for Emerging Markets

Author

Listed:
  • Souhir Ben Amor
  • Michael Althof
  • Wolfgang Karl Hardle

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

Suggested Citation

  • Souhir Ben Amor & Michael Althof & Wolfgang Karl Hardle, 2021. "FRM Financial Risk Meter for Emerging Markets," Papers 2102.05398, arXiv.org.
    • Handle: RePEc:arx:papers:2102.05398
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2102.05398
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cai, Jian & Eidam, Frederik & Saunders, Anthony & Steffen, Sascha, 2018. "Syndication, interconnectedness, and systemic risk," Journal of Financial Stability, Elsevier, vol. 34(C), pages 105-120.
    2. Bunda, Irina & Hamann, A. Javier & Lall, Subir, 2009. "Correlations in emerging market bonds: The role of local and global factors," Emerging Markets Review, Elsevier, vol. 10(2), pages 67-96, June.
    3. Alexander Haluszczynski & Ingo Laut & Heike Modest & Christoph Rath, 2017. "Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization," Papers 1712.02661, arXiv.org.
    4. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019. "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, vol. 208(1), pages 282-298.
    5. Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter FRM based on Expectiles," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Wang, Ruting & Althof, Michael & Härdle, Wolfgang, 2021. "A financial risk meter for China," IRTG 1792 Discussion Papers 2021-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ben Amor, Souhir & Althof, Michael & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter for emerging markets," Research in International Business and Finance, Elsevier, vol. 60(C).
    2. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter FRM based on Expectiles," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Wang, Ruting & Althof, Michael & Härdle, Wolfgang, 2021. "A financial risk meter for China," IRTG 1792 Discussion Papers 2021-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang, 2021. "Financial Risk Meter based on expectiles," IRTG 1792 Discussion Papers 2021-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Shakya, Shasta, 2022. "Geographic networks and spillovers between banks," Journal of Corporate Finance, Elsevier, vol. 77(C).
    6. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    7. Ioana Alexopoulou & Irina Bunda & Annalisa Ferrando, 2010. "Determinants of Government Bond Spreads in New EU Countries," Eastern European Economics, Taylor & Francis Journals, vol. 48(5), pages 5-37, September.
    8. Kevin F. Kiernan & Vladimir Yankov & Filip Zikes, 2021. "Liquidity Provision and Co-insurance in Bank Syndicates," Finance and Economics Discussion Series 2021-060, Board of Governors of the Federal Reserve System (U.S.).
    9. Hasan, Iftekhar & Kim, Suk-Joong & Politsidis, Panagiotis N. & Wu, Eliza, 2021. "Loan syndication under Basel II: How do firm credit ratings affect the cost of credit?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    10. Huang, Lele & Zhao, Junlong & Wang, Huiwen & Wang, Siyang, 2016. "Robust shrinkage estimation and selection for functional multiple linear model through LAD loss," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 384-400.
    11. Jaros{l}aw Gruszka & Janusz Szwabi'nski, 2023. "Portfolio Optimisation via the Heston Model Calibrated to Real Asset Data," Papers 2302.01816, arXiv.org.
    12. Lenka Zbonakova & Wolfgang Karl Härdle & Weining Wang, 2016. "Time Varying Quantile Lasso," SFB 649 Discussion Papers SFB649DP2016-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Dimic, Nebojsa & Piljak, Vanja & Swinkels, Laurens & Vulanovic, Milos, 2021. "The structure and degree of dependence in government bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    14. Förster, Marcel & Jorra, Markus & Tillmann, Peter, 2014. "The dynamics of international capital flows: Results from a dynamic hierarchical factor model," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 101-124.
    15. Cappelletti, Giuseppe & Mistrulli, Paolo Emilio, 2023. "The role of credit lines and multiple lending in financial contagion and systemic events," Journal of Financial Stability, Elsevier, vol. 67(C).
    16. Yudistira Permana & Saiqa Akbar & Anisa Nurpita, 2022. "Systemic risk and the financial network system: an experimental investigation," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 631-651, December.
    17. Hasan, Iftekhar & Politsidis, Panagiotis N. & Sharma, Zenu, 2021. "Global syndicated lending during the COVID-19 pandemic," Journal of Banking & Finance, Elsevier, vol. 133(C).
    18. Susanna Saroyan & Lilit Popoyan, 2017. "Bank-sovereign ties against interbank market integration: the case of the Italian segment," LEM Papers Series 2017/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    19. Marc van Kralingen & Diego Garlaschelli & Karolina Scholtus & Iman van Lelyveld, 2020. "Crowded trades, market clustering, and price instability," Papers 2002.03319, arXiv.org.
    20. Qian, Qian & Chao, Xiangrui & Feng, Hairong, 2023. "Internal or external control? How to respond to credit risk contagion in complex enterprises network," International Review of Financial Analysis, Elsevier, vol. 87(C).

    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2102.05398. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.