The Study of Mean-Variance Risky Asset Management with State-Dependent Risk Aversion under Regime Switching Market | Zendy

Shuang Li | Zendy; Yu Yang | Zendy; Yanli Zhou | Zendy; Yonghong Wu | Zendy; Xiangyu Ge | Zendy

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The Study of Mean-Variance Risky Asset Management with State-Dependent Risk Aversion under Regime Switching Market

Author(s) -

Shuang Li,

Yu Yang,

Yanli Zhou,

Yonghong Wu,

Xiangyu Ge

Publication year - 2021

Publication title -

journal of function spaces

Language(s) - English

Resource type - Journals

eISSN - 2314-8896

pISSN - 2314-8888

DOI - 10.1155/2021/5476781

Subject(s) - asset (computer security) , variance (accounting) , risk aversion (psychology) , liability , bellman equation , hamilton–jacobi–bellman equation , actuarial science , stochastic control , payment , economics , markov process , investment (military) , markov chain , econometrics , investment strategy , risk management , financial market , expected utility hypothesis , microeconomics , computer science , optimal control , mathematical optimization , mathematical economics , mathematics , finance , statistics , computer security , accounting , machine learning , politics , political science , law , profit (economics)

How do investors require a distribution of the wealth among multiple risky assets while facing the risk of the uncontrollable payment for random liabilities? To cope with this problem, firstly, this paper explores the approach of asset-liability management under the state-dependent risk aversion with only risky assets, which has been considered under a continuous-time Markov regime-switching setting. Next, based on this realistic modelling, an extended Hamilton-Jacob-Bellman (HJB) system has been necessarily established for solving the optimization problem of asset-liability management. It has been derived closed-form analytical expressions applied in the time-inconsistent investment with optimal control theory to see that happens to the optimal value of the function. Ultimately, numerical examples presented with comparisons of the analytical results under different market conditions are exposed to analyse numerically the developed mean variance asset liability management strategy. We find that our proposed model can explain the financial phenomena more effectively and accurately.

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