IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i5p213-d551263.html
   My bibliography  Save this article

Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities

Author

Listed:
  • Tomaso Aste

    (Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK
    UCL Centre for Blockchain Technologies, University College London, London WC1E 6BT, UK
    Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A 2AE, UK)

Abstract

Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach.

Suggested Citation

  • Tomaso Aste, 2021. "Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities," JRFM, MDPI, vol. 14(5), pages 1-17, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:213-:d:551263
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/5/213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/5/213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. de Bandt, O. & Bruneau, C. & El Amri, W., 2008. "Stress testing and corporate finance," Journal of Financial Stability, Elsevier, vol. 4(3), pages 258-274, September.
    2. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    3. Pier Francesco Procacci & Tomaso Aste, 2019. "Forecasting market states," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1491-1498, September.
    4. Reinaldo B. Arellano-Valle & Javier E. Contreras-Reyes & Marc G. Genton, 2013. "Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(1), pages 42-62, March.
    5. Fabio Caccioli & Paolo Barucca & Teruyoshi Kobayashi, 2018. "Network models of financial systemic risk: a review," Journal of Computational Social Science, Springer, vol. 1(1), pages 81-114, January.
    6. 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.
    7. Younes Bensalah, 2000. "Steps in Applying Extreme Value Theory to Finance: A Review," Staff Working Papers 00-20, Bank of Canada.
    8. Pier Francesco Procacci & Tomaso Aste, 2018. "Forecasting market states," Papers 1807.05836, arXiv.org, revised May 2019.
    9. Birch, Annika & Aste, Tomaso, 2014. "Systemic losses due to counterparty risk in a stylized banking system," LSE Research Online Documents on Economics 57700, London School of Economics and Political Science, LSE Library.
    10. Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
      • Tobias Adrian & Markus K. Brunnermeier, 2011. "CoVaR," NBER Working Papers 17454, National Bureau of Economic Research, Inc.
    11. Nikola Tarashev & Claudio Borio & Kostas Tsatsaronis, 2009. "The systemic importance of financial institutions," BIS Quarterly Review, Bank for International Settlements, September.
    12. Sachapon Tungsong & Fabio Caccioli & Tomaso Aste, 2017. "Relation between regional uncertainty spillovers in the global banking system," Papers 1702.05944, arXiv.org.
    13. Francesca Biagini & Jean‐Pierre Fouque & Marco Frittelli & Thilo Meyer‐Brandis, 2019. "A unified approach to systemic risk measures via acceptance sets," Mathematical Finance, Wiley Blackwell, vol. 29(1), pages 329-367, January.
    14. Barfuss, Wolfram & Massara, Guido Previde & Di Matteo, T. & Aste, Tomaso, 2016. "Parsimonious modeling with information filtering networks," LSE Research Online Documents on Economics 68860, London School of Economics and Political Science, LSE Library.
    15. Pritsker, Matt, 2019. "An overview of regulatory stress-testing and steps to improve it," Global Finance Journal, Elsevier, vol. 39(C), pages 39-43.
    16. Longin, Francois M., 2000. "From value at risk to stress testing: The extreme value approach," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1097-1130, July.
    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. Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024. "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, vol. 129(C).

    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. Tomaso Aste, 2020. "Stress testing and systemic risk measures using multivariate conditional probability," Papers 2004.06420, arXiv.org, revised May 2021.
    2. Ebrahimi Kahou, Mahdi & Lehar, Alfred, 2017. "Macroprudential policy: A review," Journal of Financial Stability, Elsevier, vol. 29(C), pages 92-105.
    3. Yun, Tae-Sub & Jeong, Deokjong & Park, Sunyoung, 2019. "“Too central to fail” systemic risk measure using PageRank algorithm," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 251-272.
    4. Isobel Seabrook & Fabio Caccioli & Tomaso Aste, 2021. "An Information Filtering approach to stress testing: an application to FTSE markets," Papers 2106.08778, arXiv.org.
    5. Qin, Xiao & Zhou, Chen, 2021. "Systemic risk allocation using the asymptotic marginal expected shortfall," Journal of Banking & Finance, Elsevier, vol. 126(C).
    6. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    7. Chang, Carolyn W. & Li, Xiaodan & Lin, Edward M.H. & Yu, Min-Teh, 2018. "Systemic risk, interconnectedness, and non-core activities in Taiwan insurance industry," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 273-284.
    8. Kabundi, Alain & De Simone, Francisco Nadal, 2020. "Monetary policy and systemic risk-taking in the euro area banking sector," Economic Modelling, Elsevier, vol. 91(C), pages 736-758.
    9. Ahelegbey, Daniel Felix & Giudici, Paolo & Hashem, Shatha Qamhieh, 2021. "Network VAR models to measure financial contagion," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    10. Robert F. Engle & Emil N. Siriwardane, 2018. "Structural GARCH: The Volatility-Leverage Connection," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 449-492.
    11. Shimizu, Katsutoshi & Ly, Kim Cuong, 2017. "Were regulatory interventions effective in lowering systemic risk during the financial crisis in Japan?," Journal of Multinational Financial Management, Elsevier, vol. 41(C), pages 80-91.
    12. Borri, Nicola, 2018. "Local currency systemic risk," Emerging Markets Review, Elsevier, vol. 34(C), pages 111-123.
    13. Morelli, David & Vioto, Davide, 2020. "Assessing the contribution of China’s financial sectors to systemic risk," Journal of Financial Stability, Elsevier, vol. 50(C).
    14. Zhang, Weiping & Zhuang, Xintian & Wang, Jian & Lu, Yang, 2020. "Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    16. Hwang, Inchang & Xu, Simon & In, Francis & Kim, Tong Suk, 2017. "Systemic risk and cross-sectional hedge fund returns," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 109-130.
    17. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    18. Abendschein, Michael & Grundke, Peter, 2018. "On the ranking consistency of global systemic risk measures: empirical evidence," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181623, Verein für Socialpolitik / German Economic Association.
    19. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    20. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.

    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:gam:jjrfmx:v:14:y:2021:i:5:p:213-:d:551263. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.