The commodity risk premium and neural networks

Hossein Rad, Rand Kwong Yew Low, Joëlle Miffre and Robert Faff

Journal of Empirical Finance, 2023, vol. 74, issue C

Abstract: The paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and the risk premium of commodity futures contracts. The linear models use shrinkage methods based on either naive averaging or principal components. The nonlinear models use feedforward deep neural networks (DNN) either as stand-alone or in conjunction with a long short-term memory network (LSTM). Out of the four specifications considered, the LSTM-DNN architecture best captures the risk premium, which underscores the need to estimate models that are both nonlinear and recurrent. The superior performance of the LSTM-DNN portfolio persists after accounting for transaction costs or illiquidity and is unrelated to previously-documented commodity risk factors.

Keywords: Recurrent neural network; Commodity risk premium; Macroeconomic and financial variables; Nonlinear and linear predictive models (search for similar items in EconPapers)
JEL-codes: C52 C55 C58 G13 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:74:y:2023:i:c:s0927539823001007

DOI: 10.1016/j.jempfin.2023.101433

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