COVID 19 Duyurularının ve Devlet Kısıtlamalarının Ülke Borsalarına Etkisi: Gelişmiş ve Gelişmekte Olan Piyasalar

Abstract

Bu çalışma, özellikle COVID-19 salgını sürecinde ülkelerin vaka ve ölüm sayısı duyurularının yatırımcıda oluşturduğu panik ve risk algısının borsalara nasıl yansıdığını araştırmaktadır. Ayrıca salgındaki bulaşma hızının düşürülmesi için ülkeler tarafından alınan tedbirlerin borsalar da oluşturduğu etki araştırılmıştır. Bu amaçla ülke borsaları MSCI endeksindeki gelişmiş ve gelişmekte olan 10’ar ülke belirlenmiştir. COVID-19 duyurusu olarak ölüm ve vaka sayıları kullanılırken, devlet kısıtlamaları için ise Oxford COVID-19 Devlet Müdahale İzleyicisi tarafından hesaplanan sıkılık endeksi kullanılmıştır. Veri setleri Refinitiv Datastream ve ourworldin data veri tabanlarından elde edilmiştir. Yapılan analiz sonuçlarında hem gelişmiş hem de gelişmekte olan ülkeler için kullanılan değişkenler arasında %1’de anlamlı uzun dönem bir ilişki tespit edilmiştir. Ayrıca hükümet kısıtlamalarından hem gelişmiş hem de gelişmekte olan ülke borsalarına doğru nedensellik ilişkisi tespit edilirken, vaka sayılarından bir ilişki tespit edilememiştir. Son olarak ise yapılan panel veri modeli analizi sonucunda gelişmekte olan ülke borsalarında sadece hükümet kısıtlamalarının negatif ve anlamlı etkisi tespit edilirken, gelişmiş ülkelerde hem hükümet kısıtlamalarının hem de COVID-19 duyurularının negatif ve anlamlı etkisi tespit edilmiştir.

Keywords

• COVID-19
• Hükümet kısıtlamaları
• Eşbütünleşme
• Nedensellik
• Panel veri analizi

Impact of COVID-19 Announcements and Government Restrictions on Country Stock Exchanges: Developed and Emerging Markets

Abstract

This study investigates how the panic and risk perception created by countries' announcements of the number of cases and deaths, especially during the COVID-19 epidemic, are reflected in the stock markets. In addition, the impact of the measures taken by countries to reduce the rate of infection in the epidemic on the stock markets was investigated. For this purpose, 10 developed and developing countries in the MSCI index of country stock markets were determined. While death and case numbers were used as COVID-19 announcements, the stringency index calculated by the Oxford COVID-19 Government Response Tracker was used for government restrictions. Data sets were obtained from Refinitiv Datastream and ourworldindata databases. As a result of the analysis, a significant long-term relationship of 1% was detected between the variables used for both developed and developing countries. In addition, while a causal relationship was detected from government restrictions to stock markets in both developed and developing countries, no relationship could be determined from the number of cases. Finally, as a result of the panel data model analysis, only the negative and significant effect of government restrictions was detected on the stock markets of developing countries, while the negative and significant effect of both government restrictions and COVID-19 announcements was detected in developed countries.

Keywords

• COVID-19
• Government restrictions
• Cointegration
• Causality
• Panel data analysis

References

1. Aggarwal, S., Nawn, S. and Dugar, A. (2021). What caused global stock market meltdown during the Covid pandemic–Lockdown stringency or investor panic?, Finance Research Letters 38, 101827.
2. Ashraf, B.N. (2020). Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets, Journal of Behavioral and Experimental Finance, 27, 100371.
3. Bakry, W., Kavalmthara, P.J., Saverimuttu, V., Liu, Y. and Cyril, S. (2022). Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets, Finance Research Letters, 46, 102350.
4. Baltagi, B. H., (2008). Econometric analysis of panel data. Vol:4. Chichester: Wiley.
5. Barbieri, L. (2008). Panel cointegration tests: a survey. Rivista internazionale di scienze sociali, 3-36.
6. Bouri, E., Naeem, M. A., Mohd Nor, S., Mbarki, I., & Saeed, T. (2022). Government responses to COVID-19 and industry stock returns. Economic Research-Ekonomska Istraživanja, 35(1), 1967-1990.
7. Caporale, G. M., Kang, W. Y., Spagnolo, F., & Spagnolo, N. (2022). The COVID-19 pandemic, policy responses and stock markets in the G20. International Economics, 172, 77-90.
8. Chang, C.P., Feng, G.F. and Zheng, M. (2021) Government fighting pandemic, stock market return, and COVID-19 virus outbreak, Emerging Markets Finance and Trade, 57(8), 2389-2406, DOI: 10.1080/1540496X.2021.1873129.

