A penalized two-pass regression to predict stock returns with time-varying risk premia. (arXiv:2208.00972v1 [econ.EM])

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 …

arxiv regression returns risk stock time