Diffusion of COVID-19 impact across selected stock markets: a wavelet coherency analysis
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Author(s)Taufeeque Ahmad SiddiquiLink to ORCID Index: https://orcid.org/0000-0002-3149-2234
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Haseen AhmedLink to ORCID Index: https://orcid.org/0000-0002-8721-6132
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Mohammad NaushadLink to ORCID Index: https://orcid.org/0000-0003-4421-3422
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DOIhttp://dx.doi.org/10.21511/imfi.17(4).2020.19
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Article InfoVolume 17 2020, Issue #4, pp. 202-214
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- 1067 Views
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292 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
COVID-19 has impacted the world economy in an unprecedented manner; the financial markets indicate the same. This spontaneous event landed most of the stock markets into extreme volatility. Large capital outflow and extreme rapid fall were seen among almost all the world financial markets. Though similar trend prevailed everywhere during this pandemic, the impact could not be accumulated in absolute terms. Using the data of five stock markets, the current study endeavored to draw an impact of COVID-19 on major stock exchanges. The study uses wavelet coherency analysis on one-year daily data from June 2019 to May 2020 of five stock markets: Bombay Stock Exchange (BSE), London Stock Exchange (LSE), NASDAQ, Tokyo Stock Exchange (Nikkei), and Shanghai Stock Exchange. It is observed that there are time-variation and scale-variation in co-movements between the studied markets. During the crisis, the co-movement concentrates on a short time scale, even for two days. These results have significant implications for international investors, which will help them in portfolio diversification with time elements. All the stock markets under study have indicated co-movement at different time scales and frequencies with varying cross-power levels. However, the concentration of co-movement is found the most between the UK and the US stock markets. It is the least between Japan and the UK. In BSE, co-movement at shorter time scales started late. NASDAQ is leading only in one case, i.e., Shanghai Stock Exchange. BSE is not leading any stock index. LSE is in the leading position in all four cases. It has also been observed that co-movement started to concentrate at a shorter time scale as soon as the impact of the crisis increased.
- Keywords
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JEL Classification (Paper profile tab)E22, F32, G24, G32, O16
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References48
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Tables0
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Figures10
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- Figure 1. London Stock Exchange and Tokyo Stock Exchange (Nikkei)
- Figure 2. London Stock Exchange and NASDAQ
- Figure 3. Shanghai Stock Exchange and BSE
- Figure 4. Shanghai Stock Exchange and NASDAQ
- Figure 5. NASDAQ and BSE
- Figure 6. London Stock Exchange and BSE
- Figure 7. Shanghai Stock Exchange and Tokyo Stock Exchange (Nikkei)
- Figure 8. Shanghai Stock Exchange and London Stock Exchange
- Figure 9. NASDAQ and Tokyo Stock Exchange (Nikkei)
- Figure 10. Tokyo Stock Exchange (Nikkei) and BSE
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- Albuquerque, R., & Vega, C. (2009). Economic News and International Stock Market Co-movement. Review of Finance, 13(3), 401-465.
- Aloui, C., & Hkiri, B. (2014). Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis. Economic Modelling, 36, 421-431.
- Baig, T., & Goldfajn, I. (1998). Financial Market Contagion in the Asian Crisis (IMF working paper, 98-155).
- Barunik, J., Vacha, L., & Kristoufek, L. (2011). Comovement of Central European Stock Markets using wavelet coherence: Evidence from high frequency data (IES Working Paper).
- Brooks, R., & Del Negro, M. (2004). The rise in co-movement across national stock markets: market integration or IT bubble? Journal of Empirical Finance, 11(5), 659-680.
- Brooks, R., & Del Negro, M. (2005). Country versus region effects in international stock returns. Journal of Portfolio Management, 31(4), 67-72.
- Dalkir, M. (2004). A new approach to causality in the frequency domain. Economics Bulletin, 3(44), 1-14.
- Dalkir, M. (2009). Revisiting Stock Market Index Correlations. Finance Research Letters, 6(1), 23-33.
- Das, D., Kannadhasan, M., Tiwari, A. K., & Al-Yahyaee, K. H. (2018). Has co-movement dynamics in emerging stock markets changed after global financial crisis? New evidence from wavelet analysis. Applied Economics Letters, 25(20), 1447-1453.
- Dooley, M., & Hutchinson, M. (2009). Transmission of the US subprime crisis to emerging markets: evidence on the decoupling–recoupling hypothesis. Journal of International Money and Finance, 28, 1331-1349.
- Felmingham, D., & Grüneberg, R. N. (2000). The Alexander Project 1996–1997: latest susceptibility data from this international study of bacterial pathogens from community-acquired lower respiratory tract infections. Journal of Antimicrobial Chemotherapy, 45(2), 191-203.
- Forbes, K., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market co-movements. Journal of Finance, 57, 2223-2261.
