Abstract
This article proposes a new paradigm of asymmetric multifractality in financial time series, where the scaling feature varies over two adjacent intervals. The proposed approach first locates a change-point and then performs a multifractal detrended fluctuation analysis (MF-DFA) on each interval. The study investigates the impact of the COVID-19 pandemic on asymmetric multifractal scaling by analyzing financial indices of the G3+1 nations, including the world’s four largest economies, from January 2018 to November 2021. The results show common periods of local scaling with increasing multifractality after a change-point at the beginning of 2020 for the US, Japanese, and Eurozone markets. The study also identifies a significant transition in the Chinese market from a turbulent multifractal state to a stable monofractal state. Overall, this new approach provides valuable insights into the characteristics of financial time series and their response to extreme events.
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