Journal: European Actuarial Journal

Abbreviation

Eur. Actuar. J.

Publisher

Springer

Journal Volumes

ISSN

2190-9733
2190-9741

Description

Filters

2011 - 2019132020 - 202510
Reset filters

Search Results

Publications 1 - 10 of 23
  • Estimation error and bootstrapping in the chain-ladder model of Mack
    Item type: Journal Article
    Gisler, Alois (2021)
    European Actuarial Journal
    In 2006 there was quite some discussion on how to estimate theconditionalestimation error in the chain-ladder (CL) model of Mack. Buchwalder, Buhlmann, Merz and Wuthrich (BBMW) (ASTIN Bull 36(2):521-542, 2006) proposed another estimator than the one derived by Mack (ASTIN Bull 23(2):213-225, 1993). These two estimators are also found in a broader context by new authors in recent papers. In the present paper we examine the theoretical properties of the two estimators and come to the conclusion that the BBMW estimator has some major deficiencies compared with the Mack estimator. It takes much less information of the observed triangle into account, the averaging is done over inappropriate sets and it does not properly fit to the Mack CL-model.
  • Feature extraction from telematics car driving heatmaps
    Item type: Journal Article
    Gao, Guangyuan; Wüthrich, Mario V. (2018)
    European Actuarial Journal
    Insurance companies have started to collect high-frequency GPS car driving data to analyze the driving styles of their policyholders. In previous work, we have introduced speed and acceleration heatmaps. These heatmaps were categorized with the K-means algorithm to differentiate varying driving styles. In many situations it is useful to have low-dimensional continuous representations instead of unordered categories. In the present work we use singular value decomposition and bottleneck neural networks (autoencoders) for principal component analysis. We show that a two-dimensional representation is sufficient to re-construct the heatmaps with high accuracy (measured by Kullback–Leibler divergences).
  • Making Tweedie’s compound Poisson model more accessible
    Item type: Journal Article
    Delong, Łukasz; Lindholm, Mathias; Wüthrich, Mario V. (2021)
    European Actuarial Journal
    The most commonly used regression model in general insurance pricing is the compound Poisson model with gamma claim sizes. There are two different parametrizations for this model: the Poisson-gamma parametrization and Tweedie’s compound Poisson parametrization. Insurance industry typically prefers the Poisson-gamma parametrization. We review both parametrizations, provide new results that help to lower computational costs for Tweedie’s compound Poisson parameter estimation within generalized linear models, and we provide evidence supporting the industry preference for the Poisson-gamma parametrization.
  • Parameter reduction in log-normal chain-ladder models
    Item type: Journal Article
    Wüthrich, Mario V.; Verrall, Richard J. (2015)
    European Actuarial Journal
  • Machine learning techniques for mortality modeling
    Item type: Journal Article
    Deprez, Philippe; Shevchenko, Pavel V.; Wüthrich, Mario V. (2017)
    European Actuarial Journal
  • Modeling accounting year dependence in runoff triangles
    Item type: Journal Article
    Salzmann, Robert; Wüthrich, Mario V. (2012)
    European Actuarial Journal
  • Covariate selection from telematics car driving data
    Item type: Journal Article
    Wüthrich, Mario V. (2017)
    European Actuarial Journal
    Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing.
  • Model selection with Gini indices under auto-calibration
    Item type: Journal Article
    Wüthrich, Mario V. (2023)
    European Actuarial Journal
    The Gini index does not give a strictly consistent scoring function. Therefore, simply maximizing the Gini index may lead to a wrong model choice. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for strictly consistent scoring if we restrict it to the class of auto-calibrated regression models. That is, on the class of auto-calibrated models we know that the true model maximizes the Gini index.
  • Neural networks applied to chain–ladder reserving
    Item type: Journal Article
    Wüthrich, Mario V. (2018)
    European Actuarial Journal
    Classical claims reserving methods act on so-called claims reserving triangles which are aggregated insurance portfolios. A crucial assumption in classical claims reserving is that these aggregated portfolios are sufficiently homogeneous so that a coarse reserving algorithm can be applied. We start from such a coarse reserving method, which in our case is Mack’s chain–ladder method, and show how this approach can be refined for heterogeneity and individual claims feature information using neural networks.
  • Insurance: models, digitalization, and data science
    Item type: Journal Article
    Albrecher, Hansjörg; Bommier, Antoine; Filipović, Damir; et al. (2019)
    European Actuarial Journal
Publications 1 - 10 of 23