Laura González-Vila Puchades

Dr. Laura González-Vila Puchades

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 February 2012 in Fuzzy Sets and Systems
Verified
Journal Article
Closed access
JAJorge de Andrés-Sánchez
Jorge de Andrés-Sánchez
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades

8 June 2023 in European Research on Management and Business Economics
Verified
Journal Article
Open access
JAJorge De Andrés-Sánchez
Jorge De Andrés-Sánchez
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades

1 January 2019 in International Journal of Computational Intelligence Systems
Verified
Journal Article
Open access
JAJorge de Andrés-Sánchez
Jorge de Andrés-Sánchez
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades

1 January 2017 in Insurance: Mathematics and Economics
Verified
Journal Article
Open access
JAJorge de Andrés-Sánchez
Jorge de Andrés-Sánchez
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades
This paper extends the framework for the valuation of life insurance policies and annuities by Andrés- Sánchez and González-Vila (2012, 2014) in two ways. First we allow various uncertain magnitudes to be estimated by means of fuzzy numbers. This applies not only to interest rates but also to the amounts to be paid out by the insurance company. Second, the use of symmetrical triangular fuzzy numbers allows us to obtain expressions for the pricing of life contingencies and their variability that are closely linked to standard financial and actuarial mathematics. Moreover, they are relatively straightforward to compute and understand from a standard actuarial point of view

9 February 2021 in Mathematics
Verified
Journal Article
Open access
PDF file
PVPablo J. Villacorta
Pablo J. Villacorta
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades
Jorge de Andrés-SánchezJorge de Andrés-Sánchez
Jorge de Andrés-Sánchez
Markov chains (MCs) are widely used to model a great deal of financial and actuarial problems. Likewise, they are also used in many other fields ranging from economics, management, agricultural sciences, engineering or informatics to medicine. This paper focuses on the use of MCs for the design of non-life bonus-malus systems (BMSs). It proposes quantifying the uncertainty of transition probabilities in BMSs by using fuzzy numbers (FNs). To do so, Fuzzy MCs (FMCs) as defined by Buckley and Eslami in 2002 are used, thus giving rise to the concept of Fuzzy BMSs (FBMSs). More concretely, we describe in detail the common BMS where the number of claims follows a Poisson distribution under the hypothesis that its characteristic parameter is not a real but a triangular FN (TFN). Moreover, we reflect on how to fit that parameter by using several fuzzy data analysis tools and discuss the goodness of triangular approximates to fuzzy transition probabilities, the fuzzy stationary state, and the fuzzy mean asymptotic premium. The use of FMCs in a BMS allows obtaining not only point estimates of all these variables, but also a structured set of their possible values whose reliability is given by means of a possibility measure. Although our analysis is circumscribed to non-life insurance, all of its findings can easily be extended to any of the abovementioned fields with slight modifications.

28 November 2021 in The Geneva Papers on Risk and Insurance - Issues and Practice
Verified
Journal Article
Closed access
PDF file
Jorge de Andrés-SánchezJorge de Andrés-Sánchez
Jorge de Andrés-Sánchez
Laura González-Vila PuchadesLaura González-Vila Puchades
Laura González-Vila Puchades
Mario Arias-OlivaMario Arias-Oliva
Mario Arias-Oliva