 Good variance, bad variance, and stock return predictabilityYaojie Zhang, Feng Ma, Chao Liang and Yi Zhang International Journal of Finance & Economics, 2021, vol. 26, issue 3, 4410-4423 Abstract: In the stock market, past winners and losers usually have different attitudes to variance risk. In light of this, we decompose stock variance into good and bad variances, and further construct a composite signed variance. The recursively estimated coefficient of standard variance is volatile over time, but the ones of good, bad, and signed variances are fairly stable. This suggests the high efficiency of our decomposition approach. We provide convincing evidence that bad variance and composite signed variance can significantly predict future stock returns both in‐ and out‐of‐sample. Furthermore, a mean–variance investor can realize substantial economic gains by using our bad and signed variances relative to the traditional variance predictor and simple mean benchmark. Our decomposition approach can outperform other similar but complex techniques, including threshold regression and Markov regime switching. The results are consistent across multiple robustness tests. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:ijfiec:v:26:y:2021:i:3:p:4410-4423 Ordering information: This journal article can be ordered from Access Statistics for this article International Journal of Finance & Economics is currently edited by Mark P. Taylor, Keith Cuthbertson and Michael P. Dooley More articles in International Journal of Finance & Economics from John Wiley & Sons, Ltd. |
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