The Use of Monte Carlo Method to Model the Aggregate Loss Distribution

Based on Law Number 24 of 2011, a state program was established to provide social protection and welfare for everyone, one of which is health insurance by the Social Insurance Administration Organization (BPJS). In its implementation, several important evaluations are needed. One that requires accurate evaluation is claim frequency and claim severity in determining premiums and reserved funds. This thesis provides one form of a method for selecting the distribution of claim frequency and claim severity. The data used in this study was taken from BPJS Health in the City of Tangerang in 2017. The distribution of opportunities chosen had been adjusted to the participant's claim data and parameter estimated using the Maximum Likelihood Estimation method. The chi-square test was used to check the goodness of fit for claim frequency distributions whereas the Anderson Darling tests were applied to claim severity distributions. The results of the chi-square test and the Anderson-Darling test showed that the model that matched the claim frequency distribution was the Z12M–NBGE distribution while the model that matched the claim severity was lognormal. The Z12M–NBGE distribution and the lognormal formed the aggregate loss distribution using the Monte Carlo method. Furthermore, the simulation results were obtained to the measurement of the Value in Risk (VaR) and Shortfall Expectations (ES). So, the Monte Carlo method is simple to implement the aggregate loss distributions and can easily handle various risks with dependency.

The Stability of the Aggregate Loss Distribution

In this article we introduce the stability analysis of a compound sum: it consists of computing the standardized variation of the survival function of the sum resulting from an infinitesimal perturbation of the common distribution of the summands. Stability analysis is complementary to the classical sensitivity analysis, which consists of computing the derivative of an important indicator of the model, with respect to a model parameter. We obtain a computational formula for this stability from the saddlepoint approximation. We apply the formula to the compound Poisson insurer loss with gamma individual claim amounts and to the compound geometric loss with Weibull individual claim amounts.

CMPH: a multivariate phase-type aggregate loss distribution

We introduce a compound multivariate distribution designed for modeling insurance losses arising from different risk sources in insurance companies. The distribution is based on a discrete-time Markov Chain and generalizes the multivariate compound negative binomial distribution, which is widely used for modeling insurance losses.We derive fundamental properties of the distribution and discuss computational aspects facilitating calculations of risk measures of the aggregate loss, as well as allocations of the aggregate loss to individual types of risk sources. Explicit formulas for the joint moment generating function and the joint moments of different loss types are derived, and recursive formulas for calculating the joint distributions given. Several special cases of particular interest are analyzed. An illustrative numerical example is provided.

MODEL COMPOUNDS DALAM MENGHITUNG AGGREGATE LOSS

Penelitian ini bertujuan untuk menentukan taksiran distribusi aggregate loss. Dalam hal ini, aggregate loss merupakan total kerugian dalam periode satu tahun yang dialami oleh pemegang polis yang ditanggung suatu perusahaan asuransi. Dalam menentukan taksiran fungsi peluang aggregate loss, akan dibentuk model distribusi compound. Untuk menyelesaikan distribusi compound, ada beberapa metode yang digunakan antara lain metode Panjer Recursion, Fourier Inversion, dan Fast Fourier Transform. COMPOUNDS MODEL TO DETERMINE AGGREGATE LOSSABSTRACTThis study aims to determine the estimated aggregate loss distribution. In this case, the aggregate loss is a total loss within one year period experienced by the policyholder who paid an insurance company. In determining the estimated aggregate loss function , will be established distribution model compound. To determine the compound distribution, there are several methods used, namely, Panjer Recursion, Fourier Inversion, dan Fast Fourier Transform.

Medidas de riesgo para riesgo operacional con un modelo de perdida agregada de Poisson-Lindley

En este trabajo se considera la determinación de medidas de riesgo en riesgo operacional, es decir, la determinación de cuantiles de alto orden. Se considera la aproximación basada en la distribución de la pérdida dentro de la aproximación avanzada. Se calculan, y se comparan entre si, las medidas de riesgo a partir de la distribución de la pérdida agregada y a partir de la distribución predictiva considerando como funciones estructura para los perfiles de riesgo las distribuciones Triangular y Gamma.<br /><br />This paper considers the determination of the risk measures in Operational Risk, i.e. the determination of a high level quantile. The Loss Distribution Approach in the Advanced Measurement Approach is adopted. The risk measures, obtained from the aggregate loss distribution and from the predictive distribution are determined and compared, using the Triangular and Gamma distributions as structure functions of the risk profiles.<br />

Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling

The idea of using common Poisson shock processes to model dependent event frequencies is well known in the reliability literature. In this paper we examine these models in the context of insurance loss modelling and credit risk modelling. To do this we set up a very general common shock framework for losses of a number of different types that allows for both dependence in loss frequencies across types and dependence in loss severities. Our aims are threefold: to demonstrate that the common shock model is a very natural way of approaching the modelling of dependent losses in an insurance or risk management context; to provide a summary of some analytical results concerning the nature of the dependence implied by the common shock specification; to examine the aggregate loss distribution that results from the model and its sensitivity to the specification of the model parameters.

Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling

The idea of using common Poisson shock processes to model dependent event frequencies is well known in the reliability literature. In this paper we examine these models in the context of insurance loss modelling and credit risk modelling. To do this we set up a very general common shock framework for losses of a number of different types that allows for both dependence in loss frequencies across types and dependence in loss severities. Our aims are threefold: to demonstrate that the common shock model is a very natural way of approaching the modelling of dependent losses in an insurance or risk management context; to provide a summary of some analytical results concerning the nature of the dependence implied by the common shock specification; to examine the aggregate loss distribution that results from the model and its sensitivity to the specification of the model parameters.