保险业未决赔款准备金的确认

未决赔款准备金是指保险公司为尚未结案的赔案而提取的准备金,包括已发生已报案未决赔款准备金、已发生未报案未决赔款准备金和理赔费用准备金。未决赔款准备金主     

保险业未决赔款准备金的确认

未决赔款准备金是指保险公司为尚未结案的赔案而提取的准备金，包括已发生已报案未决赔款准备金、已发生未报案未决赔款准备金和理赔费用准备金。未决赔款准备金主要产生于保险合同对约定的保险事故发生时可能承担的赔付责任，这种责任实际上是一种或有事项，而与此相关的未决赔款准备金则是一种预计负债，而非或有负债。     

准备金评估的随机性Munich链梯法及其改进——基于Bootstrap方法的实证分析

传统链梯法是未决赔款准备金评估最常用的确定性方法,Munich链梯法基于Mack模型的假设,利用已决赔款和已报案赔款的相关性调整进展因子,有效减少了链梯法分别基于已决赔款和已报案赔款得到的未决赔款准备金之间的差异。本文在系统介绍Munich链梯法的基础上,结合模型假设,提出了两种基于Bootstrap方法的随机性Munich链梯法,并通过精算实务中的数值实例应用R软件加以实证分析。本文的研究对保险公司准备金负债评估的准确性和充足性具有重要参考价值。     

我国产险业未决赔款准备金修饰盈余实证研究

2005年以前,产险公司根据财务制度提存未决赔款准备金,使公司极易通过提存未决赔款准备金的方式修饰盈余.因此,本文实证分析产险公司财务报表中未决赔款准备金数额与公司运营结果之间的关联性,揭示公司调整未决赔款准备金的动机.结果表明,在财务制度规定下,产险公司确实存在故意调整未决赔款准备金数字,以实现扩张盈余修饰空间的目的.     

产险公司赔款准备金误差动机研究

赔款准备金是财产责任保险公司资产负债表上最大的负债。赔款准备金估计的准确与否,直接影响到保险公司经营结果的评价,从而影响公司战略决策。本文探讨了财产责任保险公司赔款准备金调整的动机,包括税负、盈余平滑、财务状况、价格管制四个方面,对产险公司准确估计准备金有一定指导意义。     

财险公司未决赔款准备金盈余管理与稳健性实证研究

本文对我国财险公司未决赔款准备金的盈余管理与稳健性进行了实证分析。结果表明：在2001年至2011年期间,财险公司普遍利用未决赔款准备金的调整进行盈余平滑和避税;在2001年至2011年期间,财险公司的未决赔款准备金计提不具有条件稳健性特征;从盈余管理的科目来看,2007年之前,未决赔款准备金的盈余管理主要来自于已发生已报告准备金。     

非寿险业务理赔环节财务风险防范

理赔是保险公司经营活动的中心环节,对于非寿险业务,发生理赔的不确定性尤为突出,风险防范对于保险业的生存与发展显得极为重要。目前非寿险业务在赔款支出、预付赔付款、应收代位追偿款、损余物资、未决赔款准备金等方面存在较大的财务风险,针对这些关键风险点应采取相应的对策。     

贝叶斯非线性分层模型在多元索赔准备金评估中的应用

本文将贝叶斯非线性分层模型应用于基于不同业务线的多元索赔准备金评估中,设计了一种合适的模型结构,将非线性分层模型与贝叶斯方法结合起来,应用WinBUGS软件对精算实务中经典流量三角形数据进行建模分析,并使用MCMC方法得到了索赔准备金完整的预测分布。这种方法扩展并超越了已有多元评估方法中最佳估计和预测均方误差估计的研究范畴。在贝叶斯框架下结合后验分布实施推断对非寿险公司偿付能力监管和行业决策具有重要作用。     

我国财产险公司盈余管理分析

盈余管理在学术界广受关注,我国财产险公司也存在盈余管理的现象。财产险公司盈余管理既有与一般企业盈余管理相同的共性,又有其特殊资产结构所导致的个性。本文对我国财产险公司盈余管理的动机和手段进行了分析,发现财产险公司主要通过未决赔款准备金的调整实现盈余管理。     

