Work

On Consistency of Signature Using Lasso

Year: 2025

Type: article

Abstract: The signature transform is a powerful tool for feature extraction in time series analysis, yet its statistical properties remain underexplored. In their paper in Operations Research, Guo et al. examine the consistency of signature when used with Lasso regression. The study establishes conditions under which Lasso achieves consistent feature selection, both asymptotically and in finite samples. A key finding is that the Itô signature performs better for processes resembling Brownian motion, whereas the Stratonovich signature is more effective for mean-reverting processes. The authors further demonstrate the practical relevance of their results by applying signature-based Lasso regression to option pricing, highlighting its potential in financial modeling. These insights provide a crucial theoretical foundation for improving predictive performance across various domains, including operations research and machine learning.

Source: Operations Research

DOI: 10.1287/opre.2024.1133

Authors: Xin Guo, Binnan Wang, Ruixun Zhang, Chaoyi Zhao

Institutions: University of California, Berkeley, Peking University, Massachusetts Institute of Technology

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Peking University, Fundamental Research Funds for the Central Universities

Awards: G3782347217, G6373215931, G995801844