Person: Vilar Zanón, José Luis
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First Name
José Luis
Last Name
Vilar Zanón
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Económicas y Empresariales
Department
Economía Financiera, Actuarial y Estadística
Area
Economía Financiera y Contabilidad
Identifiers
6 results
Search Results
Now showing 1 - 6 of 6
Item On the evaluation of the asymptotic fairness of bonus-malus systems(2001) Heras Martínez, Antonio José; Vilar Zanón, José Luis; Gil Fana, José AntonioIn this paper we try to evaluate the asymptotic fairness of bonus-malus systems, assuming the simplest case when there is no hunger for bonus.The asymptotic fairness has to be understood as the bonus-malus systemability in assessing the individual risks in the long run (see Lemaire[1995] p.xvi). Firstly we de…ne the asymptotic fairness of a bonus-malussystem following an expression that can be found in Lemaire [1985] p.168. Secondly, we de…ne a measure of the global asymptotic fairness considering the structure function of the risk group. Finally we try to calculate, for each set of transition rules and a given structure function,the scale of premiums that brings the global asymptotic fairness closest to the ideal situation where each insured pays in the long run a premium corresponding to its own claim frequency. This is possible thanks to the application of a multiobjective optimization technique named Goal Programing. We give an example illustrating the fact that the ideal case could be fairly well approached.Item Linear Goal Programming and experience rating(2001) Heras Martínez, Antonio José; Vilar Zanón, José Luis; Gil Fana, José Antonio; García Pineda, María PilarThis paper is devoted to the explanation of a new methodology in bonus malus system design, capable of taking into account very well known theoretical conditions like fairness and …nancial equilibrium of the portfolio, in addition to market conditions that could …t the resulting scale of premiums into competitive commercial settings. This is done through the resolution of a classical Bayesian decision problem, by means of minimization of the absolute error instead of the classical quadratic error. It is at this stage that we apply Goal Programming methods, which are linear thanks to the equivalence between the minimization of the absolute error and the minimization of the sum of some deviation variables which have a natural interpretation as rating errors. We show in an example how does the new methodology work. All the linear programs have been solved using the simplex method.Item Using rough sets to predict insolvency of Spanish non-life insurance companies(2003) Segovia Vargas, María Jesús; Gil Fana, José Antonio; Heras Martínez, Antonio José; Vilar Zanón, José Luis; Sanchís Arellano, AliciaInsolvency of insurance companies has been a concern of several parties stemmed from the perceived need to protect the general public and to try to minimize the costs associated to this problem such as the effects on state insurance guaranty funds or the responsibilities for management and auditors. Most methods applied in the past to predict business failure in insurance companies are techniques of statistical nature and use financial ratios as explicative variables. These variables do not normally satisfy statistical assumptions so we propose an approach to predict insolvency of insurance companies based on Rough Set Theory. Some of the advantages of this approach are: first, it is a useful tool to analyse information systems representing knowledge gained by experience; second, elimination of the redundant variables is got, so we can focus on minimal subsets of variables to evaluate insolvency and the cost of the decision making process and time employed by the decision maker are reduced; third, a model consisted of a set of easily understandable decision rules is produced and it is not necessary the interpretation of an expert and, fourth, these rules based on the experience are well supported by a set of real examples so this allows the argumentation of the decisions we make. This study completes previous researches for bankruptcy prediction based on Rough Set Theory developing a prediction model for Spanish non-life insurance companies and using general financial ratios as well as those that are specifically proposed for evaluating insolvency of insurance sector. The results are very encouraging in comparison with discriminant analysis and show that Rough Set Theory can be a useful tool for parties interested in evaluating insolvency of an insurance firm.Item Claims counts and stable distributions(2008) Vilar Zanón, José Luis; Heras Martínez, Antonio José; Gil Fana, José AntonioCómo se usan las distribuciones estables para garantiza el ajuste de la función de estructura de una cartera de pólizas de seguros.Item Estudio del comportamiento de la probabilidad de ruina en un caso de reaseguro cuota-parte con varias subcarteras(1992) Gil Fana, José Antonio; Heras Martínez, Antonio José; Vilar Zanón, José LuisEn el seno de la empresa aseguradora se realizan diversas actividades cuyo comportamiento determina el resultado de cada ejercicio. Considerando únicamente la actividad puramente aseguradora, esto es, el que podemos denominar negocio de seguros en sentido estricto (cobro de primas - pago de siniestros), entendemos que el riesgo para la empresa de seguros estriba en la posibilidad de que una siniestralidad excesiva le impida hacer frente a las obligaciones derivadas de los siniestros, esto es, caiga en un estado de insolvencia.Item Predicción de insolvencias con el método Rough Set(2003) Segovia Vargas, María Jesús; Gil Fana, José Antonio; Heras Martínez, Antonio José; Vilar Zanón, José LuisLa detección precoz de la insolvencia de una empresa interesa tanto para proteger al público en general como para minimizar los costes económicos y sociales asociados a este problema. Se han aplicado numerosos métodos estadísticos para afrontar este problema utilizando como variables explicativas los ratios financieros. Estas variables no suelen cumplir las hipótesis estadísticas que requieren estos métodos. En consecuencia, hemos aplicado la metodología Rough Set para la predicción de la insolvencia sobre una muestra de empresas españolas. Esta metodología presenta, entre otras, estas ventajas: 1) es útil para analizar sistemas de información que representan el conocimiento adquirido por la experiencia, 2) elimina las variables redundantes reduciendo el coste, en tiempo y dinero, del proceso de decisión, 3) se obtienen unas reglas de decisión fácilmente comprensibles que no necesitan interpretación de ningún experto y, 4) las reglas están bien justificadas por extraerse de ejemplos reales lo que justificaría las decisiones que en base a ellas se tomen. Los resultados muestran cómo esta metodología se adapta muy bien a los problemas de clasificación con atributos múltiples aplicándola a la detección de la insolvencia como problema de clasificación entre empresas sanas y fracasadas, y utilizando como atributos los ratios financieros.