IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2509.02267.html
   My bibliography  Save this paper

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs

Author

Listed:
  • Dong Yan
  • Nanyi Zhang
  • Junyi Guo

Abstract

In this paper, we first conduct a study of the portfolio selection problem, incorporating both exogenous (proportional) and endogenous (resulting from liquidity risk, characterized by a stochastic process) transaction costs through the utility-based approach. We also consider the intrinsic relationship between these two types of costs. To address the associated nonlinear two-dimensional Hamilton-Jacobi-Bellman (HJB) equation, we propose an innovative deep learning-driven policy iteration scheme with three key advantages: i) it has the potential to address the curse of dimensionality; ii) it is adaptable to problems involving high-dimensional control spaces; iii) it eliminates truncation errors. The numerical analysis of the proposed scheme, including convergence analysis in a general setting, is also discussed. To illustrate the impact of these two types of transaction costs on portfolio choice, we conduct through numerical experiments using three typical utility functions.

Suggested Citation

  • Dong Yan & Nanyi Zhang & Junyi Guo, 2025. "A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs," Papers 2509.02267, arXiv.org.
    • Handle: RePEc:arx:papers:2509.02267
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2509.02267
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vathana Ly Vath & Mohamed Mnif & Huyên Pham, 2007. "A model of optimal portfolio selection under liquidity risk and price impact," Finance and Stochastics, Springer, vol. 11(1), pages 51-90, January.
    2. Puneet Pasricha & Song-Ping Zhu & Xin-Jiang He, 2022. "A closed-form pricing formula for European options in an illiquid asset market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-18, December.
    3. Philipp Grohs & Fabian Hornung & Arnulf Jentzen & Philippe von Wurstemberger, 2018. "A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations," Papers 1809.02362, arXiv.org, revised Jan 2023.
    4. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    5. Xiaoling Mei & Huanjun Zhu & Chongzhu Chen, 2023. "Mean-variance portfolio selection with estimation risk and transaction costs," Applied Economics, Taylor & Francis Journals, vol. 55(13), pages 1436-1453, March.
    6. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    7. Merton, Robert C., 1971. "Optimum consumption and portfolio rules in a continuous-time model," Journal of Economic Theory, Elsevier, vol. 3(4), pages 373-413, December.
    8. Nitin R. Patel & Marti G. Subrahmanyam, 1982. "A Simple Algorithm for Optimal Portfolio Selection with Fixed Transaction Costs," Management Science, INFORMS, vol. 28(3), pages 303-314, March.
    9. Ana González & Gonzalo Rubio, 2011. "Portfolio choice and the effects of liquidity," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(1), pages 53-74, March.
    10. M. H. A. Davis & A. R. Norman, 1990. "Portfolio Selection with Transaction Costs," Mathematics of Operations Research, INFORMS, vol. 15(4), pages 676-713, November.
    11. Feng, Shih-Ping & Hung, Mao-Wei & Wang, Yaw-Huei, 2014. "Option pricing with stochastic liquidity risk: Theory and evidence," Journal of Financial Markets, Elsevier, vol. 18(C), pages 77-95.
    12. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2016. "Liquidity Risk And Instabilities In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-28, August.
    13. Holger Kraft, 2005. "Optimal portfolios and Heston's stochastic volatility model: an explicit solution for power utility," Quantitative Finance, Taylor & Francis Journals, vol. 5(3), pages 303-313.
    14. Feng, Shih-Ping & Hung, Mao-Wei & Wang, Yaw-Huei, 2016. "The importance of stock liquidity on option pricing," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 457-467.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong Yan & Ke Zhou & Zirun Wang & Xin-Jiang He, 2025. "Portfolio selection with exogenous and endogenous transaction costs under a two-factor stochastic volatility model," Papers 2510.21156, arXiv.org.
    2. Zhang, Hongyu & Guo, Xunxiang & Wang, Ke & Huang, Shoude, 2024. "The valuation of American options with the stochastic liquidity risk and jump risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    3. He, Xin-Jiang & Pasricha, Puneet & Lin, Sha, 2024. "Analytically pricing European options in dynamic markets: Incorporating liquidity variations and economic cycles," Economic Modelling, Elsevier, vol. 139(C).
    4. Marcos Escobar-Anel & Michel Kschonnek & Rudi Zagst, 2022. "Portfolio optimization: not necessarily concave utility and constraints on wealth and allocation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(1), pages 101-140, February.
    5. He, Xin-Jiang & Pasricha, Puneet & Lu, Tuantuan & Lin, Sha, 2024. "Vulnerable options with regime switching and stochastic liquidity," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    6. Li, Zhe & Zhang, Weiguo & Zhang, Yue & Yi, Zhigao, 2019. "An analytical approximation approach for pricing European options in a two-price economy," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    7. Johannes Muhle-Karbe & Max Reppen & H. Mete Soner, 2016. "A Primer on Portfolio Choice with Small Transaction Costs," Papers 1612.01302, arXiv.org, revised May 2017.
    8. An Chen & Thai Nguyen & Manuel Rach, 2021. "A collective investment problem in a stochastic volatility environment: The impact of sharing rules," Annals of Operations Research, Springer, vol. 302(1), pages 85-109, July.
    9. Yang Shen, 2020. "Effect of Variance Swap in Hedging Volatility Risk," Risks, MDPI, vol. 8(3), pages 1-34, July.
    10. M. Escobar-Anel & M. Kschonnek & R. Zagst, 2023. "Mind the cap!—constrained portfolio optimisation in Heston's stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 23(12), pages 1793-1813, November.
    11. Xin‐Jiang He & Hang Chen & Sha Lin, 2025. "A Closed‐Form Formula for Pricing European Options With Stochastic Volatility, Regime Switching, and Stochastic Market Liquidity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(5), pages 429-440, May.
    12. Escobar, Marcos & Ferrando, Sebastian & Rubtsov, Alexey, 2018. "Dynamic derivative strategies with stochastic interest rates and model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 49-71.
    13. Escobar, Marcos & Ferrando, Sebastian & Rubtsov, Alexey, 2016. "Portfolio choice with stochastic interest rates and learning about stock return predictability," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 347-370.
    14. Marcos Escobar-Anel & Ben Spies & Rudi Zagst, 2024. "Optimal consumption and investment in general affine GARCH models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(3), pages 987-1026, September.
    15. Dmitry Muravey, 2017. "Optimal investment problem with M-CEV model: closed form solution and applications to the algorithmic trading," Papers 1703.01574, arXiv.org, revised Jul 2018.
    16. Cheng, Yuyang & Escobar-Anel, Marcos, 2023. "A class of portfolio optimization solvable problems," Finance Research Letters, Elsevier, vol. 52(C).
    17. Elena Boguslavskaya & Dmitry Muravey, 2015. "An explicit solution for optimal investment in Heston model," Papers 1505.02431, arXiv.org, revised May 2015.
    18. Xiaoxiao Zheng & Xin Zhang, 2014. "Optimal investment-reinsurance policy under a long-term perspective," Papers 1406.7604, arXiv.org.
    19. Larsen, Linda Sandris & Munk, Claus, 2012. "The costs of suboptimal dynamic asset allocation: General results and applications to interest rate risk, stock volatility risk, and growth/value tilts," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 266-293.
    20. Rongju Zhang & Nicolas Langren'e & Yu Tian & Zili Zhu & Fima Klebaner & Kais Hamza, 2016. "Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach," Papers 1610.07694, arXiv.org, revised Jun 2019.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2509.02267. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.