Machine Learning for Continuous-Time Finance

Victor Duarte, Diogo Duarte and Dejanir H. Silva

No 10909, CESifo Working Paper Series from CESifo

Abstract: We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito’s lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights.

Date: 2024
New Economics Papers: this item is included in nep-big and nep-cmp
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