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• War attention is an important driver of stock volatility. • The war attention index uses NLP and dimensionality reduction. • New GARCH-MIDAS models, adding war-attention extreme effects, are used. • War attention index enhances S&P500 volatility predictability. This paper aims to explore the impact of war attention on stock volatility predictability by constructing a new war attention index and employing an extended GARCH-MIDAS-ES model. The war attention index is developed by incorporating the Google search volume data for 56 war-related keywords using natural language processing methods and dimensionality reduction techniques. Since war attention is considered an exogenous shock, we modify the new extended MIDAS model by incorporating the extreme effects caused by war attention into the GARCH-MIDAS-ES framework. Compelling evidence demonstrates that our proposed war attention index is a statistically significant driver of S&P 500 volatility, and our extended model exhibits higher out-of-sample predictive accuracy as it captures both the normal and extreme effects of war attention on stock volatility within the MIDAS framework. By examining how war attention affects stock volatility predictability during the ongoing Russia–Ukraine war, we observe that the extended model's forecasting performance deteriorates as the forecasting horizon increases to a relatively large extent, which is in line with the findings of Andrei and Hasler (2015). [ABSTRACT FROM AUTHOR]

This research investigates the predictability of economic uncertainty indexes on the volatility of Bitcoin (BTC) during COVID-19. The economic uncertainty indexes include US economic policy uncertainty (EPU), Twitter economic uncertainty (TEU), Twitter market uncertainty (TMU), geopolitical risk index (GPR), and trade policy uncertainty (TPU) index. The empirical findings show that the Twitter market uncertainty (TMU) and geopolitical risk (GPR) uncertainty index are valuable predictors of BTC volatility. Moreover, the combination forecasts information for all economic uncertainty indexes is useful for BTC volatility forecasting. Also, we find evidence during high and low volatility and the Russia-Ukraine war. Our results show that Twitter market uncertainty and geopolitical risk uncertainty index are effective predictors of Bitcoin volatility during high volatility periods. During the Russia-Ukraine war, economic policy uncertainty (EPU), the Twitter market uncertainty index, and combination forecast information for all uncertainty indexes are effective for Bitcoin volatility prediction. Our findings are robust with the alternative method MCS test. [ABSTRACT FROM AUTHOR]

In this paper, we propose a conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model that incorporates economic policy uncertainty (EPU) to predict the crude oil futures price volatility (range). We apply the proposed model to West Texas Intermediate (WTI) oil futures price ranges and four EPU indices, namely the Global EPU, US EPU, China EPU and Russia EPU. Empirical results show that all the four EPU indices have a significantly negative impact on the oil futures price volatility, and the EPU indices are informative for forecasting the oil futures price volatility. Moreover, the China EPU index outperforms the other EPU indices in forecasting the oil futures price volatility. [ABSTRACT FROM AUTHOR]