Optimal proportional reinsurance and investment under partial information

We consider partial and complete information models to investigate how partial information has a unique quality over complete information for insurers. We find that optimal reinsurance and investment strategies for the partially informed insurer depend on prior beliefs, whereas those for the completely informed insurer do not. In addition, information quality can affect insurer behaviour, mainly through the relative difference between risk-adjusted market premium and risk-adjusted insurance premium projected on the financial markets.Numerical results indicate that partial information increases the conservativeness of insurer strategies.

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“…A CI example is found in Jang and Kim (2015) and PI examples are found in Peng and Hu (2013), Liang and Song (2015) and Peng et al (2018).…”

Section: Insurance Claimsmentioning

confidence: 95%

Optimal reinsurance and portfolio selection: Comparison between partial and complete information models

Jang

Kim²,

Lee³

2021

Euro Fin Management

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“…. Then, we can conclude that J 3 (π * ) = min π∈A J 3 (π) (for a detailed proof, one can refer to Theorem 4.3 in Peng and Hu [20]).…”

Section: Characterization Of Optimal Strategiesmentioning

confidence: 96%

Deterministic Investment Strategy in a Dc Pension Plan With Inflation Risk Under Mean-Variance Criterion

Peng¹,

Chen²

2020

Prob. Eng. Inf. Sci.

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This paper studies an optimal deterministic investment problem for a DC pension plan member with inflation risk. We describe the price processes of the inflation-indexed bond and the stock by a continuous diffusion process and a jump diffusion process with random parameters, respectively. The contribution rate linked to the income of the DC plan member is assumed to be a non-Markovian adapted process. Under the mean-variance criterion, we use Malliavin calculus to derive a characterization for the optimal deterministic investment strategy. In some special cases, we obtain the explicit expressions for the optimal deterministic strategies.

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“…They considered an optimal portfolio problem for a dealer who has access to some information that in general is smaller than the one generated by market events. More general results can refer to Peng and Hu [18], Wang and Wu [22] and Huang et al [9]. More specially, Peng and Hu [18] studied the optimal proportional reinsurance and investment strategy for an insurer that only has partial information at its disposal.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reinsurance and Investment Strategies under Mean-Variance Criteria: Partial and Full Information

Zhu¹,

Shi²

2019

Preprint

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This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic (LQ) optimal control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution for the Hamilton-Jacobi-Bellman (HJB) equation in the full information case. Some numerical illustrations are also provided.

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Optimal proportional reinsurance and investment under partial information

Cited by 21 publications

References 11 publications

Optimal reinsurance and portfolio selection: Comparison between partial and complete information models

Optimal reinsurance and portfolio selection: Comparison between partial and complete information models

Deterministic Investment Strategy in a Dc Pension Plan With Inflation Risk Under Mean-Variance Criterion

Optimal Reinsurance and Investment Strategies under Mean-Variance Criteria: Partial and Full Information

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