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 - 8 of 8

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.
Online product returns risk assessment and management
(TOP, 2017) Vilar Zanón, José Luis; Vilar, Eduardo; Heras Martínez, Antonio José
Commonly viewed as a cost center from an operations perspective, product returns have the potential to strongly influence operating margins and business profitability, thus constituting a risk for online retailers. This work addresses the problem of how to assess and manage product returns costs using a risk analysis methodology. Online product returns are seen as a random phenomenon that fluctuates in severity over time, threatening the profitability of the online store. Therefore, the starting point is that this risk can be modeled as a future random stream of payments. Given one or many future time periods, we aim to assess and manage this risk by answering two important questions: (1) Pricing—or what dollar amount factored on top of the current price of goods sold online would cover the cost of product returns, and (2) Reserving—or how much capital does an online retailer need to reserve at the beginning of each period to cover the cost of online product returns. We develop our analysis for one period (a month) by a closed formula model, and for multi-period (a year) by a dynamic simulation model. Risk measurements are executed in both cases to answer the two main questions above. We exemplify this methodology using an anonymized archival database of actual purchase and return history provided by a large size US women’s apparel online retailer.
A linear goal programming method to recover risk neutral probabilities from options prices by maximum entropy
(Decisions in Economics and Finance, 2019) Peraita Ezcurra, Olivia; Vilar Zanón, José Luis
We develop a new methodology to retrieve risk neutral probabilities (equivalent martingale measure) with maximum entropy from quoted option prices. We assume the no arbitrage hypothesis and model the efficient market hypothesis by means of a maximum entropic risk neutral distribution. The method is free of parametric assumption except for the simulation of the distribution support, for which purpose we can choose any stochastic model. Firstly, we innovate in the minimization of a different f-divergence than Kullback–Leibler’s relative entropy, resulting in the total variation distance. We minimize it by means of linear goal programming, thus guaranteeing a fast numerical resolution. The method values non-traded assets finding a RNP minimizing its f-divergence to the maximum entropy distribution over a simulated support—the uniform distribution—calibrated to the benchmarks prices constraints. Our second innovation is that in an incomplete market, we can increase the f-divergence from its minimum to obtain any asset price belonging to the interval satisfying the non-existence of an arbitrage portfolio, without presupposing any utility function for the decision maker. We exemplify our methodology by means of synthetic and real-world cases, showing that our methodology can either price non-traded assets or interpolate and extrapolate a volatility surface.
Pricing Illiquid Assets by Entropy Maximization Through Linear Goal Programming
(Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018) Peraita Ezcurra, Olivia; Vilar Zanón, José Luis
In this contribution we study the problem of retrieving a risk neutral probability (RNP) in an incomplete market, with the aim of pricing non-traded assets and hedging their risk. The pricing issue has been often addressed in the literature finding an RNP with maximum entropy by means of the minimization of the Kullback-Leibler divergence. Under this approach, the efficient market hypothesis is modelled by means of the maximum entropy RNP. This methodology consists of three steps: firstly simulating a finite number of market states of some underlying stochastic model, secondly choosing a set of assets—called benchmarks—with characteristics close to the given one, and thirdly calculating an RNP by means of the minimization of its divergence from the maximum entropy distribution over the simulated finite sample market states, i.e. from the uniform distribution. This maximum entropy RNP must exactly price the benchmarks by their quoted prices. Here we proceed in a different way consisting of the minimization of a different divergence resulting in the total variation distance. This is done by means of a two steps linear goal programming method. The calculation of the super-replicating portfolios (not supplied by the Kullback-Leibler approach) would then be derived as solutions of the dual linear programs.
Risk neutral valuation and linear programming
(2014) Vilar Zanón, José Luis; Peraita Ezcurra, Olivia
Gracias a la programación Lineal por metas podemos construir un método inverso para la valoración de activos.
Equilibrio entre tarificaciones a priori y a posteriori: sistemas Bonus Malus
(2013) Vilar Zanón, José Luis
El avance de las técnicas estadísticas aplicadas a la determinación a priori y a posteriori de las primas de pólizas de seguros es motivo de un replanteamiento acerca de la misión de ambas fases de la tarificación en seguros generales.
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.