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

Zhifeng Dai, Tingyu Li and Mi Yang

Journal of Forecasting, 2022, vol. 41, issue 5, 980-996

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.

Date: 2022
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Citations: View citations in EconPapers (19)

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https://doi.org/10.1002/for.2841

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:41:y:2022:i:5:p:980-996

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