Corresponding author: Hamza Bouhali ( hamzaelbouhali@gmail.com ) © 2021 Non-profit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Bouhali H, Dahbani A, Dinar B (2021) COVID-19 impacts on financial markets: Takeaways from the third wave. Russian Journal of Economics 7(3): 200-212. https://doi.org/10.32609/j.ruje.7.65328
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This study provides an updated analysis of the impact of COVID-19 daily contaminations and vaccinations on the financial markets by incorporating the third wave observed in 2021. Our methodology is based on a comparative approach using a multivariate heteroscedasticity model and data from the Eurozone and ten other countries from different economies. Our results show that COVID-19 contaminations and vaccinations strongly affected most of the countries in our sample (except for the UK, Russia and India in the case of COVID-19 contaminations). We also found that optimistic market sentiment concerning the evolution of the pandemic prevailed among the countries forming our sample (except for Switzerland, Russia and India).
COVID-19, exchange rates, vaccine, financial market.
Following the initial outbreak in Wuhan in December 2019, the spectacular scale and spread of the deadly coronavirus forced authorities worldwide to implement rounds of economically damaging lockdowns and to put global supply chains in a standstill mode. The high socio-economic costs associated with COVID-19 contaminations triggered extreme risk aversion globally, causing a sharp liquidity squeeze across the financial markets and massively dumping many asset class valuations. If the panic reaction of the global financial markets was primarily expected due to the high uncertainty surrounding this economic shock, the extent of the collapse, however, was unparalleled, as illustrated by the meltdown of the major stock markets’ indices worldwide.
The bond, credit, and commodities markets’ behaviors reflected the general pessimistic mood, with the government bonds’ long term yields plunging to historic record lows due to the prolonged depression and significant investors’ concerns despite the disproportionate scale of public debt monetization and exponential growth of major central banks’ balance sheets (
Unsurprisingly, emerging and frontier financial markets suffered more disruptions in comparison with the developed markets owing to the gap of market maturity and their high exposition to the sectors which were hard hit by the pandemic (
Against this background, many researchers started analyzing the financial market’s behavior shift during the pandemic. The first line of research adopted a comparative approach using econometric modeling to exhibit the change in the financial market’s behavior after the breakout of the COVID-19 pandemic by splitting their sample into pre-pandemic and post-pandemic periods. We cite various articles such as
Although research on the impact of the COVID-19 pandemic on financial assets’ behavior is exhaustive, most of the studies reported in the previous paragraph focused on the daily contaminations in a single case country or on an economic coalition. Our study aims to provide an updated analysis of the impact of COVID-19 daily contaminations and vaccinations on the financial markets by incorporating the third wave observed in 2021. We will adopt a synthetic approach inspired by the work of Hai
The significant findings of our study are that COVID-19 contaminations strongly impacted the majority of studied countries from developed and emerging markets (except the UK and South Korea), with an even more substantial impact in the cases of Russia and India. We also found that COVID-19 vaccinations strongly impacted all the studied countries, especially those composing the Euro bloc. Finally, our results showed that most of the studied countries had an optimistic market sentiment concerning the evolution of the pandemic and its impact on the financial market except for Switzerland, Russia and India.
The article will be organized as follows: in section 2, we present the sample data, we then expose the methods used in our study in section 3. We give the empirical findings of this research in section 4 and the concluding remarks in section 5.
The staggering health and socio-economic tolls of COVID-19 hit every corner of the globe, but the impact varied across countries depending on their specific economic and financial structures. Therefore, setting up an appropriate sample of nations was one of the main challenges for our study. We selected ten countries representing significant types of world economies. We also incorporated the Eurozone as a whole bloc to ensure a more coherent analysis of the euro exchange rate behavior. Furthermore, we converted all the daily closing exchange rates to be expressed in indirect quotations against the U.S. dollar.
To properly investigate the United States case, we will use the CBOE (Chicago Board of Trade) Volatility Index (VIX), commonly known on the financial markets as the fear index. We will also explore the dynamic correlation between the gold price against the U.S. dollar, considered a safe haven by investors, and the new daily contaminations and vaccinations worldwide. The selected countries, the chosen currency pairs, and the data sample for each country are listed in Tables
Countries, currency pairs, and selected time frame for the study of new contaminations’ impact on forex market volatility.
Country | F.X. pair | Time frame (based on the first declared case) |
Developed markets | ||
United States | VIX | 01/21/2020 – 06/30/2021 |
Eurozone | USD/EUR | 01/25/2020 – 06/30/2021 |
Switzerland | USD/CHF | 02/26/2020 – 06/30/2021 |
United Kingdom | USD/GBP | 02/01/2020 – 06/30/2021 |
South Korea | USD/KRW | 01/20/2020 – 06/30/2021 |
Japan | USD/JPY | 01/15/2020 – 06/30/2021 |
Emerging markets | ||
Russia | USD/RUB | 02/01/2020 – 06/30/2021 |
South Africa | USD/ZAR | 03/06/2020 – 06/30/2021 |
India | USD/INR | 01/30/2020 – 06/30/2021 |
China | USD/CNY | 12/31/2019 – 06/30/2021 |
Turkey | USD/TRY | 03/12/2020 – 06/30/2021 |
Worldwide | GOLD price against USD | 12/31/2019 – 06/30/2021 |
Countries, currency pairs, and selected time frame for the study of the impact of vaccinations on forex market volatility.
