Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

Peters, Gareth W.; Targino, Rodrigo S.; Wüthrich, Mario V.

doi:10.2139/ssrn.2961888

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…mortality) and model market / credit risk stochastically. Peters et al [17] provide a Bayesian framework with a focus on coherent capital allocation that is compliant with the Swiss Solvency Test. Ha and Bauer [9] frame the estimation problem via a loss operator that maps future payoffs to the conditional expected value at the risk horizon to obtain an optimal choice for the basis functions in the regression.…”

Section: The Stochastic Risk Modelmentioning

confidence: 99%

Quantifying Life Insurance Risk using Least-Squares Monte Carlo

Baumgart,

Krebs,

Lempertseder

et al. 2019

Preprint

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This article presents a stochastic framework to quantify the biometric risk of an insurance portfolio in solvency regimes such as Solvency II or the Swiss Solvency Test (SST). The main difficulty in this context constitutes in the proper representation of long term risks in the profit-loss distribution over a one year horizon. This will be resolved by using least-squares Monte Carlo methods to quantify the impact of new experience on the annual re-valuation of the portfolio. Therefore our stochastic model can be seen as an example for an internal model, as allowed under Solvency II or the SST. Since our model does not rely upon nested simulations it is computationally fast and easy to implement.

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Section: The Stochastic Risk Modelmentioning

confidence: 99%

Quantifying Life Insurance Risk using Least-Squares Monte Carlo

Baumgart,

Krebs,

Lempertseder

et al. 2019

Preprint

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“…Using simulation methodology to determine the distribution of the total costs of claims is very beneficial in the field of insurance risk management [36]. Nath and Das [15], Asmussen [37], and Peters et al [38] have applied MC simulation in insurance analyses. According to Hahn [39], insurance companies act as the institutional investors in the financial system of a country and risk dispersion is an important segment of their business.…”

Section: Development Of the MC Simulation Modelmentioning

confidence: 99%

Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

Kakhki

Freeman

Mosher

2018

Safety

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Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R2 = 0.79). Injury characteristics and worker’s occupation were predictive of large claims’ occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers’ compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.

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“…Zhang & Dukic (2013) performs full Bayesian inference for models defined through several combinations of copulas and marginal distributions and Avanzi et al (2016) explores a multivariate Tweedie family of models. Peters et al (2017a) and (Gao, 2018, Chapter 6) study the inferential procedure of models with multiple triangles coupled through a copula. The first performs Bayesian inference for the marginals and assume the dependence structure is fully determined by the regulator.…”

Section: Introductionmentioning

confidence: 99%

Modelling dependence within and across run-off triangles for claims reserving

Nieto‐Barajas¹,

Targino²

2019

Preprint

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We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is of autoregressive form of order p and is achieved through the use of latent variables. We carry out bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of businesses or companies, through the use of hierarchical priors.

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Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

Cited by 4 publications

References 39 publications

Quantifying Life Insurance Risk using Least-Squares Monte Carlo

Quantifying Life Insurance Risk using Least-Squares Monte Carlo

Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

Modelling dependence within and across run-off triangles for claims reserving

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