Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T12:10:48.146Z Has data issue: false hasContentIssue false

PRICING OF CYBER INSURANCE CONTRACTS IN A NETWORK MODEL

Published online by Cambridge University Press:  25 July 2018

Matthias A. Fahrenwaldt
Affiliation:
Department of Actuarial Mathematics & Statistics, Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK E-Mail: [email protected]
Stefan Weber*
Affiliation:
Institut für Mathematische Stochastik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany
Kerstin Weske
Affiliation:
Institut für Mathematische Stochastik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany E-Mail: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We develop a novel approach for pricing cyber insurance contracts. The considered cyber threats, such as viruses and worms, diffuse in a structured data network. The spread of the cyber infection is modeled by an interacting Markov chain. Conditional on the underlying infection, the occurrence and size of claims are described by a marked point process. We introduce and analyze a new polynomial approximation of claims together with a mean-field approach that allows to compute aggregate expected losses and prices of cyber insurance. Numerical case studies demonstrate the impact of the network topology and indicate that higher order approximations are indispensable for the analysis of non-linear claims.

Type
Research Article
Copyright
Copyright © Astin Bulletin 2018 

References

Bezuidenhout, C. and Grimmett, G. (1990) The critical contact process dies out. The Annals of Probability, 18 (4), 14621482.Google Scholar
Bielecki, T.R. and Rutkowski, M. (2004) Credit Risk: Modeling, Valuation and Hedging. Springer Finance. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Boguñá, M. and Pastor-Satorras, R. (2002) Epidemic spreading in correlated complex networks. Physical Review E, 66, 047104.Google Scholar
Böhme, R. (2005) Cyber-insurance revisited. 4th Annual Workshop on the Economics of Information Security, WEIS 2005, Harvard University, Cambridge, MA, USA, June 1-3, 2005.Google Scholar
Bolot, J. and Lelarge, M. (2008) A new perspective on internet security using insurance. In IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, pp. 7175. Piscataway, NY: The Institute of Electrical and Electronics Engineers.Google Scholar
Brémaud, P. (1981) Point Processes and Queues: Martingale Dynamics. Springer Series in Statistics. New York, Heidelberg, Berlin: Springer-Verlag.Google Scholar
Burnecki, K., Janczura, J. and Weron, R. (2011) Building loss models. In Statistical Tools for Finance and Insurance (eds. Cizek, P., Härdle, W.K. and Weron, R.), pp. 293328. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Cator, E. and Van Mieghem, P. (2012) Second-order mean-field susceptible-infected-susceptible epidemic threshold. Physical Review E, 85 (5), 056111.Google Scholar
Cator, E. and Van Mieghem, P. (2014) Nodal infection in Markovian susceptible-infected-susceptible and susceptible-infected-removed epidemics on networks are non-negatively correlated. Physical Review E, 89 (5), 052802.Google Scholar
Durrett, R. and Liu, X.-F. (1988) The contact process on a finite set. The Annals of Probability, 16 (3), 11581173.Google Scholar
Gandel, S. (2015) Lloyd's CEO: Cyber attacks cost companies $400 billion every year. Fortune. 23 January 2015. Available at: http://fortune.com/2015/01/23/cyber-attack-insurance-lloyds/.Google Scholar
Giesecke, K. and Weber, S. (2004) Cyclical correlations, credit contagion, and portfolio losses. Journal of Banking and Finance, 28 (12), 30093036.Google Scholar
Giesecke, K. and Weber, S. (2006) Credit contagion and aggregate losses. Journal of Economic Dynamics and Control, 30 (5), 741767.Google Scholar
