Nos derniers articles

This paper focuses on the development of #bayesian classification and regression tree (#cart) models for claims frequency modeling in non-life #insurance pricing. The authors propose the use of the zero-inflated #poisson distribution to address the issue of imbalanced claims data and introduce a general MCMC algorithm for posterior tree exploration. Additionally, the deviance information criterion (DIC) is used for model selection. The paper discusses the applicability of these models through simulations and real insurance data.

This paper discusses #decentralized#insurance and its various forms of #risksharing mechanisms developed worldwide. It highlights the need for a unified #mathematical framework to describe the commonalities and relationships between different forms of #peertopeer insurance. The framework allows for a comparison of existing practices and the design of hybrid and innovative #models .

This article applies the Insurance Performance Measure (IPM) model to a set of #indian#insurance companies over the period 2005-2016, which is the first study to apply this model on real industry data. The IPM was introduced as a better way to assess industry and company performance for insurance companies as traditional #financialaccounting analysis is not suitable for the unique format of insurance company financials. IPM incorporates #underwriting, investment, and #reinsurance along with a hurdle rate and is consistent with Warren Buffett's desire for a balanced overview of industry performance. The model could help in identifying the threshold limit for overall profitability and in negotiations for reinsurance renewals.Read

"We describe a simple model of #insurance demand that can be applied to the #propertyinsurance, #liabilityinsurance, #lifeinsurance, and #healthinsurance markets. We also demonstrate how #riskaversion affects a variety of real-life insurance decisions made under conditions of #uncertainty, including how much the market will bear to pay for insurance administrative expenses and how demand varies for different types of #autoinsurance#coverage.”

"... we revisit the study of an optimal risk management strategy for an insurer who wants to maximize the expected utility by purchasing reinsurance and managing reinsurance counterparty risk with a default-free hedging instrument, where the reinsurance premium is calculated by the expected value principle and the price of the hedging instrument equals to the expected payoff plus a proportional loading."

"Our model yields richer separating Nash equilibria than pure moral hazard and pure adverse selection models, although separating Nash equilibria may not exist in some cases. It also retains some properties, for example, no full insurance and the positive correlation between insurance coverage and risk type, in those benchmark models. Our study on comparative statics indicates that, under some conditions and with some exceptions, the optimal indemnity and premium decrease with disutility from effort, increase with potential loss, and decrease with the initial wealth of the insured."

This article offers a #riskmanagement framework specifically designed for the #insurance and #reinsurance industries to analyze the #capitalallocation of risk, capital budgeting, and capital structure decisions.

"After reviewing the main characteristics of cyber risk, we consider the three layers of cyber space: hardware, software and psycho-cognitive layer."

"... model uncertainty is a vital component of the current challenges in risk measurement, and therefore the regulator should design risk measures encouraging well-understood prudent decisions over (less understood) risky ones. From this perspective robust regulation should be a desirable goal. To achieve such an objective, simple – but not simpler – rules are needed."

"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."