Optimal Reinsurance Strategy Research Articles

Research on Optimal Investment Reinsurance of Insurance Companies under Delayed Risk Model

The reinsurance and investment portfolio of insurance companies has always been a hot issue in insurance business. In insurance practice, it is inevitable for insurance companies to invest their own funds in order to expand their capital scale and enhance market competitiveness so as to obtain greater returns. At the same time, in order for insurance companies to disperse insurance risks and to avoid too concentrated claims or catastrophes caused by failure to perform compensation responsibilities, the purchase of reinsurance business has also become an important way. Stochastic control theory is widely used in reinsurance and investment issues. Based on the reinsurance system architecture, this paper establishes a reinsurance delay risk investment model, which reduces the amount of claims to be borne by buying proportional reinsurance to avoid bankruptcy caused by the excessive amount of claims. By using the delayed venture capital model to describe the earnings of insurance companies, the optimal investment and reinsurance strategy are solved under the optimization criterion of minimizing the probability of bankruptcy. By analyzing the model parameter data, the influence of each parameter on optimal investment strategy and optimal reinsurance strategy is discussed.

Robust reinsurance strategy under ambiguity-averse

In this paper, we study the robust optimal reinsurance policy for an insurer with ambiguity aversion in the continuous time model within a class of mixed reinsurance premium criteria, under which several common premium criteria are included.By minimizing the discounted ruin probability of the insurer and maximizing the expected wealth utility of the insurer at the terminal moment, the explicit expressions of the value function and the corresponding robust optimal reinsurance strategy are obtained in both the Cramér-Lundberg jump model and its diffusion approximation model.For the optimization problem of minimizing the discounted ruin probability of the insurer, we prove that the value function has the exponential form by giving an equivalent form of the value function of the optimization problem.In addition, to solve the optimization problems, we construct the auxiliary Lagrangian function of the optimizationproblem skillfully, and then explicitly obtain the expression of robust optimal reinsurance strategy under the general mixed reinsurance criteria.It turns out that the optimal reinsurance strategy has a non trivial structure in the form of curve, which is very different from piecewise linear reinsurance strategy (such as proportional reinsurance strategy, excess loss reinsurance strategy and layer reinsurance strategy). This greatly enriches the results of optimal reinsurance. Finally, we show the sensibilities of some model parameters (including ambiguity-aversion level) to the optimal reinsurance strategy and value function.

The Optimal Time to Merge Two First-Line Insurers with Proportional Reinsurance Policies

We examine the optimal time to merge two first-line insurers with proportional reinsurance policies. The problem is considered in a diffusion approximation model. The objective is to maximize the survival probability of the two insurers. First, the verification theorem is verified. Then, we divide the problem into two cases. In case 1, never merging is optimal and the two insurers follow the optimal reinsurance policies that maximize their survival probability. In case 2, the two insurers follow the same reinsurance policies as those in case 1 until the sum of their surplus processes reaches a boundary. Then, they merge and apply the merged company’s optimal reinsurance strategy.

The Optimal Reinsurance Strategy under Conditional Tail Expectation (CTE) and Wang’s Premium Principle

In this study, we take the conditional tail expectation (CTE) as the constraint condition and consider the optimal reinsurance issues under Wang’s premium principle in general insurance contracts. With the confidence level and the distortion function in Wang’s premium principle given by the insurer in advance, a threshold can be obtained. When the insurer’s risk tolerance level is greater than this value, the optimal reinsurance is a proportional reinsurance in which the deductible equals to this value, else the optimal form of reinsurance is a stop-loss reinsurance. Corresponding numerical examples and economic explanations are also given.

