The complex network of the impact of the coronavirus (Qovid-19) on macroeconomic variables and the stock markets crash
Subject Areas : Financial engineering
matin
saneifar
1
(Department of Financial Engineering, Aliabad Katoul branch, Islamic Azad University, Aliabad Katoul, Iran.)
parviz
saeedi
2
(Department of Accounting and Management, Ali Abad Katoul Branch, Islamic Azad University , Ali Abad Katoul,Iran.)
Ebrahim
Abaasi
3
(Department of Accounting and Finance, Alzahra University, Tehran, Iran)
Hossein
Didehkhani
4
(Department of Financial Engineering, Aliabad Katoul branch, Islamic Azad University , Ali Abad Katoul, Iran.)
Keywords: Coronavirus, Complex network, Qovid 19, economic variables, stock markets,
Abstract :
The outbreak of the corona virus has led to strong negative reactions from stock markets in various countries, and the side effects of the virus have caused the fall in prices of many macroeconomic variables worldwide.These have drawn the attention of analysts and investors to the negative effects of the spread of the virus on stock markets.The goal of the study is to create a complex network of corona viruses on the stock market of 75 countries with oil, gold, silver and copper.The results show that the interconnectedness of the modern economy of stock markets and economic variables has made the health crisis to a global economic crisis.Corona virus has a direct negative impact on 35% of stock market markets, this virus has had the greatest impact on stock market markets in European and Asian countries,It has also had the least impact on the stock markets of the Arab and African countries.The coronavirus has indirectly affected stock market markets by affecting economic variables.The unprecedented drop in oil prices has caused the stock markets to fall 56 percent,and gold price fluctuations have affected 29 percent of these markets.Silver and copper prices have fallen between 25 and 32 percent in stock markets.
_||_
1) Ismail Pourmoghadam Hadi, Mohammadi Taimur, Faqhi Kashani Mohammad, Shakri Abbas (2017). "Presenting a new index to reflect stock market behavior using complex network analysis approach". Financial Economics Quarterly, 13th year, number 46. 25-39
2) Albulescu, C. (2020a). Coronavirus and financial volatility: 40 days of fasting and fear. arXiv preprint arXiv:2003.04005.
3) Albulescu, C. (2020b). Coronavirus and oil price crash. Available at SSRN 3553452.
4) Birch, J., A.A. Pantelous, and K. Zuev, The maximum number of 3-and 4-cliques within a planar maximally filtered graph. Physica A: Statistical Mechanics and its Applications, 2015. 417: p. 221-229.
5) Barratt, A., & Weigt, M. (2000). On the properties of small-world network models. The European Physical Journal B-Condensed Matter and Complex Systems, 13(3), 547-560.
6) Bollobás, B. (1981). The diameter of random graphs. Transactions of the American Mathematical Society, 267(1), 41-52.
7) Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163-177.
8) Drożdż, S., Grümmer, F., Ruf, F., & Speth, J. (2001). Towards identifying the world stock market cross-correlations: DAX versus Dow Jones. Physica A: Statistical Mechanics and its Applications, 294(1-2), 226-234.
9) George, S., & Changat, M. (2017). Network approach for stock market data mining and portfolio analysis. Paper presented at the 2017 International Conference on Networks & Advances in Computational Technologies (NetACT).
10) Gormsen, N. J., & Koijen, R. S. (2020). Coronavirus: Impact on stock prices and growth expectations. University of Chicago, Becker Friedman Institute for Economics Working Paper (2020-22).
11) Helbing, D. 2013. Globally networked risks and how to respond. Nature, 497(7447), 51.
12) Li, F., Identifying asymmetric movements of international stock market returns. Journal of Financial Econometrics, 2013. 12(3): p. 507-543.
13) Mantegna, R.N., Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 1999. 11(1): p. 193-197.
14) Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social networks, 27(1), 39-54.
15) Nobi, A., Lee, S., Kim, D. H., & Lee, J. W. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482-2489.
16) Nie, C.-X. and F.-T. Song, Constructing financial network based on PMFG and threshold method. Physica A: Statistical Mechanics and its Applications, 2018. 495: p. 104-113.
17) Pereira, E., Ferreira, P., & de Borges Pereira, H. B. (2020). COVID-19 in Stock Markets: A Complexity Perspective
18) Ramelli, S., & Wagner, A. F. (2020). Feverish stock price reactions to covid-19.
19) Selmi, R., & Bouoiyour, J. (2020). Global Market's Diagnosis on Coronavirus: A Tug of War between Hope and Fear.
20) Sornette, D. 2017. Why stock markets crash: critical events in complex financial systems. Princeton University Press.
21) Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421-10426.
22) Vandewalle, N., P. Boveroux, and F. Brisbois, Domino effect for world market fluctuations. The European Physical Journal B-Condensed Matter and Complex Systems, 2000. 15(3): p. 547-549.
23) West, D., Introduction to Graph Theory, Prntice-Hall. Englewood Cliffs, NJ, 2001.
24) Wagner, A. F. 2020. What the stock market tells us about the post-COVID-19 world. Nature Human Behavior. https://doi.org/10.1038/s41562-020-0869-y
25) Yan, B., Stuart, L., Tu, A., & Zhang, T. (2020). Analysis of the Effect of COVID-19 on the Stock Market and Potential Investing Strategies. Available at SSRN 3563380.
