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

Abstract

Abstract 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.

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