Abstract
Extreme climate change has greatly damaged society and human beings, which has been verified to affect financial markets. In this paper, we detect the predictive performance of the air quality index (AQI) on stock market volatility under a decomposed GARCH-MIDAS model framework. In addition, considering that weather variables have significant seasonal characteristics, we further investigate which component of the AQI is the most powerful driver of stock volatility, so STL decomposition is adopted to divide the AQI into three sub-sequences. We further construct several extended models. The empirical results show that the model considering the trend component is superior to other competing models.
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