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Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion

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  • Hu, Mingshang
  • Ji, Shaolin

Abstract

In this paper, we study a stochastic recursive optimal control problem in which the cost functional is described by the solution of a backward stochastic differential equation driven by G-Brownian motion. Under standard assumptions, we establish the dynamic programming principle and the related fully nonlinear HJB equation in the framework of G-expectation. Finally, we show that the value function is the viscosity solution of the obtained HJB equation.

Suggested Citation

  • Hu, Mingshang & Ji, Shaolin, 2017. "Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 127(1), pages 107-134.
    • Handle: RePEc:eee:spapps:v:127:y:2017:i:1:p:107-134
    DOI: 10.1016/j.spa.2016.06.002
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    References listed on IDEAS

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    1. Larry G. Epstein & Shaolin Ji, 2013. "Ambiguous Volatility and Asset Pricing in Continuous Time," The Review of Financial Studies, Society for Financial Studies, vol. 26(7), pages 1740-1786.
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    3. Hu, Mingshang & Ji, Shaolin & Peng, Shige & Song, Yongsheng, 2014. "Comparison theorem, Feynman–Kac formula and Girsanov transformation for BSDEs driven by G-Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 124(2), pages 1170-1195.
    4. Epstein, Larry G. & Ji, Shaolin, 2014. "Ambiguous volatility, possibility and utility in continuous time," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 269-282.
    5. Bruno Bouchard & Ludovic Moreau & Marcel Nutz, 2012. "Stochastic target games with controlled loss," Papers 1206.6325, arXiv.org, revised Apr 2014.
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    8. Revaz Tevzadze & Teimuraz Toronjadze & Tamaz Uzunashvili, 2013. "Robust utility maximization for a diffusion market model with misspecified coefficients," Finance and Stochastics, Springer, vol. 17(3), pages 535-563, July.
    9. N. El Karoui & S. Peng & M. C. Quenez, 1997. "Backward Stochastic Differential Equations in Finance," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 1-71, January.
    10. Bruno Bouchard & Marcel Nutz, 2016. "Stochastic Target Games and Dynamic Programming via Regularized Viscosity Solutions," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 109-124, February.
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    Cited by:

    1. Hanwu Li & Falei Wang, 2019. "Stochastic Optimal Control Problem with Obstacle Constraints in Sublinear Expectation Framework," Journal of Optimization Theory and Applications, Springer, vol. 183(2), pages 422-439, November.
    2. Wang, Bingjun & Yuan, Mingxia, 2019. "Forward-backward stochastic differential equations driven by G-Brownian motion," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 39-47.
    3. Falei Wang & Guoqiang Zheng, 2021. "Backward Stochastic Differential Equations Driven by G-Brownian Motion with Uniformly Continuous Generators," Journal of Theoretical Probability, Springer, vol. 34(2), pages 660-681, June.
    4. Cheng, Bingqian & Wang, Hao & Zhang, Lihong, 2024. "Robust investment for insurers with correlation ambiguity," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 247-257.

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