Nonconcave Optimal Investment with Value-at-Risk Constraint: An Application to Life Insurance Contracts

2020 ◽  
Vol 58 (2) ◽  
pp. 895-936 ◽  
Author(s):  
Thai Nguyen ◽  
Mitja Stadje
Keyword(s):  
At Risk ◽  
Life Insurance ◽  
Value At Risk ◽  
Optimal Investment ◽  
Insurance Contracts ◽  
Risk Constraint

scholarly journals Optimal investment-reinsurance policy with regime switching and value-at-risk constraint

2020 ◽  
Vol 16 (5) ◽  
pp. 2195-2211
Author(s):  
Ming Yan ◽  
◽  
Hongtao Yang ◽  
Lei Zhang ◽  
Shuhua Zhang ◽  
...  
Keyword(s):  
At Risk ◽  
Regime Switching ◽  
Value At Risk ◽  
Optimal Investment ◽  
Risk Constraint

scholarly journals A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 32 ◽  
Author(s):  
Zhuo Jin ◽  
Zhixin Yang ◽  
Quan Yuan
Keyword(s):  
Genetic Algorithm ◽  
At Risk ◽  
Information Processing ◽  
Decision Maker ◽  
Value At Risk ◽  
Optimal Investment ◽  
Finite Horizon ◽  
Risk Constraint ◽  
The Cost ◽  
Consumption Strategies

This paper studies the optimal investment and consumption strategies in a two-asset model. A dynamic Value-at-Risk constraint is imposed to manage the wealth process. By using Value at Risk as the risk measure during the investment horizon, the decision maker can dynamically monitor the exposed risk and quantify the maximum expected loss over a finite horizon period at a given confidence level. In addition, the decision maker has to filter the key economic factors to make decisions. Considering the cost of filtering the factors, the decision maker aims to maximize the utility of consumption in a finite horizon. By using the Kalman filter, a partially observed system is converted to a completely observed one. However, due to the cost of information processing, the decision maker fails to process the information in an arbitrarily rational manner and can only make decisions on the basis of the limited observed signals. A genetic algorithm was developed to find the optimal investment, consumption strategies, and observation strength. Numerical simulation results are provided to illustrate the performance of the algorithm.

scholarly journals Optimal investment with a value-at-risk constraint

2012 ◽  
Vol 8 (3) ◽  
pp. 531-547 ◽  
Author(s):  
Jingzhen Liu ◽  
Lihua Bai ◽  
Ka-Fai Cedric Yiu
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Optimal Investment ◽  
A Value ◽  
Risk Constraint

scholarly journals Collective Value-At-Risk (Colvar) In Life Insurance Collection

2018 ◽  
Vol 7 (3.7) ◽  
pp. 25
Author(s):  
Abdul Talib Bon ◽  
Muhammad Iqbal Al-Banna Ismail ◽  
Sukono . ◽  
Adhitya Ronnie Effendie
Keyword(s):  
At Risk ◽  
Standard Deviation ◽  
Life Insurance ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Model Development ◽  
Insurance Company ◽  
Insurance Claims ◽  
Level Of Significance

Analysis of risk in life insurance claims is very important to do by the insurance company actuary. Risk in life insurance claims are generally measured using the standard deviation or variance. The problem is, that the standard deviation or variance which is used as a measure of the risk of a claim can not accommodate any claims of risk events. Therefore, in this study developed a model called risk measures Collective Modified Value-at-Risk. Model development is done for several models of the distribution of the number of claims and the distribution of the value of the claim. Collective results of model development Modified Value-at-Risk is expected to accommodate any claims of risk events, when given a certain level of significance  

Optimal Reciprocal Reinsurance under VaR or TVaR Constraint

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hung-Hsi Huang ◽  
Ching-Ping Wang
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Moral Hazard ◽  
Value At Risk ◽  
Incentive Compatibility ◽  
Regular Form ◽  
Optimal Reinsurance ◽  
The Common ◽  
Risk Constraint ◽  
Management Requirement

Abstract Most existing researches on optimal reinsurance contract are based on an insurer’s viewpoint. However, the optimal reinsurance contract for an insurer is not necessarily to be optimal for a reinsurer. Hence, this study aims to develop the optimal reciprocal reinsurance which satisfies the benefits of both the insurer and reinsurer. Additionally, due to legislative restriction or risk management requirement, the wealth of insurer and reinsurer are frequently imposed upon a VaR (Value-at-Risk) or TVaR (Tail Value-at-Risk) constraint. Therefore, this study develops an optimal reciprocal reinsurance contract which maximizes the common benefits (evaluated by weighted addition of expected utilities) of the insurer and reinsurer subject to their VaR or TVaR constraints. Furthermore, for avoiding moral hazard problem, the developed contract is additionally restricted to a regular form or incentive compatibility (both indemnity schedule and retained loss schedule are continuously nondecreasing).

HOW TO PRICE INFORMATION BY KULLBACK-LEIBLER ENTROPY AND A MOMENT-RETURN RELATION FOR PORTFOLIOS

2001 ◽  
Vol 04 (03) ◽  
pp. 535-543
Author(s):  
ANDREAS DE VRIES
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Optimization Problem ◽  
Portfolio Theory ◽  
Price Information ◽  
Expected Return ◽  
Risk And Return ◽  
Leibler Information ◽  
Risk Constraint

A connection between the notion of information and the concept of risk and return in portfolio theory is deduced. This succeeds in two steps: A general moment-return relation for arbitrary assets is derived, thereafter the total expected return is connected to the Kullback-Leibler information. With this result the optimization problem to maximize the expected return of a portfolio consisting of n subportfolios by moment variation under a given value-at-risk constraint is solved. This yields an ansatz to price information.

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