Type II bivariate Pólya–Aeppli distribution

In this paper, we introduce the Type II bivariate Pólya–Aeppli distribution as a compound Poisson distribution with bivariate geometric compounding distribution. The probability mass function, recursion formulas, conditional distributions and some other properties are then derived for this distribution.     

On signed normal-Poisson approximations

For lattice distributions a convolution of two signed Poisson measures proves to be an approximation comparable with the normal law. It enables to get rid of cumbersome summands in asymptotic expansions and to obtain estimates for all Borel sets. Asymptotics can be constructed two-ways: by adding summands to the leading term or by adding summands in its exponent. The choice of approximations is confirmed by the Ibragimov-type necessary and sufficient conditions. Received: 20 November 1996 / Revised version: 5 December 1997     

过离散次数分布模型的尾部特征

在保险精算和生物统计等领域,离散型次数分布模型的应用十分广泛.当实际数据的尾部较长(即过离散),且零点的概率较大时,许多模型的拟合效果往往欠佳.本文通过计算概率之比的极限和偏度系数,对混合泊松分布和复合泊松分布的右尾特征和零点概率进行了比较,给出了它们的尾部排列顺序,以及尾部长短与零点概率的关系,从而为模型的构造或选择提供了一种指导.本文最后应用一组实际数据说明了在构造或选择次数分布模型时如何考虑尾部特征,从而改善对实际数据的拟合效果.     

Bounds on compound distributions and stop-loss premiums

In the actuarial literature a lot of attention is given to the approximation of aggregate claims distributions by compound Poisson and Polya distributions and their subsequent numerical evaluation. The present contribution derives bounds for the tail of compound distributions and stop-loss premiums. The bounds are obtained in an elementary manner based on a version of the Chebyshev inequality. The main point of this contribution consists in deriving bounds with explicit dependence on the distribution function itself as well as on some partial moments of it.     

Joint distributions of some actuarial random vectors for the Cox risk model

The main purpose of this paper was to investigate the joint distributions of some actuarial vectors that contain the ruin time for the Cox risk model. Joint distributions of some actuarial vectors such as those containing the ruin time, the maximum surplus before ruin, duration of the surplus being negative, and others are important for measuring the risk management level and the severity caused by ruin. In the past decade, great literatures have devoted to the study of these distributions for classical models, such as the compound Poisson model and the perturbed compound Poisson model etc. The main result of this paper provides the joint distributions of these actuarial vectors for the Cox risk model—a model with wide applications in risk theory. The main method of this paper is to apply the idea of ‘operational time scale’ to the Cox model, which enables us to solve our problem by intergrading some existing results for the compound Poisson risk model. To some extent, we can view our work as an extension of joint distributions of some actuarial vectors for the compound Poisson risk model to the ones for the Cox risk model. Copyright © 2011 John Wiley & Sons, Ltd.     

Risk aggregation in multivariate dependent Pareto distributions

In this paper we obtain closed expressions for the probability distribution function of aggregated risks with multivariate dependent Pareto distributions. We work with the dependent multivariate Pareto type II proposed by Arnold (1983, 2015), which is widely used in insurance and risk analysis. We begin with an individual risk model, where the probability density function corresponds to a second kind beta distribution, obtaining the VaR, TVaR and several other tail risk measures. Then, we consider a collective risk model based on dependence, where several general properties are studied. We study in detail some relevant collective models with Poisson, negative binomial and logarithmic distributions as primary distributions. In the collective Pareto–Poisson model, the probability density function is a function of the Kummer confluent hypergeometric function, and the density of the Pareto–negative binomial is a function of the Gauss hypergeometric function. Using data based on one-year vehicle insurance policies taken out in 2004–2005 (Jong and Heller, 2008) we conclude that our collective dependent models outperform other collective models considered in the actuarial literature in terms of AIC and CAIC statistics.     

