Loss reserving using loss aversion functions

Loss reserving using loss aversion functions

This article discusses the determination of risk capital based on “aversion” functions. Aversion functions weigh different outcomes according to perceived severity. Many practical and popular risk measures are usefully viewed in terms of aversion functions including those arising from distortion operators and risk margin loadings. The approach of this paper builds on, unifies,and extends existi...

Dependent Loss Reserving Using Copulas

Modeling the dependence among multiple loss triangles has important implications for the determination of loss reserves, a critical element of risk management and capital allocation practices of property-casualty insurers. In this article, we propose a copula regression model for dependent lines of business that can be used to predict unpaid losses and hence determine loss reserves. The propose...

Using Expected Loss Ratios in Reserving

Myopic Loss Aversion Revisited

In this paper we reexamine several experimental papers on myopic loss aversion by analyzing individual rather than aggregate choice patterns. We find that the behavior of the majority of subjects is inconsistent with the hypothesis of myopic loss aversion.

Artificial Neural Networks in Insurance Loss Reserving

In this paper we analyse insurance data using Artificial Neural Networks (ANN)[1]. In particular, we use ANN for the problem of Loss Reserving. Loss reserving is the practice of estimating the future payments for the claims which have occurred on an insurance portfolio. A difficulty in forecasting future payments is that the time series of payments often depends on influences that are not obser...