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Title: A penalized two-pass regression to predict stock returns with time-varying risk premia Authors:  Gaetan Bakalli - Auburn university (United States) [presenting]
Stephane Guerrier - University of Geneva (Switzerland)
Olivier Scaillet - University of Geneva and Swiss Finance Institute (Switzerland)
Abstract: A penalized two-pass regression with time-varying factor loadings is developed. 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.