- Gencay, R., Selcuk, F., & Whitcher, B. (2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, San Diego, CA.
- Graham, M., Kiviaho, J., & Nikkinen, J. (2012). Integration of 22 emerging stock markets: a three- dimensional analysis. Global Finance Journal, 23, 34-47.
- Graham, M., Kiviaho, J., & Nikkinen, J. (2013). Global and regional co-movement of the MENA stock markets. Journal of Economics and Business, 65, 86-100.
- Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11, 561-566.
- Hahn, L. S. (2004). International transmission of stock market movements: a wavelet analysis. Applied Economics Letters, 11(3), 197-201.
- Huang, S., An, H., Gao, X., & Huang, X. (2016). Time-frequency featured co-movement between the stock and prices of crude oil and gold. Phys. Stat. Mech. Appl. 444, 985-995.
- Hudgins, L., Friehe, A. C., & Mayer, E. M. (1993). Wavelet Transform Atmospheric Turbulence. Physical Review Letters, 71(20), 3279.
- Hwang, I., In, F., & Kim, T. S. (2011). Contagion and Spillover effects of US Subprime crisis: Evidence from international stock markets. Financial Contagion: The viral threat to wealth of the nations. John Wiley and Sons.
- Jang, H., & Sul, W. (2002). The Asian financial crisis and the co-movement of Asian stock markets. Journal of Asian Economics, 13(1), 94-104.
- Johnson, R., & Soenen, L. (2003). Economic integration and stock market co-movement in Americas. Journal of Multinational Financial Management, 13(1), 85-100.
- Khan, S., & Park, K. (2009). Contagion in the stock markets: The Asian financial crisis revisited. Journal of Asian Economics, 20(5), 561-569.
- Kiviaho, J., Nikkinen, J., Piljak, V., & Rothovius, T. (2012). The Co-movement Dynamics of European Frontier Stock Markets. European Financial Management, 20(3), 574-595.
- Kizys, R., & Pierdzioch, C. (2009). Changes in the international co-movement of stock returns and asymmetric macroeconomic shocks. Journal of International Financial Markets, Institutions and Money, 19(2), 289-305.
- Lee, B.-S., & Jeon, B. N. (1995). Common Stochastic Trends and Predictability of International Stock Prices. Journal of the Japanese and International Economies, 9(3), 245-277.
- Lee, S. H. (2004). International transmission of stock markets movements: a wavelet analysis. Applied Economic Letters, 11, 197-201.
- Leong, S., & Felmingham, B. (2003). The interdependence of share markets in the developed economies of East Asia. Pacific-Basin Finance Journal, 11, 219-237.
- Loh, L. (2013). Co-movement of Asia-Pacific with European and US stock market returns: A cross-time-frequency analysis. Research in International Business and Finance, 29, 1-13.
- Morana, C., & Beltratti, A. (2008). Co-movements in international stock markets. Journal of International Financial Markets, Institutions and Money, 18(1), 31-45.
- Naushad, M. (2020). Investigating the technical and scale efficiency of cement companies in Saudi Arabia. Management Science Letters, 11(2), 339-346.
- Naushad, M., & Malik, S. A. (2015). Corporate governance and bank performance: A study of selected banks in GCC region. Asian Social Science, 11(9), 226.
- Nikkinen, J., Piljak, V., & Äijö, J. (2012). Baltic stock markets and the financial crisis of 2008–2009. Research in International Business and Finance, 26(3), 398-409.
- Nikkinen, J., Pynnönen, S., Ranta, M., & Vähämaa, S. (2010). Cross-dynamics of exchange rate expectations: a wavelet analysis. International Journal of Finance & Economics, 167(3), 205-221.
- Park, Y. C., & Song, C. Y. (2001). Financial Contagion in the East Asian Crisis: With Special Reference to the Republic of Korea. International Financial Contagion. Springer, Boston, MA.
- Park, Y. C., & Song, C. Y. (1999). The East Asian Financial Crisis: A Year Later. IDS Bulletin, 30(1), 93-107.
- Percival, D. B., & Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press.
- Power, G. J., & Turvey, C. G. (2010). Long-range dependence in the volatility of commodity Futures prices: wavelet-based evidence. Phys. Stat. Mech. Appl., 389(1), 79-90.
- Pretorius, E. (2002). Economic determinants of emerging stock market interdependence. Emerging Markets Review, 3(1), 84-105.
- Ramsey, J., & Lampart, C. (1998a). Decomposition of economic relationships by time scale using wavelets. Macro-economic Dynamics, 2(1), 49-71.
- Ramsey, J., & Lampart, C. (1998b). The decomposition of economic relationships by time scale using wavelets: Expenditure and income. Studies in Nonlinear Dynamics and Econometrics, 3(1), 23-42.
- Ramsey, J., & Zhang, Z. (1997). The analysis of foreign exchange data using waveform dictionaries. Journal of Empirical Finance, 4, 341-372.