Optimal Surplus-Dependent Reinsurance under Regime-Switching in a Brownian Risk Model

In this paper, we consider a company that wishes to determine the optimal reinsurance strategy minimising the total expected discounted amount of capital injections needed to prevent the ruin. The company’s surplus process is assumed to follow a Brownian motion with drift, and the reinsurance price is modelled by a continuous-time Markov chain with two states. The presence of regime-switching substantially complicates the optimal reinsurance problem, as the surplus-independent strategies turn out to be suboptimal. We develop a recursive approach that allows to represent a solution to the corresponding Hamilton–Jacobi–Bellman (HJB) equation and the corresponding reinsurance strategy as the unique limits of the sequence of solutions to ordinary differential equations and their first- and second-order derivatives. Via Ito’s formula, we prove the constructed function to be the value function. Two examples illustrate the recursive procedure along with a numerical approach yielding the direct solution to the HJB equation.

跳扩散模型下稳健最优再保险和CDS投资策略

跳扩散模型下稳健最优再保险和CDS投资策略

A continuous-time theory of reinsurance chains

A chain of reinsurance is a hierarchical system formed by the subsequent interactions among multiple (re)insurance agents, which is quite often encountered in practice. This paper proposes a novel continuous-time framework for studying the optimal reinsurance strategies within a chain of reinsurance. The transactions between reinsurance buyers and sellers are formulated by means of Stackelberg games, in order to reflect the conflicting interests and unequal negotiation powers in the bargaining process. Assuming the variance premium principle and the mean–variance criterion on the surplus processes, we solve the time-consistent optimal reinsurance demands and pricing strategies in explicit forms, which are surprisingly plain.Based on the proposed reinsurance chain models, our in-depth theoretical analysis shows that: (a.) it is optimal to situate more (resp. less) risk averse reinsurers to the latter (resp. former) positions in a chain of reinsurance; (b.) adding new reinsurers will lower the reinsurance prices at all levels in a chain of reinsurance, promoting the existing agents to rationally control their respective risk exposures; and essentially (c.) alleviate the systemic risk in the chain structure.

Minimizing the Probability of Absolute Ruin Under Ambiguity Aversion

In this paper, we consider an optimal robust reinsurance problem in a diffusion model for an ambiguity-averse insurer, who worries about ambiguity and aims to minimize the robust value involving the probability of absolute ruin and a penalization of model ambiguity. It is assumed that the insurer is allowed to purchase per-claim reinsurance to transfer its risk exposure, and that the reinsurance premium is computed according to the mean-variance premium principle which is a combination of the expected-value and variance premium principles. The optimal reinsurance strategy and the associated value function are derived explicitly by applying stochastic dynamic programming and by solving the corresponding boundary-value problem. We prove that there exists a unique point of inflection which relies on the penalty parameter greatly such that the robust value function is strictly concave up to the unique point of inflection and is strictly convex afterwards. It is also interesting to observe that the expression of the optimal robust reinsurance strategy is independent of the penalty parameter and coincides with the one in the benchmark case without ambiguity. Finally, some numerical examples are presented to illustrate the effect of ambiguity aversion on our optimal results.

Optimal Time-Consistent Investment and Reinsurance Strategy Under Time Delay and Risk Dependent Model

In this paper, we consider the problem of investment and reinsurance with time delay under the compound Poisson model of two-dimensional dependent claims. Suppose an insurance company controls the claim risk of two kinds of dependent insurance businesses by purchasing proportional reinsurance and invests its wealth in a financial market composed of a risk-free asset and a risk asset. The risk asset price process obeys the geometric Brownian motion. By introducing the capital flow related to the historical performance of the insurer, the wealth process described by stochastic delay differential equation (SDDE) is obtained. The extended HJB equation is obtained by using the stochastic control theory under the framework of game theory. Under the reinsurance expected premium principle, optimal time-consistent investment and reinsurance strategy and the corresponding value function are obtained. Finally, the influence of model parameters on the optimal strategy is explained by numerical analysis.

Robust optimal investment and reinsurance problems with learning

In this paper, we consider an optimal investment and reinsurance problem with partially unknown model parameters which are allowed to be learned. The model includes multiple business lines and dependency between them. The aim is to maximize the expected exponential utility of terminal wealth which is shown to imply a robust approach. We can solve this problem using a generalized HJB equation where derivatives are replaced by Clarke's generalized gradient. The optimal investment strategy can be determined explicitly and the optimal reinsurance strategy is given in terms of the solution of an equation. Since this equation is hard to solve, we derive bounds for the optimal reinsurance strategy via comparison arguments.

