IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i9p165-d1242949.html
   My bibliography  Save this article

Cyber Risk Contagion

Author

Listed:
  • Arianna Agosto

    (Department of Economics and Management, University of Pavia, Via San Felice 5, 27100 Pavia, Italy
    These authors contributed equally to this work.)

  • Paolo Giudici

    (Department of Economics and Management, University of Pavia, Via San Felice 5, 27100 Pavia, Italy
    These authors contributed equally to this work.)

Abstract

Financial technologies (fintechs) are continuously expanding, across different markets and financial services. While financial technologies bring many opportunities, such as reduced costs and extended inclusion, they also bring risks, among which include cyber risks, that are difficult to measure. One of the difficulties that arise in the measurement of cyber risks is the interdependence among cyber losses, a problem that has not yet been solved. To fill the gap, this paper proposes a multivariate model for cyber risks, based on their observed time series of counts. The time-varying intensity parameter of the model determines the probability that a cyber attack occurs, and its specification takes not only time but also sectorial interdependence into account. The effectiveness of the proposed model is demonstrated by means of a real cyber loss dataset, in which there exists time and sectorial dependence among different events.

Suggested Citation

  • Arianna Agosto & Paolo Giudici, 2023. "Cyber Risk Contagion," Risks, MDPI, vol. 11(9), pages 1-10, September.
  • Handle: RePEc:gam:jrisks:v:11:y:2023:i:9:p:165-:d:1242949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/9/165/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/11/9/165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
    2. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 355-372, July.
    3. Emanuel Kopp & Lincoln Kaffenberger & Christopher Wilson, 2017. "Cyber Risk, Market Failures, and Financial Stability," IMF Working Papers 2017/185, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    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. Escribano, Ana & Maggi, Mario, 2019. "Intersectoral default contagion: A multivariate Poisson autoregression analysis," Economic Modelling, Elsevier, vol. 82(C), pages 376-400.
    2. José Ramón Martínez Resano, 2022. "Digital resilience and financial stability. The quest for policy tools in the financial sector," Financial Stability Review, Banco de España, issue Autumn.
    3. Xiao, Tim, 2018. "The Valuation of Credit Default Swap with Counterparty Risk and Collateralization," EconStor Preprints 203447, ZBW - Leibniz Information Centre for Economics.
    4. Marco Lo Duca & Diego Moccero & Fabio Parlapiano, 2024. "The impact of macroeconomic and monetary policy shocks on the default risk of the euro-area corporate sector," Temi di discussione (Economic working papers) 1460, Bank of Italy, Economic Research and International Relations Area.
    5. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. repec:hum:wpaper:sfb649dp2008-047 is not listed on IDEAS
    7. Xiao,Tim, 2018. "Pricing Financial Derivatives Subject to Multilateral Credit Risk and Collateralization," EconStor Preprints 202075, ZBW - Leibniz Information Centre for Economics.
    8. Silvia Facchinetti & Paolo Giudici & Silvia Angela Osmetti, 2020. "Cyber risk measurement with ordinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 173-185, March.
    9. Eugenio J. Miravete, 2009. "Competing with Menus of Tariff Options," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 188-205, March.
    10. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    11. Aldasoro, Iñaki & Gambacorta, Leonardo & Giudici, Paolo & Leach, Thomas, 2022. "The drivers of cyber risk," Journal of Financial Stability, Elsevier, vol. 60(C).
    12. Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2018. "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print hal-04590149, HAL.
    13. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    14. Paolo Giudici & Laura Parisi, 2019. "Bail-In or Bail-Out? Correlation Networks to Measure the Systemic Implications of Bank Resolution," Risks, MDPI, vol. 7(1), pages 1-25, January.
    15. Xing, Kai & Yang, Xiaoguang, 2020. "Predicting default rates by capturing critical transitions in the macroeconomic system," Finance Research Letters, Elsevier, vol. 32(C).
    16. Sim, Jaehun & Kim, Chae-Soo, 2019. "The value of renewable energy research and development investments with default consideration," Renewable Energy, Elsevier, vol. 143(C), pages 530-539.
    17. Berger, Allen N. & Curti, Filippo & Mihov, Atanas & Sedunov, John, 2022. "Operational Risk is More Systemic than You Think: Evidence from U.S. Bank Holding Companies," Journal of Banking & Finance, Elsevier, vol. 143(C).
    18. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    19. Claudiu Ioan Negrea, 2022. "Can Cyber Risk Affect Financial Stability?," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 368-376, September.
    20. Jungmu Kim, 2019. "The Effect of Systematic Default Risk on Credit Risk Premiums," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    21. Nguyen, Ha, 2023. "An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 103-121.

    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:jrisks:v:11:y:2023:i:9:p:165-:d:1242949. 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.