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A Review of New Developments in Finance with Deep Learning: Deep Hedging and Deep Calibration

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  • Yuji Shinozaki

    (Deputy Director, Institute for Monetary and Economic Studies, Bank of Japan (currently, Associate Professor, Musashino University, E-mail:y-shino@musashino-u.ac.jp))

Abstract

The application of machine learning to the field of finance has recently become the subject of active discussions. In particular, the deep learning is expected to significantly advance the techniques of hedging and calibration. As these two techniques play a central role in financial engineering and mathematical finance, the application to them attracts attentions of both practitioners and researchers. Deep hedging, which applies deep learning to hedging, is expected to make it possible to analyze how factors such as transaction costs affect hedging strategies. Since the impact of these factors was difficult to be assessed quantitatively due to the computational costs, deep hedging opens possibilities not only for refining and automating hedging operations of derivatives but also for broader applications in risk management. Deep calibration, which applies deep learning to calibration, is expected to make the parameter optimization calculation, which is an essential procedure in derivative pricing and risk management, faster and more stable. This paper provides an overview of the existing literature and suggests future research directions from both practical and academic perspectives. Specifically, the paper shows the implications of deep learning to existing theoretical frameworks and practical motivations in finance and identifies potential future developments that deep learning can bring about and the practical challenges.

Suggested Citation

  • Yuji Shinozaki, 2024. "A Review of New Developments in Finance with Deep Learning: Deep Hedging and Deep Calibration," IMES Discussion Paper Series 24-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
  • Handle: RePEc:ime:imedps:24-e-02
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    References listed on IDEAS

    as
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    3. 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.
    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.
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    7. A. E. Whalley & P. Wilmott, 1997. "An Asymptotic Analysis of an Optimal Hedging Model for Option Pricing with Transaction Costs," Mathematical Finance, Wiley Blackwell, vol. 7(3), pages 307-324, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Financial engineering; Mathematical finance; Derivatives; Hedging; Calibration; Numerical optimization;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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