A BSDE-based approach for the optimal reinsurance problem under partial information

We investigate the optimal reinsurance problem under the criterion of maximizing the expected utility of terminal wealth when the insurance company has restricted information on the loss process. We propose a risk model with claim arrival intensity and claim sizes distribution affected by an unobservable environmental stochastic factor. By filtering techniques (with marked point process observations), we reduce the original problem to an equivalent stochastic control problem under full information. Since the classical Hamilton–Jacobi–Bellman approach does not apply, due to the infinite dimensionality of the filter, we choose an alternative approach based on Backward Stochastic Differential Equations (BSDEs). Precisely, we characterize the value process and the optimal reinsurance strategy in terms of the unique solution to a BSDE driven by a marked point process.

Optimal time-consistent reinsurance strategies for mean-variance insurers under thinning dependence structure

This paper considers an optimal time-consistent proportional reinsurance problem with constraints on the strategies under the mean-variance criterion for an insurer. It is assumed that the surplus process is described by a compound Poisson risk model with dependent classes of insurance business named thinning-dependence structure, in which the stochastic sources related to claim occurrence are classified into different groups, and that each group may cause a claim in each insurance class with a certain probability. By solving the extended Hamilton-Jacobi-Bellman equation within the game theoretic framework, not only explicit expressions of the optimal strategies and value function are obtained for but closed-form expressions of optimal results are derived for by the method of dimension reduction. Moreover, some numerical examples are presented to illustrate the effects of parameters on the optimal strategies as well as the economic interpretation behind.

Optimal reinsurance to minimize the probability of drawdown under the mean-variance premium principle

In this paper, we determine the optimal reinsurance strategy to minimize the probability of drawdown, namely, the probability that the insurer's surplus process reaches some fixed fraction of its maximum value to date. We assume that the reinsurance premium is computed according to the mean-variance premium principle, a combination of the expected-value and variance premium principles. We derive closed-form expressions of the optimal reinsurance strategy and the corresponding minimum probability of drawdown. Then, under the variance premium principle, we show that the safe level can never be reached before drawdown under the optimally controlled surplus process. Finally, we present some numerical examples to show the impact of model parameters on the optimal results.

A Stackelberg reinsurance–investment game with asymmetric information and delay

ABSTRACT This paper investigates a Stackelberg stochastic differential reinsurance–investment game problem, in which the reinsurer is the leader and the insurer is the follower. The unequal status of the reinsurer and the insurer in the financial market is characterized by the asymmetric information model. We consider their wealth processes with delay to characterize bounded memory. The objective of the reinsurer is to find the optimal premium pricing strategy and investment strategy such that its constant absolute risk aversion (CARA) utility of the combination of terminal wealth and average performance wealth is maximized. The objective of the insurer is to find the optimal reinsurance strategy and investment strategy such that its CARA utility of the relative performance is maximized. We derive the equilibrium strategy explicitly for the game by solving corresponding Hamilton–Jacobi–Bellman equations sequentially. Then, we establish the verification theorem. The equilibrium investment strategy indicates that the insurer with less information completely imitates the investment strategy of the reinsurer who has more information on the financial market. Further, we find that the effect of the delay weight on the equilibrium strategy is related to the length of delay time. Finally, we present some numerical examples to demonstrate the findings.

Optimal Proportional Reinsurance Strategy Using Dynamic Programming

This paper is concerned with stochastic optimization in continuous time and its application in reinsurance. It deals with a model of optimization reinsurance which makes it possible to maximize the technical benefit of an insurance company and to minimize the risk for a given period. Indeed, here we discuss a stochastic optimal control problem to determine the optimal risk management strategy, the purpose of which is to identify the appropriate level of transfer rate for "quota share" reinsurance contracts, which are most appropriate to the risk profile of an insurance company. In addition, we use a resolution approach based on dynamic programming and the HJB equation in order to reach the aforementioned aim, which is finding the optimal strategy for reinsurance to adapt better for insurance company.

