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引用次数: 0
摘要
网络风险的频繁发生及其严重的经济后果催生了网络保险市场的增长。作为保险定价的关键步骤,总体损失的计算近年来引起了广泛关注。本研究在树形网络结构中建立了一个基于路径的 K 世代风险传染模型,该模型纳入了源传染位置和安全等级异质性对传染概率和局部损失的影响,区别于大多数现有模型。此外,我们还利用贝叶斯网络(BN)中的 d-separation 概念讨论了多路径间的代际风险传染特性,并推导出单条路径上局部损失的均值和方差的明确表达式。结合这些结果,我们计算出了整个网络直至时间 $t$ 的总体损失的均值和方差值,这对于准确的网络保险定价至关重要。最后,通过数值计算和相关概率特性,我们获得了一些对风险管理者和保险公司有价值的发现。
A novel k-generation propagation model for cyber risk and its application to cyber insurance
The frequent occurrence of cyber risks and their serious economic
consequences have created a growth market for cyber insurance. The calculation
of aggregate losses, an essential step in insurance pricing, has attracted
considerable attention in recent years. This research develops a path-based
k-generation risk contagion model in a tree-shaped network structure that
incorporates the impact of the origin contagion location and the heterogeneity
of security levels on contagion probability and local loss, distinguishing it
from most existing models. Furthermore, we discuss the properties of
k-generation risk contagion among multi-paths using the concept of d-separation
in Bayesian network (BN), and derive explicit expressions for the mean and
variance of local loss on a single path. By combining these results, we compute
the mean and variance values for aggregate loss across the entire network until
time $t$, which is crucial for accurate cyber insurance pricing. Finally,
through numerical calculations and relevant probability properties, we have
obtained several findings that are valuable to risk managers and insurers.