What are the actual costs of cyber risk events?
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ejor.2018.07.021
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Liu, Feng & Pitt, David, 2017. "Application of bivariate negative binomial regression model in analysing insurance count data," Annals of Actuarial Science, Cambridge University Press, vol. 11(2), pages 390-411, September.
- Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
- Laengle, Sigifredo & Merigó, José M. & Miranda, Jaime & Słowiński, Roman & Bomze, Immanuel & Borgonovo, Emanuele & Dyson, Robert G. & Oliveira, José Fernando & Teunter, Ruud, 2017. "Forty years of the European Journal of Operational Research: A bibliometric overview," European Journal of Operational Research, Elsevier, vol. 262(3), pages 803-816.
- Eling, Martin, 2012. "Fitting insurance claims to skewed distributions: Are the skew-normal and skew-student good models?," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 239-248.
- Bolance, Catalina & Guillen, Montserrat & Pelican, Elena & Vernic, Raluca, 2008. "Skewed bivariate models and nonparametric estimation for the CTE risk measure," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 386-393, December.
- Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
- Cummins, J. David & Lewis, Christopher M. & Wei, Ran, 2006. "The market value impact of operational loss events for US banks and insurers," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2605-2634, October.
- Ganegoda, Amandha & Evans, John, 2013. "A scaling model for severity of operational losses using generalized additive models for location scale and shape (GAMLSS)," Annals of Actuarial Science, Cambridge University Press, vol. 7(1), pages 61-100, March.
- Christian Biener & Martin Eling & Jan Hendrik Wirfs, 2015.
"Insurability of Cyber Risk: An Empirical Analysis†,"
The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 40(1), pages 131-158, January.
- Biener, Christian & Eling, Martin & Wirfs, Jan Hendrik, 2015. "Insurability of Cyber Risk: An Empirical Analysis," Working Papers on Finance 1503, University of St. Gallen, School of Finance.
- Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
- Cannas, Giuseppina & Masala, Giovanni & Micocci, Marco, 2009. "Quantifying reputational effects for publicly traded financial institutions," Journal of Financial Transformation, Capco Institute, vol. 27, pages 76-81.
- Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
- Nagurney, Anna & Shukla, Shivani, 2017. "Multifirm models of cybersecurity investment competition vs. cooperation and network vulnerability," European Journal of Operational Research, Elsevier, vol. 260(2), pages 588-600.
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.- Eling, Martin & Wirfs, Jan Hendrik, 2016. "Cyber Risk: Too Big to Insure? Risk Transfer Options for a mercurial risk class," I.VW HSG Schriftenreihe, University of St.Gallen, Institute of Insurance Economics (I.VW-HSG), volume 59, number 59.
- Eling, Martin & Loperfido, Nicola, 2017. "Data breaches: Goodness of fit, pricing, and risk measurement," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 126-136.
- Matteo Malavasi & Gareth W. Peters & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang & Georgy Sofronov, 2021. "Cyber Risk Frequency, Severity and Insurance Viability," Papers 2111.03366, arXiv.org, revised Mar 2022.
- Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
- Martin Eling & Kwangmin Jung, 2022. "Heterogeneity in cyber loss severity and its impact on cyber risk measurement," Risk Management, Palgrave Macmillan, vol. 24(4), pages 273-297, December.
- Malavasi, Matteo & Peters, Gareth W. & Shevchenko, Pavel V. & Trück, Stefan & Jang, Jiwook & Sofronov, Georgy, 2022. "Cyber risk frequency, severity and insurance viability," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 90-114.
- Alexeev Vitali & Ignatieva Katja & Liyanage Thusitha, 2021. "Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-20, April.
- Uddin, Md Hamid & Mollah, Sabur & Islam, Nazrul & Ali, Md Hakim, 2023. "Does digital transformation matter for operational risk exposure?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
- Eckert, Christian & Gatzert, Nadine, 2017. "Modeling operational risk incorporating reputation risk: An integrated analysis for financial firms," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 122-137.
- Nadine Gatzert & Joan T. Schmit & Andreas Kolb, 2016. "Assessing the Risks of Insuring Reputation Risk," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 641-679, September.
- Hofert Marius & Memartoluie Amir & Saunders David & Wirjanto Tony, 2017. "Improved algorithms for computing worst Value-at-Risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 13-31, June.
- Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
- Eling, Martin, 2014. "Fitting asset returns to skewed distributions: Are the skew-normal and skew-student good models?," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 45-56.
- Zängerle, Daniel & Schiereck, Dirk, 2022. "Modelling and predicting enterprise‑level cyber risks in the context of sparse data availability," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136276, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
- Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
- Sturm, Philipp, 2013. "Operational and reputational risk in the European banking industry: The market reaction to operational risk events," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 191-206.
- Scholz, Roland W. & Czichos, Reiner & Parycek, Peter & Lampoltshammer, Thomas J., 2020. "Organizational vulnerability of digital threats: A first validation of an assessment method," European Journal of Operational Research, Elsevier, vol. 282(2), pages 627-643.
- Matteo Malavasi & Gareth W. Peters & Stefan Treuck & Pavel V. Shevchenko & Jiwook Jang & Georgy Sofronov, 2024. "Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk Classifications," Papers 2410.05297, arXiv.org.
- Di Lascio, F. Marta L. & Giammusso, Davide & Puccetti, Giovanni, 2018. "A clustering approach and a rule of thumb for risk aggregation," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 236-248.
- Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2012.
"Skew mixture models for loss distributions: A Bayesian approach,"
Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 617-623.
- Bernardi, Mauro & Maruotti, Antonello & Lea, Petrella, 2012. "Skew mixture models for loss distributions: a Bayesian approach," MPRA Paper 39826, University Library of Munich, Germany.
More about this item
Keywords
Risk analysis; Cyber risk; Operational risk; Risk management; Insurance;All these keywords.
Statistics
Access and download statisticsCorrections
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:eee:ejores:v:272:y:2019:i:3:p:1109-1119. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.