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

Gaetan Bakalli, St\'ephane Guerrier and Olivier Scaillet

Papers from arXiv.org

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.

Date: 2022-08
New Economics Papers: this item is included in nep-for
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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 (2021)
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