风险非同质性保单组合赔付额的计算

对于保单组合赔付次数及赔付额的计算,是非寿险精算研究的一项基本内容.讨论了非同质风险下的保单组合,在赔付次数采用混合泊松分布拟合时的两种情况下赔付额分布的计算,给出了相应的迭代公式.     

Decompounding random sums: a nonparametric approach

A compound distribution is the distribution of a random sum, which consists of a random number N of independent identically distributed summands, independent of N. Buchmann and Grübel (Ann Stat 31:1054–1074, 2003) considered decompounding a compound Poisson distribution, i.e. given observations on a random sum when N has a Poisson distribution, they constructed a nonparametric plug-in estimator of the underlying summand distribution. This approach is extended here to that of general (but known) distributions for N. Asymptotic normality of the proposed estimator is established, and bootstrap methods are used to provide confidence bounds. Finally, practical implementation is discussed, and tested on simulated data. In particular we show how recursion formulae can be inverted for the Panjer class in general, as well as for an example drawn from the Willmot class.     

An expansion in the exponent for compound binomial approximations

The purpose of this paper is two-fold. First, we introduce a new asymptotic expansion in the exponent for the compound binomial approximation of the generalized Poisson binomial distribution. The dependence of its accuracy on the symmetry and shifting of distributions is investigated. Second, for compound binomial and compound Poisson distributions, we present new smoothness estimates, some of which contain explicit constants. Finally, the ideas used in this paper enable us to prove new precise bounds in the compound Poisson approximation. Published in Lietuvos Matematikos Rinkinys, Vol. 46, No. 1, pp. 67–110, January–March, 2006.     

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??Traditional claims reserve approaches are all based on aggregated data and usually produce inaccurate projections of the reserve because the aggregated data make a great loss of information contained in individual claims. Thus, the researcher in actuarial science developed the so-called individual claim models that are based on marked Poisson processes. However, due to the inappropriateness of Poisson distribution in modelling the claims distributions, the present paper propose marked Cox processes as reserve models. Compared with the aggregate claims models, the models proposed in the current paper take more sufficient use of information contained in data and can be expected to produce more accurate evaluations in claim loss reserving.     

Explicit ruin formulas for models with dependence among risks

We show that a simple mixing idea allows one to establish a number of explicit formulas for ruin probabilities and related quantities in collective risk models with dependence among claim sizes and among claim inter-occurrence times. Examples include compound Poisson risk models with completely monotone marginal claim size distributions that are dependent according to Archimedean survival copulas as well as renewal risk models with dependent inter-occurrence times.     

An application of the Morgenstern family with standard two‐sided power and gamma marginal distributions to the Bayes premium in the collective risk model

The Bayes premium is a quantity of interest in the actuarial collective risk model, under which certain hypotheses are assumed. The usual assumption of independence among risk profiles is very convenient from a computational point of view but is not always realistic. Recently, several authors in the field of actuarial and operational risks have examined the incorporation of some dependence in their models. In this paper, we approach this topic by using and developing a Farlie–Gumbel–Morgenstern (FGM) family of prior distributions with specified marginals given by standard two‐sided power and gamma distributions. An alternative Poisson–Lindley distribution is also used to model the count data as the number of claims. For the model considered, closed expressions of the main quantities of interest are obtained, which permit us to investigate the behavior of the Bayes premium under the dependence structure adopted (Farlie–Gumbel–Morgenstern) when the independence case is included. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributional analysis of a generalization of the Polya process

A nonhomogeneous birth process generalizing the Polya process is analyzed, and the distribution of the transition probabilities is shown to be the convolution of a negative binomial distribution and a compound Poisson distribution, whose secondary distribution is a mixture of zero-truncated geometric distributions. A simplified form of the secondary distribution is obtained when the transition intensities have a particular structure, and may sometimes be expressed in terms of Stirling numbers and special functions such as the incomplete gamma function, the incomplete beta function, and the exponential integral. Conditions under which the compound Poisson form of the marginal distributions may be improved to a geometric mixture are also given.     

