scholarly journals The Combined Stop-Loss and Quota-Share Reinsurance: Conditional Tail Expectation-Based Optimization from the Joint Perspective of Insurer and Reinsurer

Risks ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 125
Author(s):  
Khreshna Syuhada ◽  
Arief Hakim ◽  
Suci Sari
Keyword(s):  
Convex Combination ◽  
Estimation Procedure ◽  
Total Loss ◽  
Survival Functions ◽  
Numerical Examples ◽  
Stop Loss ◽  
Conditional Tail Expectation

In the presence of reinsurance, an insurer may effectively reduce its (aggregated) loss by partially ceding such a loss to a reinsurer. Stop-loss and quota-share reinsurance contracts are commonly agreed between these two parties. In this paper, we aim to explore a combination of these contracts. The survival functions of the ceded loss and the retained loss are firstly investigated. Optimizing such a reinsurance design is then carried out from the joint perspective of the insurer and the reinsurer. Specifically, we explicitly derive optimal retentions under a criterion of minimizing a convex combination of conditional tail expectations of the insurer’s total loss and the reinsurer’s total loss. In addition, an estimation procedure and more explanations on numerical examples are also presented to find their estimated values.

scholarly journals Optimal Limited Stop-Loss Reinsurance under VaR, TVaR, and CTE Risk Measures

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xianhua Zhou ◽  
Huadong Zhang ◽  
Qingquan Fan
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Loss Ratio ◽  
Optimal Reinsurance ◽  
Numerical Examples ◽  
Optimal Values ◽  
Stop Loss ◽  
Conditional Tail Expectation ◽  
Reinsurance Market

This paper aims to provide a practical optimal reinsurance scheme under particular conditions, with the goal of minimizing total insurer risk. Excess of loss reinsurance is an essential part of the reinsurance market, but the concept of stop-loss reinsurance tends to be unpopular. We study the purchase arrangement of optimal reinsurance, under which the liability of reinsurers is limited by the excess of loss ratio, in order to generate a reinsurance scheme that is closer to reality. We explore the optimization of limited stop-loss reinsurance under three risk measures: value at risk (VaR), tail value at risk (TVaR), and conditional tail expectation (CTE). We analyze the topic from the following aspects: (1) finding the optimal franchise point with limited stop-loss coverage, (2) finding the optimal limited stop-loss coverage within a certain franchise point, and (3) finding the optimal franchise point with limited stop-loss coverage. We provide several numerical examples. Our results show the existence of optimal values and locations under the various constraint conditions.

scholarly journals Optimal reinsurance: a reinsurer’s perspective

2017 ◽  
Vol 12 (1) ◽  
pp. 147-184 ◽  
Author(s):  
Fei Huang ◽  
Honglin Yu
Keyword(s):  
At Risk ◽  
Closed Form ◽  
Value At Risk ◽  
Optimality Criteria ◽  
Total Loss ◽  
Optimal Reinsurance ◽  
Numerical Examples ◽  
Dependency Structure ◽  
Closed Form Solutions

AbstractIn this paper, the optimal safety loading that the reinsurer should set in the reinsurance pricing is studied, which is novel in the literature. It is first assumed that the insurer will choose the form of the reinsurance contract by following the results derived in Cai et al. Different optimality criteria from the reinsurer’s perspective are then studied, such as maximising the expectation of the profit, maximising the utility of the profit and minimising the value-at-risk of the reinsurer’s total loss. By applying the concept of comonotonicity, the problem in which the reinsurer is facing two risks with unknown dependency structure is also solved. Closed-form solutions are obtained when the underlying losses are zero-modified exponentially distributed. Finally, numerical examples are provided to illustrate the results derived.

scholarly journals An optimal multi-layer reinsurance policy under conditional tail expectation

2017 ◽  
Vol 12 (1) ◽  
pp. 130-146
Author(s):  
Amir T. Payandeh Najafabadi ◽  
Ali Panahi Bazaz
Keyword(s):  
Optimality Criterion ◽  
Risk Measure ◽  
Insurance Companies ◽  
Total Risk ◽  
Unknown Parameters ◽  
Practical Applications ◽  
Simulation Based ◽  
Cartesian Coordinate ◽  
Stop Loss ◽  
Conditional Tail Expectation

