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.
Keywords: Financial engineering; Mathematical finance; Derivatives; Hedging; Calibration; Numerical optimization
Views expressed in the paper are those of the authors and do not necessarily reflect those of the Bank of Japan or Institute for Monetary and Economic Studies.