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Statistical Modelling of Downside Risk Spillovers

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  • Daniel Felix Ahelegbey

    (University of Pavia)

Abstract

We extend the extreme downside hedge methodology to model sensitivity interconnectedness of market returns to the tail risk of other markets under turbulent conditions. We derive the interconnectedness via Bayesian graph structural learning. The empirical application examines the dynamic interconnectedness among 15 major markets, including G10 economies, during turbulent times. We investigate whether downside risk connections among these major markets are merely anecdotal or provide evidence of contagion and the most central market for spillover propagation. The result shows that the Covid-19 induced downside risk connections record the highest density, suggesting stronger evidence of contagion in the coronavirus pandemic than during the financial and eurozone crisis. Central to the spillover propagation is the finding that most of the transmitters and recipients of downside risk are EU markets.

Suggested Citation

  • Daniel Felix Ahelegbey, 2020. "Statistical Modelling of Downside Risk Spillovers," DEM Working Papers Series 193, University of Pavia, Department of Economics and Management.
  • Handle: RePEc:pav:demwpp:demwp0193
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    References listed on IDEAS

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    1. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    2. Ahelegbey, Daniel Felix & Giudici, Paolo & Mojtahedi, Fatemeh, 2021. "Tail risk measurement in crypto-asset markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 333-376.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    7. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Erratum to Rejoinder on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 504-504.
    8. Battiston, Stefano & Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Stiglitz, Joseph E., 2012. "Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1121-1141.
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    10. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Rejoinder on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 418-426.
    11. Ahelegbey, Daniel Felix & Giudici, Paolo, 2022. "NetVIX — A network volatility index of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    12. Gang-Jin Wang & Shuyue Yi & Chi Xie & H. Eugene Stanley, 2021. "Multilayer information spillover networks: measuring interconnectedness of financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1163-1185, July.
    13. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
    14. Fatemeh Mojtahedi & Seyed Mojtaba Mojaverian & Daniel F. Ahelegbey & Paolo Giudici, 2020. "Tail Risk Transmission: A Study of the Iran Food Industry," Risks, MDPI, vol. 8(3), pages 1-17, July.
    15. Harris, Richard D.F. & Nguyen, Linh H. & Stoja, Evarist, 2019. "Systematic extreme downside risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 128-142.
    16. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    17. Chabi-Yo, Fousseni & Ruenzi, Stefan & Weigert, Florian, 2018. "Crash Sensitivity and the Cross Section of Expected Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1059-1100, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian Inference; Centrality; Contagion; Conditional VaR; Downside Risk; Extreme downside hedge; Financial Crises; Financial Networks.;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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