- Ramsey, J. B. (2002). Wavelets in economics and finance: past and future. Stud. Non-Linear Dynam. Econom, 6(3), 1-29.
- Rua, A., & Nunes, L. C. (2009). International co-movement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16(4), 632-639.
- Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO-monsoon system. Journal of Climate, 12(8), 2679-2690.
- Torrence, C., & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61-78.
- United Nations. (2020). UN/DESA Policy Brief #59: Corona crisis causes turmoil in financial markets.
- Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revised: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241-247.
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The impact of social distancing policy on small and medium-sized enterprises (SMEs) in Indonesia
Muhtar Lutfi ,
Pricylia Chintya Dewi Buntuang ,
Yoberth Kornelius ,
Erdiyansyah ,
Bakri Hasanuddin
doi: http://dx.doi.org/10.21511/ppm.18(3).2020.40
Problems and Perspectives in Management Volume 18, 2020 Issue #3 pp. 492-503 Views: 3787 Downloads: 1360 TO CITE АНОТАЦІЯThis study aims to investigate the impact of social distancing policies on SMEs in Indonesia. It used a quantitative method with a survey design. Respondents were all SMEs in Indonesia that are affected by social distancing policies during the COVID-19 pandemic. It involved a total of 587 SME samples selected randomly. The data were collected through observations, questionnaires, and literature studies. The collected data were analyzed using descriptive statistics with SPSS software to determine the mean value. The result showed that social distancing policies affect SMEs during the COVID-19 pandemic. This is indicated by the decreasing income and demand for SMEs products, and even some have no income (mean values of 2.40) due to the social distancing policies. Besides, the policy’s impact is also shown in the increasing cost of raw materials and production costs due to supply chain problems (mean values of 4.79). The policy’s impact raises anxiety for SMEs to survive so that business actors change their plans by utilizing information technology (mean values of 4.81). This change is a strategy to survive due to the impact of the applied policies. Although social distancing policies affect SMEs’ survival during the pandemic, research findings show that SMEs in Indonesia did not terminate employment (mean values of 4.37) due to the presence of economic stimulus policies that helped SMEs survive and grow during the COVID-19 pandemic.
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The impact of the COVID-19 pandemic on retailer performance: empirical evidence from India
Amgad S.D. Khaled ,
Nabil Mohamed Alabsy ,
Eissa A. Al-Homaidi 
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Abdulmalek M.M. Saeed
doi: http://dx.doi.org/10.21511/im.16(4).2020.11
Innovative Marketing Volume 16, 2020 Issue #4 pp. 129-138 Views: 3462 Downloads: 5876 TO CITE АНОТАЦІЯThe study aims to synthesize the challenges that retailers are facing during the COVID-19 emergency. The research is definitive, informative, and based on a single design of cross-sectional research. Quantitative data based on the research instrument were produced (a questionnaire). Five hundred responses were collected from employees of major retail stores in India. Retailer performance is considered a dependent variable, whereas employee well-being, customer and brand protection, use of technology, government policies, and supply chain are used as independent variables. The current study results indicated that employee well-being and government policies have a significant positive impact on retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailers’ performance. This study will help retailers develop strategies for their employees to protect them and understand that technology is needed in the new normal times. This study highlights the need to be flexible in executing strategic strategies, but retailers need to develop comprehensive action plans, including selecting managers of initiative and defining goals and deadlines. Provided that retailers’ current reality is different from the old normal, no time is lost in taking audacious action.
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Selection of the right proxy market portfolio for CAPM
Rashmi Chaudhary ,
Priti Bakhshi
doi: http://dx.doi.org/10.21511/imfi.18(3).2021.02
Investment Management and Financial Innovations Volume 18, 2021 Issue #3 pp. 16-26 Views: 3142 Downloads: 1550 TO CITE АНОТАЦІЯThe purpose of the paper is to select the right market proxy for calculating the expected return, since critically evaluating proxies or selecting the correct proxy market portfolio is essential for portfolio management because the change in the market portfolio proxy affects returns. In this study, monthly data of equity indices are evaluated to find out the better market proxy. The indices taken are BSE 30 (Sensex), Nifty 50, BSE 100, BSE 200, and BSE 500. The macroeconomic variables used in the study are industrial production index (IIP), consumer price index (CPI), money supply (M1), and exchange rate in India. To avoid the influence of COVID-19, the research period was from January 2013 to December 2019 to critically evaluate these proxies in order to find the most appropriate market proxy. This paper reveals a noteworthy relationship between stock market returns and macroeconomic factors, while suggesting that the BSE 500 is a better choice for all equity indices, as the index also shows a significant relationship with all macroeconomic variables. BSE500 is a composite index comprising all sectors with low, mid and large cap securities, therefore it reflects the impact of macroeconomic factors most efficiently, taking it as a market proxy. This study was carried out in the context of India and can be replicated for other countries.
