Machine Learning for Continuous-Time Finance

• 成果类型: Article
• 署名作者: Duarte, Victor; Duarte, Diogo; Silva, Dejanir H.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; State University System of Florida; Florida International University; Purdue University System; Purdue University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhae043
• 发表日期: 2024
• 页码: 3217
• 关键词: AMERICAN OPTIONS; returns; models; consumption; macro; game; go

摘要

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.