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.
    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. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    4. Xiao, Tim, 2018. "The Valuation of Credit Default Swap with Counterparty Risk and Collateralization," EconStor Preprints 203447, ZBW - Leibniz Information Centre for Economics.
    5. 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.
    6. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.
    7. Jaqueline Terra Moura Marins & Myrian Beatriz Eiras das Neves, 2013. "Inadimplência de Crédito e Ciclo Econômico: um exame da relação no mercado brasileiro de crédito corporativo," Working Papers Series 304, Central Bank of Brazil, Research Department.
    8. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    9. 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.
    10. repec:hum:wpaper:sfb649dp2008-047 is not listed on IDEAS
    11. Xiao,Tim, 2018. "Pricing Financial Derivatives Subject to Multilateral Credit Risk and Collateralization," EconStor Preprints 202075, ZBW - Leibniz Information Centre for Economics.
    12. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    13. 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.
    14. Serge Darolles & Patrick Gagliardini & Christian Gouriéroux, 2012. "Survival of Hedge Funds : Frailty vs Contagion," Working Papers 2012-36, Center for Research in Economics and Statistics.
    15. 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.
    16. White, Alan, 2018. "Pricing Credit Default Swap Subject to Counterparty Risk and Collateralization," MPRA Paper 85331, University Library of Munich, Germany.
    17. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Specification Testing in Hawkes Models," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 139-171.
    18. Dag Tjøstheim, 2012. "Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 413-438, September.
    19. 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.
    20. Gouriéroux, C. & Monfort, A. & Renne, J.P., 2014. "Pricing default events: Surprise, exogeneity and contagion," Journal of Econometrics, Elsevier, vol. 182(2), pages 397-411.
    21. Giesecke, Kay & Schwenkler, Gustavo, 2018. "Filtered likelihood for point processes," Journal of Econometrics, Elsevier, vol. 204(1), pages 33-53.

    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.