Uncertainty index and stock volatility prediction:evidence from international markets

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.

Analysis of High Frequency Data in Finance:A Survey

This study examines the use of high frequency data in finance,including volatility estimation and jump tests.High frequency data allows the construction of model-free volatility measures for asset returns.Realized variance is a consistent estimator of quadratic variation under mild regularity conditions.Other variation concepts,such as power variation and bipower variation,are useful and important for analyzing high frequency data when jumps are present.High frequency data can also be used to test jumps in asset prices.We discuss three jump tests:bipower variation test,power variation test,and variance swap test in this study.The presence of market microstructure noise complicates the analysis of high frequency data.The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise.Finally,some applications of jump tests in asset pricing are discussed in this article.