A penalized two-pass regression to predict stock returns with time-varying risk premia

Gaetan Bakalli, Stéphane Guerrier and Olivier Scaillet

Journal of Econometrics, 2023, vol. 237, issue 2

Abstract: We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no-arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.

Keywords: Two-pass regression; Predictive modeling; Large panel; Factor model; LASSO penalization (search for similar items in EconPapers)
JEL-codes: C13 C23 C51 C52 C53 C55 C58 G12 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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Working Paper: A penalized two-pass regression to predict stock returns with time-varying risk premia (2023)
Working Paper: A penalized two-pass regression to predict stock returns with time-varying risk premia (2022)
Working Paper: A penalized two-pass regression to predict stock returns with time-varying risk premia (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:2:s0304407622002147

DOI: 10.1016/j.jeconom.2022.12.004

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