Optimal Reinsurance Strategy for an Insurer and a Reinsurer with Generalized Variance Premium Principle

This paper focuses on the optimal reinsurance problem with consideration of joint interests of an insurer and a reinsurer. In our model, the risk process is assumed to follow a Brownian motion with drift. The insurer can transfer the risk to the reinsurer via proportional reinsurance, and the reinsurance premium is calculated according to the variance and standard deviation premium principles. The objective is to maximize the expected exponential utility of the weighted sum of the insurer’s and the reinsurer’s terminal wealth, where the weight can be viewed as a regularization parameter to measure the importance of each party. By applying stochastic control theory, we establish the Hamilton–Jacobi–Bellman equation and obtain explicit expressions of optimal reinsurance strategies and optimal value functions. Furthermore, we provide some numerical simulations to illustrate the effects of model parameters on the optimal reinsurance strategies.

Optimal reinsurance under the mean–variance premium principle to minimize the probability of ruin

We consider the problem of minimizing the probability of ruin by purchasing reinsurance whose premium is computed according to the mean–variance premium principle, a combination of the expected-value and variance premium principles. We derive closed-form expressions of the optimal reinsurance strategy and the corresponding minimum probability of ruin under the diffusion approximation of the classical Cramér–Lundberg risk process perturbed by a diffusion. We find an explicit expression for the reinsurance strategy that maximizes the adjustment coefficient for the classical risk process perturbed by a diffusion. Also, for this risk process, we use stochastic Perron’s method to prove that the minimum probability of ruin is the unique viscosity solution of its Hamilton–Jacobi–Bellman equation with appropriate boundary conditions. Finally, we prove that, under an appropriate scaling of the classical risk process, the minimum probability of ruin converges to the minimum probability of ruin under the diffusion approximation.

Optimal mean-variance reinsurance and investment strategy with constraints in a non-Markovian regime-switching model

This paper is devoted to study the proportional reinsurance/new business and investment problem under the mean-variance criterion in a continuous-time setting. The strategies are constrained in the non-negative cone and all coefficients in the model except the interest rate are stochastic processes adapted the filtration generated by a Markov chain. With the help of a backward stochastic differential equation driven by the Markov chain, we obtain the optimal strategy and optimal cost explicitly under this non-Markovian regime-switching model. The cases with one risky asset and Markov regime-switching model are considered as special cases.

Proportional reinsurance and investment in multiple risky assets under borrowing constraint

ABSTRACTIn this paper, we consider the ruin probability minimization of an insurance company that buys proportional reinsurance and invests in markets where borrowing is constrained. We use a diffusion approximation model for the surplus process of this company and assume that the company invests its surplus into a riskless and multiple risk assets that are modeled as geometric Brownian motions. To find the results, we introduce an auxiliary market parametrized with fictitious processes to relax the borrowing constraint and apply the techniques of stochastic optimal control. In this way, we find the optimal proportional reinsurance and investment strategy of an insurance company investing into multiple risky assets to minimize its ruin probability under the borrowing constraint. Furthermore, from our solutions, we show how our results connect to economic survival analysis and how investment and reinsurance strategies are related.

Robust optimal investment–reinsurance strategies for an insurer with multiple dependent risks

This paper considers a robust optimal investment and reinsurance problem with multiple dependent risks for an Ambiguity-Averse Insurer (AAI), who is uncertain about the model parameters. We assume that the surplus of the insurance company can be allocated to the financial market consisting of one risk-free asset and one risky asset whose price process satisfies square root factor process. Under the objective of maximizing the expected utility of the terminal surplus, by adopting the technique of stochastic control, closed-form expressions of the robust optimal strategy and the corresponding value function are derived. The verification theorem is also provided. Finally, by presenting some numerical examples, the impact of some parameters on the optimal strategy is illustrated and some economic explanations are also given. We find that the robust optimal reinsurance strategies under the generalized mean–variance premium are very different from that under the variance premium principle. In addition, ignoring model uncertainty risk will lead to significant utility loss for the AAI.