The Lead-lag relations in the Commodity Futures Returns: A Machine Learning Approach

55 Pages Posted: 8 Mar 2020 Last revised: 9 Nov 2022

See all articles by Yufeng Han

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance

Lingfei Kong

Washington University in Saint Louis

Date Written: January 11, 2022

Abstract

This paper uses machine learning tools to study the lead-lag relations in commodity futures returns. We use LASSO to select the predictors because the number of predictors is large relative to the number of observations. We find significant full-sample and out-of-sample predictability. In the full sample, we find that LASSO can identify a sparse set of predictors that either come from economically linked commodities or are likely driven by excessive speculative trading. The out-of-sample forecasts based on LASSO generate statistically and economically large gains. When we use more complex machine learning models such as regression trees and neural networks to forecast commodity futures returns, the out-of-sample performance is worse than LASSO portfolios, suggesting that nonlinearities and interactions do not appear substantial in the data. Finally, we find that index trading due to financialization drives the excess comovement among commodity futures.

Keywords: Commodity Futures, LASSO, Machine Learning, Predictability, Serial Dependence, Financialization, Comovement

JEL Classification: C22, C58, G11, G12, G14

Suggested Citation

Han, Yufeng and Kong, Lingfei, The Lead-lag relations in the Commodity Futures Returns: A Machine Learning Approach (January 11, 2022). Available at SSRN: https://ssrn.com/abstract=3536046 or http://dx.doi.org/10.2139/ssrn.3536046

Yufeng Han (Contact Author)

University of North Carolina (UNC) at Charlotte - Finance ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

Lingfei Kong

Washington University in Saint Louis ( email )

1 Brookings Dr
Knight Hall and Bauer Hall
St Louis, MO 63130
United States

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