AbstractAn usual reinsurance policy for insurance companies admits one or two layers of the payment deductions. Under optimality criterion of minimising the Conditional Tail Expectation (CTE) risk measure of the insurer’s total risk, this article generalises an optimal stop-loss reinsurance policy to an optimal multi-layer reinsurance policy. To achieve such optimal multi-layer reinsurance policy, this article starts from a given optimal stop-loss reinsurance policy f(⋅). In the first step, it cuts down the interval [0, ∞) into intervals [0, M1) and [M1, ∞). By shifting the origin of Cartesian coordinate system to (M1, f(M1)), and showing that under the CTE criteria $$f\left( x \right)I_{{[0,M_{{\rm 1}} )}} \left( x \right){\plus}\left( {f\left( {M_{{\rm 1}} } \right){\plus}f\left( {x{\minus}M_{{\rm 1}} } \right)} \right)I_{{[M_{{\rm 1}} ,{\rm }\infty)}} \left( x \right)$$ is, again, an optimal policy. This extension procedure can be repeated to obtain an optimal k-layer reinsurance policy. Finally, unknown parameters of the optimal multi-layer reinsurance policy are estimated using some additional appropriate criteria. Three simulation-based studies have been conducted to demonstrate: (1) the practical applications of our findings and (2) how one may employ other appropriate criteria to estimate unknown parameters of an optimal multi-layer contract. The multi-layer reinsurance policy, similar to the original stop-loss reinsurance policy is optimal, in a same sense. Moreover, it has some other optimal criteria which the original policy does not have. Under optimality criterion of minimising a general translative and monotone risk measure ρ(⋅) of either the insurer’s total risk or both the insurer’s and the reinsurer’s total risks, this article (in its discussion) also extends a given optimal reinsurance contract f(⋅) to a multi-layer and continuous reinsurance policy.

ON SOME PROPERTIES OF TWO VECTOR-VALUED VAR AND CTE MULTIVARIATE RISK MEASURES FOR ARCHIMEDEAN COPULAS

2014 ◽  
Vol 44 (3) ◽  
pp. 613-633 ◽  
Author(s):  
Werner Hürlimann
Keyword(s):  
Value At Risk ◽  
Integral Transform ◽  
Risk Measures ◽  
Usual Sense ◽  
Translation Invariance ◽  
Archimedean Copulas ◽  
Coherent Risk ◽  
Multivariate Risk Measures ◽  
Stop Loss ◽  
Conditional Tail Expectation

AbstractWe consider the multivariate Value-at-Risk (VaR) and Conditional-Tail-Expectation (CTE) risk measures introduced in Cousin and Di Bernardino (Cousin, A. and Di Bernardino, E. (2013) Journal of Multivariate Analysis, 119, 32–46; Cousin, A. and Di Bernardino, E. (2014) Insurance: Mathematics and Economics, 55(C), 272–282). For absolutely continuous Archimedean copulas, we derive integral formulas for the multivariate VaR and CTE Archimedean risk measures. We show that each component of the multivariate VaR and CTE functional vectors is an integral transform of the corresponding univariate VaR measures. For the class of Archimedean copulas, the marginal components of the CTE vector satisfy the following properties: positive homogeneity (PH), translation invariance (TI), monotonicity (MO), safety loading (SL) and VaR inequality (VIA). In case marginal risks satisfy the subadditivity (MSA) property, the marginal CTE components are also sub-additive and hitherto coherent risk measures in the usual sense. Moreover, the increasing risk (IR) or stop-loss order preserving property of the marginal CTE components holds for the class of bivariate Archimedean copulas. A counterexample to the (IR) property for the trivariate Clayton copula is included.

scholarly journals New extragradient methods with non-convex combination for pseudomonotone equilibrium problems with applications in Hilbert spaces

Filomat ◽  
2019 ◽  
Vol 33 (6) ◽  
pp. 1677-1693 ◽  
Author(s):  
Shenghua Wang ◽  
Yifan Zhang ◽  
Ping Ping ◽  
Yeol Cho ◽  
Haichao Guo
Keyword(s):  
Hilbert Space ◽  
Fixed Point ◽  
Strong Convergence ◽  
Hilbert Spaces ◽  
Fixed Point Theorems ◽  
Equilibrium Problems ◽  
Convex Combination ◽  
Projection Methods ◽  
Extragradient Methods ◽  
Numerical Examples

In the literature, the most authors modify the viscosity methods or hybrid projection methods to construct the strong convergence algorithms for solving the pseudomonotone equilibrium problems. In this paper, we introduce some new extragradient methods with non-convex combination to solve the pseudomonotone equilibrium problems in Hilbert space and prove the strong convergence for the constructed algorithms. Our algorithms are very different with the existing ones in the literatures. As the application, the fixed point theorems for strict pseudo-contraction are considered. Finally, some numerical examples are given to show the effectiveness of the algorithms.

Granular Synthesis of Rule-Based Models and Function Approximation Using Rough Sets

2010 ◽  
pp. 408-425 ◽  
Author(s):  
Carlos Pinheiro ◽  
Fernando Gomide ◽  
Otávio Carpinteiro ◽  
Isaías Lima
Keyword(s):  
Nonlinear Systems ◽  
Rough Sets ◽  
Estimation Procedure ◽  
New Method ◽  
Rule Based ◽  
Numerical Examples ◽  
Rule Sets ◽  
Computational Procedures ◽  
Granular Synthesis ◽  
And Function

This chapter suggests a new method to develop rule-based models using concepts about rough sets. The rules encapsulate relations among variables and give a mechanism to link granular descriptions of the models with their computational procedures. An estimation procedure is suggested to compute values from granular representations encoded by rule sets. The method is useful to develop granular models of static and dynamic nonlinear systems and processes. Numerical examples illustrate the main features and the usefulness of the method.