9. Chen, D., Hu, H., & Chang, C. P. (2021). The COVID-19 shocks on the stock markets of oil exploration and production enterprises. Energy Strategy Reviews, 38, 100696.
10. Choi, I. (2001). Unit root tests for panel data. Journal of international money and Finance, 20(2), 249-272.
11. Das, P., (2019). Panel data analysis: Static models. Econometrics in Theory and Practice: Analysis of Cross Section, Time Series and Panel Data with Stata 15.1, 457-497.
12. Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic modelling, 29(4), 1450-1460.
13. Fontana, R. (2021). Impact of Covid-19 Announcements on Financial Markets, Research Paper Series, İason Essential Services for Financial Institutions.
14. Giofré, M. (2021). Covid-19 stringency measures and foreign investment: An early assessment, North American Journal of Economics and Finance, 58, 101536.
15. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438.
16. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the econometric society, 46(6)1251-1271.
17. Hu, H., Chen, D. and Fu, Q. (2022). Does a Government Response to COVID-19 Hurt the Stock Price of an Energy Enterprise?, Emerging Markets Finance and Trade, 58(1), 1-10, DOI: 10.1080/1540496X.2021.1911803
18. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1), 53-74.
19. Jiang, B., Gu, D., Sadiq, R., Khan, T.M. and Chang, H.L. (2022). Does the stringency of government interventions for COVID19 reduce the negative impact on market growth? Evidence from Pacific and South Asia, Economic Research Ekonomska Istraživanja, 35(1), 2093-2111, DOI: 10.1080/1331677X.2021.1934058
20. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics, 90(1), 1-44.
21. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24.
22. Maddala, G.S. and Wu, S. (1999), A Comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61, 631-652.
23. Marobhe, M.I. and Kansheba, J.M.P. (2022). Stock market reactions to COVID-19 shocks: do financial market interventions walk the talk?, China Finance Review International, 12(4), 623-645.
24. Martins, A. M., & Cró, S. (2022). Stock markets’ reaction to COVID-19, US lockdown and waves: the case of fast food and food delivery industry. Current Issues in Tourism, 25(11), 1702-1710.
25. Narayan, P. K., Phan, D. H. B., & Liu, G. (2021). COVID-19 lockdowns, stimulus packages, travel bans, and stock returns. Finance research letters, 38, 101732.
26. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
27. Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Advances in Econometrics. 15, 93–130.
28. Priya, P. and Sharma, C. (2023). COVID-19 related stringencies and financial market volatility: sectoral evidence from India, Journal of Financial Economic Policy, 15(1), 16-34.
29. Saif-Alyousfi, A.Y.H. (2022). The impact of COVID-19 and the stringency of government policy responses on stock market returns worldwide, Journal of Chinese Economic and Foreign Trade Studies, 15(1), 87-105.
30. Scherf, M., Matschke, X and Rieger, M.O. (2022).Stock market reactions to COVID-19 lockdown: A global analysis, Finance Research Letters, 45, 102245.
31. Wang, Y., Zhang, H., Gao, W. and Yang, C. (2021). Covid-19-related government interventions and travel and leisure stock, Journal of Hospitality and Tourism Management, 49, 189–194.
32. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
33. Yiu, M.S. and Tsang, A. (2021). Impact of COVID-19 on ASEAN5 stock markets, Journal of the Asia Pacific Economy, DOI: 10.1080/13547860.2021.1947550
34. Yu, X and Xiao, K. (2023). COVID-19 Government restriction policy, COVID-19 vaccination and stock markets: Evidence from a global perspective, Finance Research Letters 53, 103669.
35. Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance research letters, 36, 101528.