Uncertainty index and stock volatility prediction: evidence from international markets

Xue Gong (), Weiguo Zhang (), Weijun Xu () and Zhe Li ()
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Xue Gong: South China University of Technology
Weiguo Zhang: South China University of Technology
Weijun Xu: South China University of Technology
Zhe Li: Nanjing Normal University

Financial Innovation, 2022, vol. 8, issue 1, 1-44

Abstract: Abstract This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective. We construct a composite UI based on the scaled principal component analysis (s-PCA) method and demonstrate that it exhibits significant in- and out-of-sample predictabilities for realized variances in global stock markets. This predictive power is more powerful than those of two commonly employed competing methods, namely, PCA and the partial least squares (PLS) methods. The result is robust in several checks. Further, we explain that s-PCA outperforms other dimension-reduction methods since it can effectively increase the impacts of strong predictors and decrease those of weak factors. The implications of this research are significant for investors who allocate assets globally.

Keywords: Uncertainty index; High-frequency data; Realized variance; Scaled-PCA; C22; G15; G17; G41 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (17)

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DOI: 10.1186/s40854-022-00361-6

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