Nash equilibrium strategy for a multi-period mean–variance portfolio selection problem with regime switching

Wu, Huiling; Hua, Cunqing

doi:10.1016/j.econmod.2014.12.024

Cited by 33 publications

(22 citation statements)

References 15 publications

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“…We present this tendency in the left-hand side of Figure 2. This result is consistent with the result in Wu and Chen [24], where they show a similar result when the volatility of the risk aversion changes. Wu and Chen [24] point out that the volatility of the risk aversion parameter, as a component of risk, plays an important role in the equilibrium value function.…”

Section: Sensitivity Analysis Of the Risk Aversion Coefficientsupporting

confidence: 92%

“…This result is consistent with the result in Wu and Chen [24], where they show a similar result when the volatility of the risk aversion changes. Wu and Chen [24] point out that the volatility of the risk aversion parameter, as a component of risk, plays an important role in the equilibrium value function. As our risk aversion parameter is state-dependent, even though every risk aversion coefficient increases with the same volatility, the volatility of our risk aversion parameter is still changing.…”

Section: Sensitivity Analysis Of the Risk Aversion Coefficientsupporting

confidence: 92%

“…Further, the equilibrium strategy is independent of the initial information (initial wealth, initial wage, and initial interest rate), which is quite different from the precommitment strategy. This result is consistent with the results in Wu [30] and Wu and Chen [24]. The reason is that the equilibrium investor makes decision based on the forthcoming information, while the precommitment investor aims to find the globally optimal strategy from the state of initial time.…”

Section: Remark 14supporting

confidence: 89%

“…Further, Björk and Murgoci [19] and Björk et al [20] extend their own work in Björk and Murgoci [17] and analyze the game theory in detail under the discretetime setting and continuous-time setting, respectively. More results with this approach can be found in Wang and Forsyth [21], Li et al [22], Wei et al [23], Wu and Chen [24], Zhao et al [25], Wu et al [26], Zeng et al [27], and so on.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nash Equilibrium Strategy for a DC Pension Plan with State-Dependent Risk Aversion: A Multiperiod Mean-Variance Framework

Wang

Chen

2018

Discrete Dynamics in Nature and Society

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This paper investigates a defined contribution (DC) pension plan investment problem during the accumulation phase under the multiperiod mean-variance criterion. Different from most studies in the literature, where the investor's risk aversion attitude is state-independent, we choose a state-dependent risk aversion parameter, which is a fractional function of the current wealth level. Moreover, we incorporate the wage income factor into our model, which leads to a more complicated problem than the portfolio selection problems that appeared in relevant papers. Due to the time inconsistency of the resulting problem, we derive the explicit expressions for the equilibrium strategy and the corresponding equilibrium value function by adopting the game theoretic framework and using the extended Bellman equation. Further, two special cases are discussed. Finally, based on real data from the American market, some prominent features of the equilibrium strategy established in our theoretical derivations are provided by comparing them with the results in the existing literature.Hindawi

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Section: Sensitivity Analysis Of the Risk Aversion Coefficientsupporting

confidence: 92%

Section: Sensitivity Analysis Of the Risk Aversion Coefficientsupporting

confidence: 92%

Section: Remark 14supporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nash Equilibrium Strategy for a DC Pension Plan with State-Dependent Risk Aversion: A Multiperiod Mean-Variance Framework

Wang

Chen

2018

Discrete Dynamics in Nature and Society

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“…Recently, PPS has become one of the most active research topics in the fields of economic analysis (e.g., see Tofighian and Naderi 2015;Lee et al 2006;Wu and Chen 2015), R&D projects (e.g., see Fang et al 2008;Bhattacharyya et al 2011;Hassanzadeh et al 2014), supplier selection (e.g., see Hosseininasab and Ahmadi 2015;Vazhayil and Balasubramanian 2014;Lorca and Prina 2014), etc. Any model to solve this problem should consider relations between projects, uncertainties associated with incomes and risk issues so that the obtained results to be more valid.…”

Section: Introductionmentioning

confidence: 99%

Multi-period project portfolio selection under risk considerations and stochastic income

et al. 2018

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This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

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Scenario-based optimization robust model project portfolio selection under risk considerations

Ramedani

Didehkhani

Mehrabian

2022

Neural Comput & Applic

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Nash equilibrium strategy for a multi-period mean–variance portfolio selection problem with regime switching

Cited by 33 publications

References 15 publications

Nash Equilibrium Strategy for a DC Pension Plan with State-Dependent Risk Aversion: A Multiperiod Mean-Variance Framework

Nash Equilibrium Strategy for a DC Pension Plan with State-Dependent Risk Aversion: A Multiperiod Mean-Variance Framework

Multi-period project portfolio selection under risk considerations and stochastic income

Scenario-based optimization robust model project portfolio selection under risk considerations

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