The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

Abstract

Extreme haze was often observed at many locations in Beijing–Tianjin–Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014–2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing–Tianjin–Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze’s appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures

“…We use TQCC to study stock price extreme comovements and risk contagions among Dow Jones' 30 stocks. In the literature, among many applications, Wu et al (2012) illustrated the idea of studying the equity market index extreme co-movement using TQCC, and Deng and Zhang (2020) used TQCC to study haze extreme contagions in a vast region in China. In their applications, a generalised extreme value (GEV) fitting was implemented.…”