117 résultats pour « insurance »

Difference between integrated quantiles and integrated cumulative distribution functions.

"When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon."

Insurance and Enterprise: Cyber Insurance for Ransomware

"As businesses improved their resilience, cybercriminals adapted and ransoms escalated, calling insurability into question. Yet there remains little appetite for imposing restrictive conditionality in this highly competitive market. Instead, insurers have turned to governments to contain criminal threats and cushion catastrophic losses."

Multivariate Poisson Model Adjusting for Unidirectional Covariate Misrepresentation

"Insurance fraud has been a long-lasting issue in actuarial modeling. Policyholders are prone to hide their true status in their best interest when disclosing their information for insurance pricing purposes. However, from the insurers' point of view, it is either time-consuming or laborious to verify the true status of such risk factors. There is thus a strong incentive to build models accounting for potential misrepresentation, which contributes to a more robust ratemaking system."

Can We Nudge Insurance Demand by Bundling Natural Disaster Risks with Other Risks?

Date : Tags : , , , , ,
"Our findings show that demand is overall higher to insure separate risks than to cover all risks together in a bundled insurance policy in the UK, whereas no significant difference is found between demand for bundled insurance and single policy insurance in the Netherlands. This difference in preference across the two countries is partly associated with whether individuals have been flooded in the past, which is more often the case in the UK than the Netherlands."

Flood Risk Insurance: A Micro‑Economic Foundation

"... we characterize Pareto-optimal risk-sharing contracts in a market with multiple policyholders and one representative insurer. With minimal assumptions on the risk measures of the parties involved, we characterize Pareto optimality in terms of the minimization of a sum of the agents' risk positions, and we relate it to both the core and coalitional stability of an associated market game. In the special case of coherent risk measures, the optimal indemnity schedules are further characterized in explicit form, in terms of what can be called "worst-case probability measures". "

Modeling and Pricing Cyber Insurance -- A Survey

"We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and financial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions."