Forecasting stock return volatility in data-rich environment: A new powerful predictor

Zhifeng Dai, Xiaotong Zhang and Tingyu Li

The North American Journal of Economics and Finance, 2023, vol. 64, issue C

Abstract: We forecast stock return volatility by using the partial least squares approach that extract a powerful predictor from data-rich environment. Empirical results indicate that the new index has superior out-of-sample forecasting performance than the existing indexes, and the discovery is consistent with the in-sample predictive power. Specifically, the application of the new-index is extended to the allocation of investment portfolios to support mean–variance investors obtain considerable economic gains. In addition, our results are robust to various checks. Overall, our findings confirm that the partial least squares approach can effectively improve stock return volatility forecasts in a data-rich environment, successfully outperforming the competitive models and far surpassing the benchmark model.

Keywords: Partial least squares approach; Stock return volatility; Out-of-sample forecast; Asset allocation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940822001802

DOI: 10.1016/j.najef.2022.101845

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