The compound Poisson risk model with dependence under a multi-layer dividend strategy

In this paper, a compound Poisson risk model with time-dependent claims is studied under a multi-layer dividend strategy. A piecewise integro-differential equation for the Gerber-Shiu function is derived and solved. Asymptotic formulas of the ruin probability are obtained when the claim size distributions are heavy-tailed.     

On the Lagrangian Katz family of distributions as a claim frequency model

The Panjer (Katz) family of distributions is defined by a particular first-order recursion which is built on the basis of two parameters. It is known to characterize the Poisson, negative binomial and binomial distributions. In insurance, its main usefulness is to yield a simple recursive algorithm for the aggregate claims distribution. The present paper is concerned with the more general Lagrangian Katz family of distributions. That family satisfies an extended recursion which now depends on three parameters. To begin with, this recursion is derived through a certain first-crossing problem and two applications in risk theory are described. The distributions covered by the recursion are then identified as the generalized Poisson, generalized negative binomial and binomial distributions. A few other properties of the family are pointed out, including the index of dispersion, an extended Panjer algorithm for compound sums and the asymptotic tail behaviour. Finally, the relevance of the family is illustrated with several data sets on the frequency of car accidents.     

Error bounds in approximations of random sums using gamma-type operators

In this work we deal with approximations of compound distributions, that is, distribution functions of random sums. More specifically, we obtain a discrete compound distribution by replacing each summand in the initial random sum by a discrete random variable whose probability mass function is related to a well-known inversion formula for Laplace transforms [cf. Feller, W., 1971. An Introduction to Probability Theory and its Applications, vol. II, second edn. Wiley, New York]. Our aim is to show the advantages that this method has in the context of compound distributions. In particular we give accurate error bounds for the distance between the initial random sum and its approximation when the individual summands are mixtures of gamma distributions.     

欧式双向期权的两种定价比较

在股票价格服从泊松跳模型下,分别利用保险精算方法与无套利定价方法给出了欧式双向期权的定价公式;通过对这两种结果的比较发现,当股票价格服从特定的泊松跳模型时两种定价公式是相同的.     

On the Distribution of a Sum of Sarmanov Distributed Random Variables

Built from given marginals with a flexible dependency structure, Sarmanov’s family of multivariate distributions became of interest in various fields. In this paper, we present some formulas for the density of the sum of several random variables joined by Sarmanov’s distribution, with accent on the particular case of exponentially distributed marginals. Such results are useful in solving, e.g., financial and actuarial problems.     

Distributions of the location of maximuma and minimuma for diffusions with jumps

The paper deals with methods of computation of distributions of location for maxima and minima for diffusions with jumps. As an example, we obtain explicit formulas for distributions of location for the maximum of the process which is equal to the sum of a Brownian motion and the compound Poisson process. Bibliography: 8 titles.     

TVaR-based capital allocation for multivariate compound distributions with positive continuous claim amounts

In this paper, we consider a portfolio of n dependent risks X₁,…,X_n and we study the stochastic behavior of the aggregate claim amount S=X₁+?+X_n. Our objective is to determine the amount of economic capital needed for the whole portfolio and to compute the amount of capital to be allocated to each risk X₁,…,X_n. To do so, we use a top-down approach. For (X₁,…,X_n), we consider risk models based on multivariate compound distributions defined with a multivariate counting distribution. We use the TVaR to evaluate the total capital requirement of the portfolio based on the distribution of S, and we use the TVaR-based capital allocation method to quantify the contribution of each risk. To simplify the presentation, the claim amounts are assumed to be continuously distributed. For multivariate compound distributions with continuous claim amounts, we provide general formulas for the cumulative distribution function of S, for the TVaR of S and the contribution to each risk. We obtain closed-form expressions for those quantities for multivariate compound distributions with gamma and mixed Erlang claim amounts. Finally, we treat in detail the multivariate compound Poisson distribution case. Numerical examples are provided in order to examine the impact of the dependence relation on the TVaR of S, the contribution to each risk of the portfolio, and the benefit of the aggregation of several risks.