Person:
Vilar Zanón, José Luis

First Name

José Luis

Last Name

Vilar Zanón

URI

https://hdl.handle.net/20.500.14352/74818

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Económicas y Empresariales

Department

Economía Financiera, Actuarial y Estadística

Area

Economía Financiera y Contabilidad

Identifiers

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Now showing 1 - 10 of 22

Ciencia y seguros: el sugimiento de la ciencia actuarial
(2016) Vilar Zanón, José Luis
La Ciencia Actuarial, como todas las ciencias, atraviesa una serie de periodos hasta su afirmación final como disciplina científica conocida bajo ese nombre.
Modelización estocástica de los requisitos de capital de solvencia II por el riesgo de caídas de cartera para un seguro de vida de larga duración
(Anales Del Instituto De Actuarios Españoles, 2017) Vilar Zanón, José Luis; Gil Fana, José Antonio; Dylewska, Ewa; Heras Martínez, Antonio José
La observación de la estructura de los requisitos de capital de Solvencia II para un ejemplo de un seguro de vida y supervivencia indica que el elemento principal del sub-módulo de riesgo de suscripción de vida se corresponde con el riesgo de caídas de cartera. Por lo tanto, la primera tarea en la optimización de los requisitos de capital consiste en la búsqueda de posibles reducciones de requisitos de capital correspondientes a este riesgo. Los resultados del análisis implican que para productos similares al estudiado la fórmula estándar puede ser demasiado onerosa respecto a los requisitos de capital por riesgo de cartera.
Linear Goal Programming and experience rating
(2001) Heras Martínez, Antonio José; Vilar Zanón, José Luis; Gil Fana, José Antonio; García Pineda, María Pilar
This 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.
An application of two-stage quantile regression to insurance ratemaking
(Scandinavian Actuarial Journal, 2018) Heras Martínez, Antonio José; Moreno, Ignacio; Vilar Zanón, José Luis
Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.
Matemática actuarial no vida. Modelos, medición del riesgo y solvencia: curso en diapositivas. 2024. MCAF-UCM
(2023) Vilar Zanón, José Luis
Dotar a l@s alumn@s de las herramientas de modelización estocástica en seguros: • Modelización: número de siniestros, heterogeneidad de la cartera, cuantías de los siniestros, valor extremo, cálculo numérico de distribuciones compuestas, aproximaciones. • Tarificación: los principios para el cálculo de primas y los sistemas de tarificación a priori y a posteriori. • Medidas del riesgo: propiedades, principios para el cálculo de primas, distintos enfoques para definir medidas de riesgo, capital de solvencia para afrontar una operación de seguro. Expresiones analíticas y estimaciones muestrales de medidas de riesgos. • Ordenación de riesgos: distintos enfoques para definir órdenes estocásticos y preferencias sobre riesgos. • Reaseguro: modelización y distintas modalidades. Reaseguro óptimo con varias subcarteras. • Solvencia y provisiones técnicas con especial énfasis en la provisión de prestaciones bajo el enfoque de Solvencia II. Métodos Chain-Ladder.
Climate Change and Crop Insurance: Geographical Heterogeneity in Hailstorm Risk for Wine Grapes in Spain
(2024) Zhou Nan; Vilar Zanón, José Luis
Climate change presents significant challenges to the insurance industry, specially in sectors reliant on consistent climatic conditions such as agriculture. This paper investigates the impact of climate change on agricultural insurance, with a specific focus on wine grape crop insurance in Spain. We employ the provincial Spanish Actuarial Climate Index (pSACI) to analyze geographical heterogeneities in climate-related risks, highlighting the necessity for customized insurance strategies to address the increasing frequency and severity of extreme weather events, such as hailstorms. Our evaluation of spatial heterogeneity in climatic impacts utilizes Generalized Linear Mixed Models (GLMMs) and Linear Quantile Mixed Models (LQMMs). The findings reveal a significant positive correlation between pSACI and both the frequency and severity of hailstorm-related losses, emphasizing the value of pSACI inhancing regional risk assessments. These results advocate for the integration of localized climate indices into insurance risk frameworks, thereby improving the accuracy of premium and solvency capital calculations, and promoting adaptive risk management strategies.
Predicción de insolvencias con el método Rough Set
(2003) Segovia Vargas, María Jesús; Gil Fana, José Antonio; Heras Martínez, Antonio José; Vilar Zanón, José Luis
La 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.
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, Alicia
Insolvency 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.
An average model approach to experience based premium rates discounts: an application to Spanish agricultural insurance
(European Actuarial Journal, 2020) De Frutos, Estela; Vilar Zanón, José Luis; Heras Martínez, Antonio José
We address some issues in agricultural insurance, describing drawbacks of the bonus-malus system (BMS) methodology used in Spain and many other EU countries. We develop an alternative experience based premium rate discount system taking into account the adverse years when high losses caused by extreme weather events happen. Our contribution consists of a two-step methodology. Firstly, we use tobit or Tweedie regressions to calculate yearly correction rates. Secondly, we calculate the mean of the correction rates. This average model acts as a bufer against adverse year losses. We compare three alternatives: our two resulting average models and the BMS operating in the Spanish line of business exemplifed—table grapes.
On the evaluation of the asymptotic fairness of bonus-malus systems
(2001) Heras Martínez, Antonio José; Vilar Zanón, José Luis; Gil Fana, José Antonio
In 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.