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A penalized two-pass regression to predict stock returns with time-varying risk premia - new publication by Olivier Scaillet

In a new study, Olivier Scaillet, Director of GFRI, and his co-authors 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. A Monte Carlo results and the 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, the results demonstrate that the method the authors propose reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.

 

The paper was co-authored with Gaetan Bakalli and Stéphane Guerrier, and was accepted for publication in The Journal of Econometrics.

 

It is available here.

Jan 4, 2023

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