Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view

The passing of Directive 2009/138/CE (Solvency II) has opened a new era in the European insurance market. According to this new regulatory environment, the volume of own resources will be determined depending on the risks that any insurer would be holding. So, nowadays, the model to estimate the amount of economic capital is one of the most important elements. The Directive establishes that the European entities can use a general model to perform these tasks. However, this situation is far from being optimal because the calibration of the general model has been made using figures that reflects and average behaviour. This paper shows that not all the companies operating in a specific market has the same risk profile. For this reason, it is unsatisfactory to use a general model for all of them. We use the PAM clustering method and afterwards some Bayesian tools to check the results previously obtained. Analysed data (public information belonging to Spanish insurance companies about balance sheets and income statements from 1998 to 2007) comes from the DGSFP (Spanish insurance regulator).Solvency II, PAM, Longitudinal multinomial model

Profile identification via weighted related metric scaling : an application to dependent Spanish children

AMS subject classification: 62-07, 62-09, 62H20, 62H99, 62P05Disability and dependency (lack of autonomy in performing common everyday actions) affect health status and quality of life, therefore they are significant public health issues. The main purpose of this study is to establish the existing relationship among different variables (continuous, categorical and binary) referred to children between 3 and 6 years old and their functional dependence in basic activities of daily living. We combine different types of information via weighted related metric scaling to obtain homogeneous profiles for dependent Spanish children. The redundant information between groups of variables is modeled with an interaction parameter that can be optimized according to several criteria. In this paper, the goal is to obtain maximum explained variability in an Euclidean configuration. Data comes from the Survey about Disabilities, Personal Autonomy and Dependence Situations, EDAD 2008, (Spanish National Institute of Statistics, 2008)This work has been partially supported by Spanish grant MTM2010-17323 (Spanish Ministry of Science and Innovation

Non-linear models of disability and age applied to census data

It is usually considered that the proportion of handicapped people grows with age. Namely, the older the man/woman is, the more level of disability he/she suffers. However, empirical evidence shows that this assessment is not always true, or at least, it is not true in the Spanish population. This study tries to assess the impact of age on disability in Spain. It is divided into three different parts. The first one is focused in describing the way disability is measured in this work. We used a former index defined by the authors that distinguishes between men and women. The second one is focused in a literature review about the methods used in this paper. This section emphasizes on local regression, feed forward neural networks and BARS. Finally, in the last section estimations are undertaken. Several methods are used and, therefore, there are fairly differences in the results, not only among the methodologies, but also between genders

Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view

The passing of Directive 2009/138/CE (Solvency II) has opened a new era in the European insurance market. According to this new regulatory environment, the volume of own resources will be determined depending on the risks that any insurer would be holding. So, nowadays, the model to estimate the amount of economic capital is one of the most important elements. The Directive establishes that the European entities can use a general model to perform these tasks. However, this situation is far from being optimal because the calibration of the general model has been made using figures that reflects and average behaviour. This paper shows that not all the companies operating in a specific market has the same risk profile. For this reason, it is unsatisfactory to use a general model for all of them. We use the PAM clustering method and afterwards some Bayesian tools to check the results previously obtained. Analysed data (public information belonging to Spanish insurance companies about balance sheets and income statements from 1998 to 2007) comes from the DGSFP (Spanish insurance regulator)

Estimating life expectancy free of dependency : group characterization through the proximity to the deepest dependency path

The aging of population is perhaps the most important problem that developed countries must face in the near future. Dependency can be seen as a consequence of the process of gradual aging. In a health context, this contingency is defined as a lack of autonomy in performing basic activities of daily living that requires the care of another person or significant help. In Europe in general and in Spain in particular this phenomena represents a problem with economic, political, social and demographic implications. The prevalence of dependency in the population, as well as its intensity and its evolution over the course of a person's life are issues of greatest importance that should be addressed. The aim of this work is to estimate life expectancy free of dependency (LEFD) using categorical data and individual dependency trajectories that are obtained using the whole medical history concerning the dependency situation of each individual from birth up to 2008, contained in database EDAD 2008. In particular, we estimate LEFD in several scenarios attending to gender, proximity-group and dependency degree. Proximity-groups are established according to an L2-type distance from the dependency trajectories to a central trend within each age-gender group, using functional data techniques. The main findings are: First, the estimated LEFD curves reach higher values for women than for men; Second, their decreasing rate is higher (and more abrupt) for men than for women; Third, the more the dependency trajectories depart from the central trend, the more the gap between the LEFD for major dependency and the other dependency situations widens; Finally, we show evidence that to estimate LEFD ignoring the partition by proximity-groups may lead to nonrepresentative LEFD estimates.Financial support from research project MTM2014-56535-R by the Spanish Ministry of Economy and Competitiveness