Multi-state models for evaluating conversion options in life insurance

In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the ageing effect. The value of the conversion option is evaluated after the formal description of this contract.Comment: Published at http://dx.doi.org/10.15559/17-VMSTA78 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/

Kernel Density Estimation of Actuarial Loss Functions.

No abstractLoss models; Transformation; Skewness; Weighted integrated squared error

An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data

[EN] An algorithm to fit regression models aimed at predicted the average responses beyond a conditional quantile level is presented. This procedure is implemented in a case study of insured drivers covering almost 10,000. The aim is to predict the expected yearly distance driven above the posted speed limits as a function of driving patterns such as total distance, urban and night percent driven. Gender and age are also controlled. Results are analyzed for the median and the top decile. The conclusions provide evidence of factors influencing speed limit violations for risky drivers and they are interesting to price motor insurance and implement road safety policies. The efficiency of the algorithm to fit tail expectation regression is compared to quantile regression. Computational time doubles for tail expectation regression compared to quantile regression. Standard errors are estimated via bootstrap methods. Further considerations regarding in-sample predictive performance are discussed. In particular, further restrictions should be imposed in the model specification to avoid prediction outside the plausible rangePitarque, A.; Guillen, M. (2020). An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data. Editorial Universitat Politècnica de València. 51-58. https://doi.org/10.4995/CARMA2020.2020.11512OCS515

Time-varying effects when analysing customer lifetime duration, application to the insurance market

The Cox model (Cox, 1972) is widely used in customer lifetime duration research, but it assumes that the regression coefficients are time invariant. In order to analyse the temporal covariate effects on the duration times, we propose to use an extended version of the Cox model where the parameters are allowed to vary over time. We apply this methodology to real insurance policy cancellation data and we conclude that the kind of contracts held by the customer and the concurrence of an external insurer in the cancellation influence the risk of the customer leaving the company, but the effect differs as time goes by.Cox model, customer lifetime.

El aprendizaje dialógico Transformación de la escuela en el marco de la implementación de comunidades de aprendizaje en Ecuador

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Formación de Profesorado y Educación, Departamento de Didáctica y Teoría de la Educación. Fecha de lectura: 04-10-2019Esta tesis tiene embargado el acceso al texto completo hasta el 04-04-202

Transformation kernel density estimation of actuarial loss functions

A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward. An application to insurance data and the calculation of the value-at-risk are presented.non-parametric methods, heavy-tailed distributions, value at risk