journal article·2021Open access

Using Big Data to Discover Chaos in China’s Futures Market During COVID-19

Lin Tie, Bin Huang, Bin Pan, Guang Sun

Computers, Materials & Continua· Volume 69· Issue 3

Abstract

COVID-19 was first reported in China and quickly spread throughout the world. Weak investor confidence in government efforts to control the pandemic seriously affected global financial markets. This study investigated chaos in China’s futures market during COVID-19, focusing on the degree of chaos at different periods during the pandemic. We constructed a phase diagram to observe the attractor trajectory of index futures (IFs). During the COVID-19 outbreak, overall chaos in China’s futures market was increasing, and there was a clear correlation between market volatility and the macroenvironment (mainly government regulation). The Hurst index, calculated by rescaled range (R/S) analysis, was 0.46. The price and return of IFs showed long-term correlation and fractal characteristics; the relevant dimensions of the futures market were 2.17. Overall, under the influence of an emergency (COVID-19), chaos in China’s financial market intensified, creating a need for timely government intervention and macrocontrol of the market. This study’s findings can help improve the government’s understanding of the phenomenon of financial chaos caused by emergencies. This study also provides theoretical guidance for controlling financial chaos and maintaining healthy economic development when faced with similar events in the future.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Complex Systems and Time Series Analysis · Market Dynamics and Volatility · Financial Risk and Volatility Modeling

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (27)

Show source-provided references
1. Chaotic characteristics of the SSE composite index time series
2. Chaotic analysis and dimension reduction of Shanghai composite index time series
3. Multifractal detrended cross-correlation analysis of agricultural futures markets10.1016/j.chaos.2010.11.005 ↗
4. Fractal and multifractal features in China’s agricultural commodity futures prices
5. Daily volatility behavior in Chinese futures markets10.1016/j.intfin.2004.01.002 ↗
6. Study on nonlinear fluctuation characteristics in Shanghai metal futures market
7. Intelligent forecasting model of covid-19 novel coronavirus outbreak empowered with deep extreme learning machine10.32604/cmc.2020.011155 ↗
8. Machine learning and classical forecasting methods based decision support systems for covid-1910.32604/cmc.2020.011335 ↗
9. Covid-19 public opinion and emotion monitoring system based on time series thermal new word mining10.32604/cmc.2020.011316 ↗
10. Dynamical behavior and sensitivity analysis of a delayed coronavirus epidemic model10.32604/cmc.2020.011534 ↗
11. Atrocious impinging of covid-19 pandemic on software development industries10.32604/csse.2021.014929 ↗
12. Fuzzy based decision making approach for evaluating the severity of covid-19 pandemic in cities of kingdom of saudi arabia10.32604/cmc.2020.013215 ↗
13. A hybrid deep learning model for covid-19 prediction and current status of clinical trials worldwide10.32604/cmc.2020.012423 ↗
14. The impact of COVID-19 on Spanish health professionals: A description of physical and psychological effects10.32604/IJMHP.2020.011615 ↗
15. Numerical analysis of novel coronavirus (2019-ncov) pandemic model with advection10.32604/cmc.2021.012396 ↗
16. F. Imbert and Y. Li, “Dow falls more than 30 points, but Nasdaq ekes out fifth straight gain. USA: CNBC, 2020. [Online]. Available: https://www.cnbc.com/2020/06/17/stock-market-futures-open-to-close-news.html.
17. Annotating expressions of opinions and emotions in language10.1007/s10579-005-7880-9 ↗
18. The global macroeconomic impacts of covid-19: Seven scenarios10.1162/asep_a_00796 ↗
19. The impact of COVID-19 pandemic on stock markets: An empirical analysis of world major stock indices10.13106/jafeb.2020.vol7.no7.463 ↗
20. Covid-19 pandemic data predict the stock market10.32604/csse.2021.015309 ↗
21. The effects of a ‘black swan’ event (COVID19) on herding behavior in cryptocurrency markets: Evidence from cryptocurrency USD, EUR, JPY and KRW markets10.2139/ssrn.3586511 ↗
22. D. Aloui, S. Goutte, K. Guesmi and R. Hchaichi, “COVID 19’s impact on crude oil and natural gas S&P GS Indexes,” Working Paper, 2020. [Online]. Available: https://halshs.archives-ouvertes.fr/halshs-02613280/document.10.2139/ssrn.3587740 ↗
23. Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures10.1016/j.chaos.2020.109896 ↗
24. COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach10.1016/j.irfa.2020.101496 ↗
25. WHO Director-General’s, “WHO Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020,” The media briefing on COVID-19, 2020. [Online]. Available: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---20-march-2020.
26. Nonlinear dynamics, delay times and embedding windows10.1016/S0167-2789(98)00240-1 ↗
27. State space reconstruction parameters in the analysis of chaotic time series—The role of the time window length10.1016/0167-2789(96)00054-1 ↗