IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i6p224-d1402377.html
   My bibliography  Save this article

Revolutionizing Hedge Fund Risk Management: The Power of Deep Learning and LSTM in Hedging Illiquid Assets

Author

Listed:
  • Yige Wang

    (Numerix LLC, New York, NY 10017, USA)

  • Leyao Tong

    (Financial Services Forum, Washington, DC 20005, USA)

  • Yueshu Zhao

    (International Monetary Fund, Washington, DC 20431, USA)

Abstract

In the dynamic sphere of financial markets, hedge funds have emerged as a critical force, navigating through volatility with advanced risk management techniques yet grappling with the challenges posed by illiquid assets. This study aims to transcend traditional option pricing models, which struggle under the complexities of hedge fund investments, by exploring the applicability of machine learning in financial risk management. Leveraging Deep Neural Networks (DNNs) and Long Short-Term Memory (LSTM) cells, the research introduces a model-free, data-driven approach for discrete-time hedging problems. Through a comparative analysis of simulated data and the implementation of LSTM architectures, the paper elucidates the potential of these machine learning techniques to enhance the precision of risk assessments and decision-making processes in hedge fund investments. The findings reveal that DNNs and LSTMs offer significant advancements over conventional models, effectively capturing long-term dependencies and complex patterns within financial time series data. Consequently, the study underscores the transformative impact of machine learning on the methodologies employed in financial risk management, proposing a novel paradigm that promises to mitigate the intricacies of hedging illiquid assets. This research not only contributes to the academic discourse but also paves the way for the development of more adaptive and resilient investment strategies in the face of market uncertainties.

Suggested Citation

  • Yige Wang & Leyao Tong & Yueshu Zhao, 2024. "Revolutionizing Hedge Fund Risk Management: The Power of Deep Learning and LSTM in Hedging Illiquid Assets," JRFM, MDPI, vol. 17(6), pages 1-16, May.
  • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:6:p:224-:d:1402377
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/6/224/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/17/6/224/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    2. Hull, John & White, Alan, 2017. "Optimal delta hedging for options," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 180-190.
    3. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    4. 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.
    5. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    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. Xia, Kun & Yang, Xuewei & Zhu, Peng, 2023. "Delta hedging and volatility-price elasticity: A two-step approach," Journal of Banking & Finance, Elsevier, vol. 153(C).
    2. Ke Nian & Thomas F. Coleman & Yuying Li, 2018. "Learning minimum variance discrete hedging directly from the market," Quantitative Finance, Taylor & Francis Journals, vol. 18(7), pages 1115-1128, July.
    3. Christoffersen, Peter & Heston, Steven & Jacobs, Kris, 2010. "Option Anomalies and the Pricing Kernel," Working Papers 11-17, University of Pennsylvania, Wharton School, Weiss Center.
    4. Henri Bertholon & Alain Monfort & Fulvio Pegoraro, 2006. "Pricing and Inference with Mixtures of Conditionally Normal Processes," Working Papers 2006-28, Center for Research in Economics and Statistics.
    5. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    6. Minting Zhu & Mancang Wang & Jingyu Wu, 2024. "An Option Pricing Formula for Active Hedging Under Logarithmic Investment Strategy," Mathematics, MDPI, vol. 12(23), pages 1-20, December.
    7. Bodo Herzog & Sufyan Osamah, 2019. "Reverse Engineering of Option Pricing: An AI Application," IJFS, MDPI, vol. 7(4), pages 1-12, November.
    8. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    9. Wang, XiaoTian & Yang, ZiJian & Cao, PiYao & Wang, ShiLin, 2021. "The closed-form option pricing formulas under the sub-fractional Poisson volatility models," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    10. Augustyniak, Maciej & Badescu, Alexandru & Bégin, Jean-François, 2023. "A discrete-time hedging framework with multiple factors and fat tails: On what matters," Journal of Econometrics, Elsevier, vol. 232(2), pages 416-444.
    11. Rombouts, Jeroen V.K. & Stentoft, Lars, 2015. "Option pricing with asymmetric heteroskedastic normal mixture models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 635-650.
    12. Broadie, Mark & Detemple, Jerome & Ghysels, Eric & Torres, Olivier, 2000. "American options with stochastic dividends and volatility: A nonparametric investigation," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 53-92.
    13. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    14. Shvimer, Yossi & Herbon, Avi, 2020. "Comparative empirical study of binomial call-option pricing methods using S&P 500 index data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    15. Xianhua Peng & Xiang Zhou & Bo Xiao & Yi Wu, 2024. "A Risk Sensitive Contract-unified Reinforcement Learning Approach for Option Hedging," Papers 2411.09659, arXiv.org.
    16. Papantonis, Ioannis, 2016. "Volatility risk premium implications of GARCH option pricing models," Economic Modelling, Elsevier, vol. 58(C), pages 104-115.
    17. Gurupdesh S. Pandher, 2007. "Regression-based modeling of market option prices: with application to S&P500 options," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 475-496.
    18. Christoffersen, Peter & Jacobs, Kris, 2004. "The importance of the loss function in option valuation," Journal of Financial Economics, Elsevier, vol. 72(2), pages 291-318, May.
    19. René Garcia & Eric Ghysels & Eric Renault, 2004. "The Econometrics of Option Pricing," CIRANO Working Papers 2004s-04, CIRANO.
    20. Boris Ter-Avanesov & Homayoon Beigi, 2024. "MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing," Papers 2409.06724, arXiv.org, revised Oct 2024.

    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:gam:jjrfmx:v:17:y:2024:i:6:p:224-:d:1402377. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.