Deep stochastic optimization in finance

A. Max Reppen (), H. Mete Soner () and Valentin Tissot-Daguette ()
Additional contact information
A. Max Reppen: Boston University
H. Mete Soner: Princeton University
Valentin Tissot-Daguette: Princeton University

Digital Finance, 2023, vol. 5, issue 1, No 5, 111 pages

Abstract: Abstract This paper outlines, and through stylized examples evaluates a novel and highly effective computational technique in quantitative finance. Empirical Risk Minimization (ERM) and neural networks are key to this approach. Powerful open source optimization libraries allow for efficient implementations of this algorithm making it viable in high-dimensional structures. The free-boundary problems related to American and Bermudan options showcase both the power and the potential difficulties that specific applications may face. The impact of the size of the training data is studied in a simplified Merton type problem. The classical option hedging problem exemplifies the need of market generators or large number of simulations.

Keywords: ERM; Neural networks; Hedging; American options; 91G60; 49N35; 65C05 (search for similar items in EconPapers)
JEL-codes: C02 C63 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s42521-022-00074-6

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