对保险企业降低赔付率的思考

通过对我县保险公司某年度资产、负债、损益的真实、合法和效益情况进行审计,从帐面分析,该公司当年财险保费收入495.26万元,赔款支出374.14万元,赔付率达75%。若接应计原则,考虑未到期责任准备金及末决赔款支出的计提和结转,其赔付率高达82%。从审计了解的情况和我县实际分析,导致县保险公司赔付率高的原因。客观上讲,有以下几点:     

Comparisons of Bayesian estimation procedures for two-way contingency tables without interaction

Bayesian and empirical Bayesian estimation methods are reviewed and proposed for the row and column parameters in two-way Contingency tables without interaction. Rasch's multiplicative Poisson model for misreadings is discussed in an example. The case is treated where assumptions of exchangeability are reasonable a priori for the unknown parameters. Two different types of prior distributions are compared, It appears that gamma priors yield more tractable results than lognormal priors.     

A Bayesian estimation of macro and micro CES production functions

The paper presents a Bayesian estimation of CES production functions. The proposed estimation method is easier to use than methods so far developed and it allows a direct comparison with the maximum likelihood estimator. A Bayesian highest posterior density interval inference is made to examine the validity of the Cobb–Douglas representation. The method is applied to Japanese macro data and to micro data on two Japanese manufacturing plants. The results indicate that the current practice of employing the Cobb–Douglas form a priori ought to be corrected. In addition, the micro data present results paradoxical to the commonly held view of capital intensity.     

Which factors capitalize into house prices? A Bayesian averaging approach

This paper investigates the robustness of 33 community-specific explanatory variables for house prices in the Swiss metropolitan area of Zurich using Bayesian model averaging. The analysis suggests a new way to perform hedonic variable selection and provides a minimal list of variables which may serve as a priori constraints when predicting house prices or estimating the effect of other community-specific characteristics in a metropolitan area in a highly developed country. In the context analyzed, the main variables which capitalize with a high posterior probability are location-specific real estate characteristics, municipal taxes and expenditure for culture, health and social well-being. Demographic as well as other socio-economic controls seem to be of minor importance.     

Relative entropy in sequential decision problems

Consider an agent who faces a sequential decision problem. At each stage the agent takes an action and observes a stochastic outcome (e.g., daily prices, weather conditions, opponents' actions in a repeated game, etc.). The agent's stage-utility depends on his action, the observed outcome and on previous outcomes. We assume the agent is Bayesian and is endowed with a subjective belief over the distribution of outcomes. The agent's initial belief is typically inaccurate. Therefore, his subjectively optimal strategy is initially suboptimal. As time passes information about the true dynamics is accumulated and, depending on the compatibility of the belief with respect to the truth, the agent may eventually learn to optimize. We introduce the notion of relative entropy, which is a natural adaptation of the entropy of a stochastic process to the subjective set-up. We present conditions, expressed in terms of relative entropy, that determine whether the agent will eventually learn to optimize. It is shown that low entropy yields asymptotic optimal behavior. In addition, we present a notion of pointwise merging and link it with relative entropy.     

A tale of forecasting 1001 series : The Bayesian knight strikes again

In this paper it is pointed out that a Bayesian forecasting procedure performed better according to an average mean square error (MSE) criterion than the many other forecasting procedures utilized in the forecasting experiments reported in an extensive study by Makridakis et al. (1982). This fact was not mentioned or discussed by the authors. Also, it is emphasized that if criteria other than MSE are employed, Bayesian forecasts that are optimal relative to them should be employed. Specific examples are provided and analyzed to illustrate this point.     

