The effect of reinsurance treaties on the cedent loss reserving

IF 1.8 Q3 MANAGEMENT
Amir T. Payandeh Najafabadi, Fatemeh Atatalab
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引用次数: 0

Abstract

Purpose The usual, simple and computationally expensive recovery payment method for a given reinsurance treaty, besides the total run-off triangle, builds a new run-off triangle, say recovery run-off triangle, for the reinsurer’s contribution and predicts the reinsurer’s contribution to the total loss reserves. This paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties. Design/methodology/approach The authors propose a new solution to the problem of how to consider reserving issues when there is a reinsurance treaty for a portfolio of general insurance policies. Considering this when determining pricing or making capital decisions is very important. Findings In particular, it considers the quota share (QS) treaty, surplus (SPL) treaty, excess-of-loss (XL) treaty, largest claims reinsurance (LCR) treaty and excédent du coût moyen relatif (ECOMOR) treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the mean square error of prediction (MSEP). The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study. Originality/value This paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties. In particular, it considers the QS treaty, SPL treaty, XL treaty, LCR treaty and ECOMOR treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the MSEP. The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study.
再保险条约对再保险损失准备的影响
目的给定再保险协议的通常、简单且计算昂贵的追偿支付方法,除了总流失三角形外,还为再保险人的贡献建立了一个新的流失三角形,即追偿流失三角形,并预测再保险人对总损失准备金的贡献。本文在不建立回收-流失三角的情况下,利用现有的再保险协议先验知识来预测五个再保险协议下的受保人损失准备金。设计/方法论/方法作者提出了一种新的解决方案,以解决在一般保险单组合有再保险条约时如何考虑准备金问题。在确定定价或做出资本决策时考虑这一点非常重要。调查结果特别是,它考虑了配额份额(QS)条约、盈余(SPL)条约、超额损失(XL)条约、最大索赔再保险(LCR)条约和经济合作与发展(ECOMOR)条约。然后,它为预测cedent的损失准备金以及使用预测的均方误差(MSEP)来评估这种预测奠定了理论基础。通过模拟研究,研究了此类再保险条约对受保人损失准备金变化的影响。独创性/价值本文在不建立回收径流三角的情况下,使用现有的再保险条约的先验知识来预测五个再保险条约下受保人的损失准备金。特别是,它审议了QS条约、SPL条约、XL条约、LCR条约和ECOMOR条约。然后,它为预测ecedent的损失准备金和使用MSEP评估这种预测奠定了理论基础。通过模拟研究,研究了此类再保险条约对受保人损失准备金变化的影响。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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