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

Gaetan Bakalli, Stéphane Guerrier and Olivier Scaillet
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Gaetan Bakalli: University of Geneva
Stéphane Guerrier: University of Geneva

No 21-09, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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)
Pages: 36 pages
Date: 2021-01
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: 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 (2023)
Working Paper: A penalized two-pass regression to predict stock returns with time-varying risk premia (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2109

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