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Deep Learning In Finance: A Review Of Deep Hedging And Deep Calibration Techniques

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  • YUJI SHINOZAKI

    (School of Business Administration, Hitotsubashi University Business School, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8439, Japan)

Abstract

This paper presents a comprehensive review of the applications of deep learning in the field of finance, focusing specifically on hedging and calibration techniques. Deep hedging, which applies deep learning to solve the risk minimization problem of hedging directly without the risk-neutral valuation framework, is expected to significantly refine hedging technique and expand its range of applications beyond financial derivatives. Deep calibration is expected to make the parameter optimization, which is an essential procedure within the risk-neutral valuation, faster and more stable. The review summarizes the existing literature and suggests future research directions from both practical and academic perspectives. Recent developments in related areas, such as Neural Stochastic Differential Equations, are also briefly highlighted as emerging directions of interest.

Suggested Citation

  • Yuji Shinozaki, 2025. "Deep Learning In Finance: A Review Of Deep Hedging And Deep Calibration Techniques," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 28(03n04), pages 1-44, June.
    • Handle: RePEc:wsi:ijtafx:v:28:y:2025:i:03n04:n:s021902492530001x
    DOI: 10.1142/S021902492530001X
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