Hille, E. (1968) Lectures on Ordinary Differential Equations. Addison-Wesley Series in Mathematics. Reading, MA: Addison-Wesley Pub. Co.Google Scholar
Jacobsen, M. (2006) Point Process Theory and Applications: Marked Point and Piecewise Deterministic Processes. Probability and Its Applications. Boston: Birkhäuser.Google Scholar
Jacod, J. (1975) Multivariate point processes: Predictable projection, radon-nikodym derivatives, representation of martingales. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 31 (3), 235253.Google Scholar
Last, G. and Brandt, A. (1995) Marked Point Processes on the Real Line: The Dynamic Approach. Probability and its Applications. New York: Springer-Verlag.Google Scholar
Liggett, T.M. (1985) Interacting Particle Systems. Grundlehren der Mathematischen Wissenschaften, Vol. 276. New York: Springer-Verlag.Google Scholar
Liggett, T.M. (1999) Stochastic Interacting Systems: Contact, Voter and Exclusion Processes. Grundlehren der Mathematischen Wissenschaften, Vol. 324. Berlin: Springer-Verlag.Google Scholar
Marotta, A., Martinelli, F., Nanni, S. and Yautsiukhin, A. (2015) A survey on cyber-insurance. Technical Rep. IIT TR-17/2015. Istituto di Informatica e Telematica, Consiglio Nazionale delle Richerce, Pisa.Google Scholar
Mata, A.S. and Ferreira, S.C. (2013) Pair quenched mean-field theory for the susceptible-infected-susceptible model on complex networks. Europhysics Letters, 103 (4), 48003.Google Scholar
Matkowski, J. (2012) Mean-value theorem for vector-valued functions. Mathematica Bohemica, 137 (4), 415423.Google Scholar
Mountford, T., Mourrat, J.-C., Valesin, D. and Yao, Q. (2016) Exponential extinction time of the contact process on finite graphs. Stochastic Processes and their Applications, 126 (7), 19742013.Google Scholar
Nowzari, C., Preciado, V.M. and Pappas, G.J. (2016) Analysis and control of epidemics: A survey of spreading processes on complex networks. IEEE Control Systems Magazine, 36 (1), 2646.Google Scholar
Pastor-Satorras, R. and Vespignani, A. (2001) Epidemic dynamics and endemic states in complex networks. Physical Review E, 63, 066117.Google Scholar
Pastor-Satorras, R., Castellano, C., Van Mieghem, P. and Vespignani, A. (2015) Epidemic processes in complex networks. Reviews of modern physics, 87 (3), 925.Google Scholar
Protter, P.E. (2004) Stochastic Integration and Differential Equations. Applications of Mathematics, Vol. 21. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Schwartz, G.A. and Sastry, S.S. (2014) Cyber-insurance framework for large scale interdependent networks. In Proceedings of the 3rd International Conference on High Confidence Networked Systems, pp. 145154. New York: The Association for Computing Machinery.Google Scholar
Swiss Re Institute (2017) Cyber: Getting to grips with a complex risk. Sigma, 2017 (1), 140.Google Scholar
Swiss Re/IBM (2016) Cyber: In search of resilience in an interconnected world. Expertise Publication. Available at: http://www.swissre.com/library/archive/Demand_for_cyber_insurance_on_the_rise_joint_Swiss_Re_IBM_study_shows.html.Google Scholar
Taylor, M.E. (2011) Partial Differential Equations I: Basic Theory. Applied Mathematical Sciences, Vol. 115, 2nd ed. New York: Springer-Verlag.Google Scholar
Teschl, G. (2012) Ordinary Differential Equations and Dynamical Systems. Graduate Studies in Mathematics, Vol. 140. Providence: American Mathematical Society.Google Scholar
Van Mieghem, P. (2011) The N-intertwined SIS epidemic network model. Computing, 93 (2–4), 147169.Google Scholar
Van Mieghem, P., Omic, J. and Kooij, R. (2009) Virus spread in networks. IEEE/ACM Transactions on Networking, 17 (1), 114.Google Scholar
Van Mieghem, P. and van de Bovenkamp, R. (2015) Accuracy criterion for the mean-field approximation in susceptible-infected-susceptible epidemics on networks. Physical Review E, 91 (3), 032812.Google Scholar