Cross-sectional uncertainty and stock market volatility: New evidence

Article Economics

Forecasting crude oil market volatility using variable selection and common factor

Yaojie Zhang et al.

Summary: This research aims to enhance the predictability of aggregate oil market volatility using a large macroeconomic database consisting of 127 variables. Machine learning techniques are employed for variable selection and dimension reduction. The findings suggest that the supervised PCA regression model successfully predicts oil market volatility and provides forecasting gains. Furthermore, the study highlights the importance of option-implied volatility as the most powerful predictor.

INTERNATIONAL JOURNAL OF FORECASTING (2023)

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Article Business, Finance

Cross-sectional uncertainty and expected stock returns

Deshui Yu et al.

Summary: This study finds that cross-sectional uncertainty (CSU) has significant power in predicting both in-sample and out-of-sample monthly stock returns with annual R-2 of 11.89% and 6.34% respectively, surpassing popular predictors. A bivariate combination forecast using CSU with one of the alternative predictors generates an annual out-of-sample R-2 up to 18.08%. CSU generates significant economic gains for a mean-variance investor, with a utility gain of over 400 basis points per annum. A vector autoregression decomposition shows that the predictability mainly comes from a cash flow channel.

JOURNAL OF EMPIRICAL FINANCE (2023)

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Article Business, Finance

Policy uncertainty and carbon neutrality: Evidence from China

Qing Zeng et al.

Summary: This paper examines the effects of China's economic policy uncertainty and climate policy uncertainty on the volatility of the Wind carbon neutral concept index. It finds that both uncertainties have significant impacts on the index volatility. Moreover, when the market faces more volatile risks, climate policy uncertainty performs better than economic policy uncertainty in forecasting index volatility.

FINANCE RESEARCH LETTERS (2022)

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Article Business, Finance

Which predictor is more predictive for Bitcoin volatility? And why?

Chao Liang et al.

Summary: This study investigates the predictive power of five crucial predictors for Bitcoin volatility using the GARCH-MIDAS model, and finds that GVZ exhibits the strongest predictability among other competing predictors.

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS (2022)

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Article Business, Finance