A penalized two-pass regression to predict stock returns with time-varying risk premia - New publication by Olivier Scaillet
In a new study, GFRI's Director and Professor of Finance and Statistics, Olivier Scaillet, develops 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.
The Monte Carlo results and 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 proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.
The paper is co-authored with Gaetan Bakalli from Emlyon and Stéphane Guerrier from GSEM, ans is published in the Journal of Econometrics.
Nov 6, 2023