A non-parametric spatial independence test using symbolic entropy

In the present paper, we construct a new, simple, consistent and powerful test for spatial independence, called the SG test, by using the new concept of symbolic entropy as a measure of spatial dependence. The standard asymptotic distribution of the test is an affine transformation of the symbolic entropy under the null hypothesis. The test statistic, with the proposed symbolization procedure, and its standard limit distribution have appealing theoretical properties that guarantee the general applicability of the test. An important aspect is that the test does not require specification of the W matrix and is free of a priori assumptions. We include a Monte Carlo study of our test, in comparison with the well-known Moran's I, the SBDS (de Graaff et al., 2001) and τ test (Brett and Pinkse, 1997) that are two non-parametric tests, to better appreciate the properties and the behaviour of the new test. Apart from being competitive compared to other tests, results underline the outstanding power of the new test for non-linear dependent spatial processes.     

Monitoring mortality: a state-space approach

"A state-space model is developed which provides estimates of decrements in a dynamic environment. The model integrates the actual unfolding experience and a priori or Bayesian views of the rates. The estimates of present rates and predicted future rates are continually updated and associated standard errors have simple expressions. The model is described and applied in the context of mortality estimation but it should prove useful in other actuarial applications. The approach is particularly suitable for dynamic environments where data are scarce and updated parameter estimates are required on a regular basis. To illustrate the method it is used to monitor the unfolding mortality experience of the retired lives under an actual pension plan."     

Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution

Many studies have measured productivity change and efficiency when an undesirable output is a by-product. We flexibly treat the bad as a technology shifter of an input distance function and model a system of nonlinear equations subject to endogeneity. Theory dictates that we impose monotonicity on all inputs, outputs, and the bad. Since a Bayesian full-information likelihood approach can easily be misspecified, we utilize the Kim (J. Econometrics 107 (2002) 175) limited-information likelihood (LIL) derived by minimizing the entropy distance subject to the moment conditions from the Generalized Method of Moments (GMM) estimator. This represents an extension of the Bayesian Method of Moments approach of Zellner and Chen (Macroeconom. Dyn. 5 (2001) 673), Zellner and Tobias (Int. Econom. Rev. 42 (2001) 121), and Zellner (in: Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers, Edward Elgar, Cheltenham, 1997; J. Econometrics 83 (1998) 185) which uses entropy maximization but does not incorporate a specific likelihood. Using Bayes’ Theorem we combine traditional priors with the LIL, which has a mode at the standard multiple-equation GMM estimator, yielding a limited-information posterior distribution. We generalize the approach of Kim (J. Econometrics 107 (2002) 175) by incorporating an unknown covariance matrix in a Gibbs sampling framework and applying the methodology to nonlinear equations. This allows us to estimate shadow prices, technical efficiency, and productivity change for a panel of electric utilities, yielding results that differ substantially from those obtained using standard GMM.     

Coalition structure values in differential information economies: is unity a strength?

The coalition structure (CS) value, introduced by Owen [G. Owen, Values of games with a priori unions, in: Essays in Mathematical Economics and Game Theory, Springer Verlag, Berlin, 1977] and Hart and Kurz [Econometrica 51 (1983) 1047], generalizes the Shapley value to social situations where coalitions form for the purpose of bargaining. This paper introduces the CS value to economies with differential information. We show that the private CS values exists and is Bayesian incentive compatible. Moreover, we construct examples that go against the intuitive viewpoint that “unity is strength”. In particular, we consider a three-person economy in which two agents bargain as a unit against the third agent. We show that bargaining as a unit is advantageous if and only if information is complete. This result sheds new light on bargaining under differential information.     

On Bayesian and non-Bayesian estimation of a two-level CES production function for the Dutch manufacturing sector

In this paper maximum-likelihood estimates of the parameters of the two-level CES function, obtained by direct estimation of this function, are given. In addition the authors propose to show how a Bayesian analysis may help to find a solution to the difficulties related with, but not specific to, this particular estimation problem. It is shown that numerical integration of the posterior distribution may give an indication as to which parameter has to be pinpointed and at which value when multi-collinearity precludes unconditional maximization of the likelihood. It is suspected that this approach has a wider field of application.