Forecasting stock return volatility: The role of shrinkage approaches in a data-rich environment

This paper employs the prevailing shrinkage approaches, the lasso, adaptive lasso, elastic net, and ridge regression to predict stock return volatility with a large set of variables. The out-of-sample results reveal that shrinkage approaches exhibit superior performance relative to the benchmark of the autoregressive model and a series of competing models in terms of the out-of-sample R-square and the model confidence set. By using shrinkage methods to allocate portfolio, a mean-variance investor can obtain significant economic gains. Overall, our findings confirm that shrinkage approaches can effectively improve stock return volatility forecasting in a data-rich environment.

Automated summary

This paper employs shrinkage approaches to predict stock return volatility and finds that these methods outperform other models in terms of performance and confidence. Furthermore, using shrinkage methods for portfolio allocation results in significant economic gains.