scholarly journals Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sunisa Luemsai ◽  
Thongchai Botmart ◽  
Wajaree Weera
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Integral Inequality ◽  
Convex Combination ◽  
Linear Matrix ◽  
Time Varying ◽  
Numerical Examples ◽  
Combination Technique ◽  
Generalized Neural Networks ◽  
Special Case

AbstractThe problem of asymptotic stability and extended dissipativity analysis for the generalized neural networks with interval discrete and distributed time-varying delays is investigated. Based on a suitable Lyapunov–Krasovskii functional (LKF), an improved Wirtinger single integral inequality, a novel triple integral inequality, and convex combination technique, the new asymptotic stability and extended dissipativity criteria are achieved for the generalized neural networks with interval discrete and distributed time-varying delays. By the above methods, the less conservative asymptotic stability criteria are obtained for a special case of the generalized neural networks. By using the Matlab LMI toolbox, the derived new asymptotic stability and extended dissipativity criteria are expressed in terms of linear matrix inequalities (LMIs) that cover $H_{\infty }$ H ∞ , $L_{2}$ L 2 –$L_{\infty }$ L ∞ , passivity, and dissipativity performance by setting parameters in the general performance index. Finally, we show numerical examples which are less conservative than other examples in the literature. Moreover, we present numerical examples for asymptotic stability and extended dissipativity performance of the generalized neural networks, including a special case of the generalized neural networks.

scholarly journals Optimal Insurance Indemnity and Reinsurance Strategy for Health Insurance

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Zhang ◽  
Yonghong Wu ◽  
Haixiang Yao
Keyword(s):  
Optimal Control ◽  
Health Insurance ◽  
Lagrange Multiplier ◽  
Optimization Problems ◽  
Health Management ◽  
Control Technique ◽  
Numerical Examples ◽  
Optimal Insurance ◽  
Stop Loss ◽  
Theoretical Results

“Health insurance + health management” package has recently become one of the most important nonlife insurance products, and its pricing technique has drawn attention from both academia and industry. This paper investigates the optimal indemnity design and per-loss reinsurance strategy for the health insurance package, where the reinsurance contract is assumed to combine the quota-share type and the excess-of-loss type. By applying the Lagrange multiplier method and optimal control technique, we develop the solutions to the corresponding optimization problems and obtain the optimal deductible. Then, we proceed to solve the optimal quota-share proportion and the optimal stop-loss retention based on the optimal insurance indemnity. In addition to theoretical results, numerical examples are also given to illustrate the effects of various key parameters on the optimal indemnity design and combinational reinsurance strategy.

Error Measures for Functional Product Testing

1999 ◽  
Author(s):  
Uichung Cho ◽  
Kristin L. Wood ◽  
Richard H. Crawford
Keyword(s):  
Error Estimation ◽  
Prediction Accuracy ◽  
Estimation Procedure ◽  
Functional Testing ◽  
Partial Knowledge ◽  
Product Testing ◽  
Numerical Examples ◽  
Error Measures ◽  
Functional Product ◽  
Similarity Method

Abstract During product development, testing of models and prototypes offers significant advantages over direct product testing, including easier, cheaper, and faster fabrication. However, two issues prevent effective functional testing with prototypes: prediction accuracy and confidence in scale testing results. The traditional similarity method, which is based on dimensional analysis, is commonly applied to perform scale testing. However, the method may not provide accurate scale testing results, especially when available model materials are different from the final product materials. The authors have developed a new empirical similarity method, wherein specimen pairs and partial knowledge of systems are systematically utilized, to improve the prediction accuracy. In this paper we describe the construction of error measures to utilize scale testing results with confidence. In practice, scale testing results are validated based on experiences with previous testing results. This approach to predicting accuracy is difficult to formalize. We develop and simulate a systematic two-level error estimation procedure. Realistic numerical examples demonstrate the feasibility of the approach.

An Interesting Problem in the Estimation of Scoring Reliability

2004 ◽  
Vol 29 (3) ◽  
pp. 333-341 ◽  
Author(s):  
Samuel A. Livingston
Keyword(s):  
Performance Assessment ◽  
Standard Error ◽  
Interrater Reliability ◽  
Estimation Procedure ◽  
Reliability Coefficient ◽  
Reliability Estimation ◽  
Interesting Problem ◽  
Numerical Examples ◽  
Selected Subset ◽  
A Performance

A performance assessment consisting of 10 separate exercises was scored with a randomized scoring procedure. All responses to each exercise were rated once; in addition, a randomly selected subset of the responses to each exercise received an independent second rating. Each second rating was averaged with the corresponding first rating before the scores were computed. This article presents a method for estimating the scoring reliability (interrater reliability) coefficient and the standard error of scoring for the resulting scores. The report concludes with some numerical examples showing how the reliability estimation procedure can be used to estimate the effect of varying the proportions of responses that are double-scored.

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