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Machine Learning for Continuous-Time Finance

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Listed:
  • Victor Duarte
  • Diogo Duarte
  • Dejanir H. Silva

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.

Suggested Citation

  • Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    • Handle: RePEc:ces:ceswps:_10909
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    Cited by:

    1. Zhouzhou Gu & Mathieu Lauri`ere & Sebastian Merkel & Jonathan Payne, 2024. "Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models," Papers 2406.13726, arXiv.org.
    2. Jésus Fernández-Villaverde & Galo Nuño & Jesse Perla & Jesús Fernández-Villaverde, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," CESifo Working Paper Series 11448, CESifo.

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