Uncertainty index and stock volatility prediction: evidence from international markets

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.

PEER EFFECT OF CORPORATE R&D INNOVATION FROM THE PERSPECTIVE OF UNCERTAINTY

“…Economic policy uncertainty affects larger macroeconomics (Handley & Limão, 2017;Shaikh, 2020;Gong et al, 2022) thus causing the decline of employment and output, impact the economic cycle, and trigger economic recession. Simultaneously, it will also have a significant effect on the behavior decision of microeconomic entities, thereby driving corporates to either strengthen or weaken their own investment motives to seek higher market profits or avoid market risks (Chen et al, 2018(Chen et al, , 2019Caldara et al, 2020).…”