Optimal Reinsurance through Minimizing New Risk Measures under Technical Benefit Constraints

2018 ◽  
Vol 35 ◽  
pp. 24-37
Author(s):  
Abderrahim El Attar ◽  
Mostafa El Hachloufi ◽  
Zine El Abidine Guennoun
Keyword(s):  
Augmented Lagrangian ◽  
Risk Measures ◽  
Decision Support Tool ◽  
Optimal Choice ◽  
Insurance Company ◽  
Support Tool ◽  
Minimization Procedure ◽  
Variance Risk ◽  
Actuarial Risk ◽  
Conditional Tail Expectation

In this paper we present an approach to minimize the actuarial risk for the optimal choice of a form of reinsurance, and this is intended to be through a choice of treated parameters that minimize the risk using the Conditional Tail Expectation and the Conditional Tail Variance risk measures. The minimization procedure is based on the Augmented Lagrangian and a genetic algorithm with technical benefit as a constraint. This approach can be seen as a decision support tool that can be used by managers to minimize the actuarial risk in the insurance company.

scholarly journals Tail Variance Premium with Applications for Elliptical Portfolio of Risks

2006 ◽  
Vol 36 (2) ◽  
pp. 433-462 ◽  
Author(s):  
Edward Furman ◽  
Zinoviy Landsman
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Insurance Company ◽  
Risk Capital ◽  
Normal Distributions ◽  
Risk Manager ◽  
Tail Conditional Expectation ◽  
Variance Risk ◽  
Risk Managers ◽  
Variance Risk Measure

In this paper we consider the important circumstances involved when risk managers are concerned with risks that exceed a certain threshold. Such conditions are well-known to insurance professionals, for instance in the context of policies involving deductibles and reinsurance contracts. We propose a new premium called tail variance premium (TVP) which answers the demands of these circumstances. In addition, we suggest a number of risk measures associated with TVP. While the well-known tail conditional expectation risk measure provides a risk manager with information about the average of the tail of the loss distribution, tail variance risk measure (TV) estimates the variability along such a tail. Furthermore, given a multivariate setup, we offer a number of allocation techniques which preserve different desirable properties (sub-additivity and fulladditivity, for instance). We are able to derive explicit expressions for TV and TVP, and risk capital decomposition rules based on them, in the general framework of multivariate elliptical distributions. This class is very popular among actuaries and risk managers because it contains distributions with marginals whose tails are heavier than those of normal distributions. This distinctive feature is desirable when modeling financial datasets. Moreover, according to our results, in some cases there exists an optimal threshold, such that by choosing it, an insurance company minimizes its risk.

AN INCLUSIVE CRITERION FOR AN OPTIMAL CHOICE OF REINSURANCE

2017 ◽  
Vol 12 (04) ◽  
pp. 1750018 ◽  
Author(s):  
EL ATTAR ABDERRAHIM ◽  
EL HACHLOUFI MOSTAFA ◽  
GUENNOUN ZINE EL ABIDINE
Keyword(s):  
Genetic Algorithms ◽  
Augmented Lagrangian ◽  
Risk Measures ◽  
Risk Measure ◽  
Optimization Procedure ◽  
Optimal Choice ◽  
Adjustment Coefficient ◽  
Probability Of Ruin ◽  
Reinsurance Treaty ◽  
Mean Variance

In this paper, we propose an inclusive model which allows to improve the results obtained in the literature with regard to the criteria set by the insurers such as, maximizing the expected technical benefit under the variance constraint (mean-variance), minimizing the probability of ruin and minimizing risk measures. In this model, we determine the optimal reinsurance treaty parameter that minimizes both the risk and the probability of ruin (by maximizing the Lundberg adjustment coefficient) under the constraint of the technical benefit which must also be maximal, based on the conditional tail variance (CTV) risk measure. Thus, we have developed an optimization procedure based on the augmented Lagrangian and genetic algorithms, in order to solve the optimization program of this model.

scholarly journals Tail Variance Premium with Applications for Elliptical Portfolio of Risks

2006 ◽  
Vol 36 (02) ◽  
pp. 433-462 ◽  
Author(s):  
Edward Furman ◽  
Zinoviy Landsman
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Insurance Company ◽  
Risk Capital ◽  
Normal Distributions ◽  
Risk Manager ◽  
Tail Conditional Expectation ◽  
Variance Risk ◽  
Risk Managers ◽  
Variance Risk Measure

In this paper we consider the important circumstances involved when risk managers are concerned with risks that exceed a certain threshold. Such conditions are well-known to insurance professionals, for instance in the context of policies involving deductibles and reinsurance contracts. We propose a new premium called tail variance premium (TVP) which answers the demands of these circumstances. In addition, we suggest a number of risk measures associated with TVP. While the well-known tail conditional expectation risk measure provides a risk manager with information about the average of the tail of the loss distribution, tail variance risk measure (TV) estimates the variability along such a tail. Furthermore, given a multivariate setup, we offer a number of allocation techniques which preserve different desirable properties (sub-additivity and fulladditivity, for instance). We are able to derive explicit expressions for TV and TVP, and risk capital decomposition rules based on them, in the general framework of multivariate elliptical distributions. This class is very popular among actuaries and risk managers because it contains distributions with marginals whose tails are heavier than those of normal distributions. This distinctive feature is desirable when modeling financial datasets. Moreover, according to our results, in some cases there exists an optimal threshold, such that by choosing it, an insurance company minimizes its risk.

A protocol for sampling pastures in hill country

2016 ◽  
Vol 78 ◽  
pp. 203-209 ◽  
Author(s):  
K.J. Hutchinson ◽  
D.R. Scobie ◽  
J. Beautrais ◽  
A.D. Mackay ◽  
G.M. Rennie ◽  
...  
Keyword(s):  
Random Sampling ◽  
Geographical Information Systems ◽  
Field Measurements ◽  
Decision Support Tool ◽  
Accurate Estimate ◽  
Ease Of Use ◽  
Geographical Information ◽  
Support Tool ◽  
Topographic Complexity ◽  
Hill Country

To develop a protocol to guide pasture sampling for estimation of paddock pasture mass in hill country, a range of pasture sampling strategies, including random sampling, transects and stratification based on slope and aspect, were evaluated using simulations in a Geographical Information Systems computer environment. The accuracy and efficiency of each strategy was tested by sampling data obtained from intensive field measurements across several farms, regions and seasons. The number of measurements required to obtain an accurate estimate was related to the overall pasture mass and the topographic complexity of a paddock, with more variable paddocks requiring more samples. Random sampling from average slopes provided the best balance between simplicity and reliability. A draft protocol was developed from the simulations, in the form of a decision support tool, where visual determination of the topographic complexity of the paddock, along with the required accuracy, were used to guide the number of measurements recommended. The protocol was field tested and evaluated by groups of users for efficacy and ease of use. This sampling protocol will offer farmers, consultants and researchers an efficient, reliable and simple way to determine pasture mass in New Zealand hill country settings. Keywords: hill country, feed budgeting, protocol pasture mass, slope

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

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