Country | F.X. pair | Time frame (based on the first vaccine shot given) |
Developed markets | ||
United States | VIX | 12/21/2020 – 06/30/2021 |
Eurozone | USD/EUR | 12/21/2020 – 06/30/2021 |
Switzerland | USD/CHF | 12/23/2020 – 06/30/2021 |
United Kingdom | USD/GBP | 12/14/2020 – 06/30/2021 |
South Korea | USD/KRW | 03/01/2021 – 06/30/2021 |
Japan | USD/JPY | 02/18/2021 – 06/30/2021 |
Emerging markets | ||
Russia | USD/RUB | 12/16/2020 – 06/30/2021 |
South Africa | USD/ZAR | 12/17/2020 – 06/30/2021 |
India | USD/INR | 01/19/2021 – 06/30/2021 |
China | USD/CNY | 12/16/2020 – 06/30/2021 |
Turkey | USD/TRY | 01/14/2021 – 06/30/2021 |
Worldwide | GOLD price against USD | 12/14/2020 – 06/30/2021 |
Daily logarithmic closing rates against the U.S. dollars for the studied series.
Source: Authors’ calculations.
Daily logarithmic new COVID-19 contamination for the studied countries.
Source: Authors’ calculations.
Daily logarithmic number of COVID-19 vaccinations for the studied countries.
Source: Authors’ calculations.
The closing exchange rates were obtained from the central banks’ websites for the different countries, the daily data of the VIX index from the CBOE website, the daily COVID-19 new contaminations, and the vaccination data from the World Health Organisation (WHO) website.
To use our data in heteroscedasticity models, we need to ensure stationarity. The Dickey–Fuller test results show that all variables are stationary at the first difference with a 5% significance. Therefore, for heteroscedasticity modeling, we will use the first difference in our data (Table
Results of the augmented Dickey–Fuller test for the studied times series.
Country | Currency pair against USD | COVID–19 contaminations | COVID–19 vaccinations | ||||||
Level data | 1st diff data | Level data | 1st diff data | Level data | 1st diff data | ||||
Worldwide | Value | –0.8116 | –28.5107 | –1.5168 | –6.9958 | 1.2448 | –8.9526 | ||
Prob | 0.8149 | 0.0000 | 0.5242 | 0.0000 | 0.9983 | 0.0000 | |||
Developed markets | |||||||||
United States | Value | –3.6663 | –15.944 | –1.4665 | –7.6304 | –4.3666 | –10.4533 | ||
Prob | 0.2248 | 0.0000 | 0.5497 | 0.0000 | 0.8905 | 0.0000 | |||
Europe | Value | –1.7377 | –28.6164 | –1.4264 | –7.9515 | –0.2847 | –8.1150 | ||
Prob | 0.4118 | 0.0000 | 0.5697 | 0.0000 | 0.9229 | 0.0000 | |||
Switzerland | Value | –1.4225 | –28.9692 | –3.3241 | –4.0082 | –1.3619 | –10.2707 | ||
Prob | 0.5723 | 0.0000 | 0.2145 | 0.0015 | 0.5990 | 0.0000 | |||
United Kingdom | Value | –1.9346 | –27.6436 | –2.0831 | –3.8617 | –2.2162 | –8.4313 | ||
Prob | 0.3164 | 0.0000 | 0.3287 | 0.0026 | 0.2016 | 0.0000 | |||
South Korea | Value | –2.2181 | –32.3367 | –2.1500 | –7.2774 | –2.2223 | –5.0414 | ||
Prob | 0.2000 | 0.0000 | 0.2253 | 0.0000 | 0.1994 | 0.0000 | |||
Japan | Value | –2.9235 | –31.6859 | –2.7193 | –7.3499 | 2.5983 | –4.2579 | ||
Prob | 0.6431 | 0.0000 | 0.1717 | 0.0000 | 0.9890 | 0.0000 | |||
Emerging markets | |||||||||
Russia | Value | –1.9112 | –31.7067 | –2.1272 | –3.7562 | –0.2040 | –11.2322 | ||
Prob | 0.3272 | 0.0000 | 0.2342 | 0.0021 | 0.9340 | 0.0000 | |||
South Africa | Value | –1.7701 | –29.9507 | –2.6843 | –3.9718 | 2.3189 | –5.0150 | ||
Prob | 0.3956 | 0.0000 | 0.1777 | 0.0018 | 0.9900 | 0.0000 | |||
India | Value | –2.3911 | –30.7487 | –1.4610 | –7.3837 | –0.2466 | –8.5771 | ||
Prob | 0.2445 | 0.0000 | 0.5524 | 0.0000 | 0.9283 | 0.0000 | |||
China | Value | –0.8739 | –32.5299 | –1.1542 | –10.9680 | 0.8159 | –7.6706 | ||
Prob | 0.7965 | 0.0000 | 0.3254 | 0.0000 | 0.9940 | 0.0000 | |||
Turkey | Value | –0.7584 | –15.0347 | –2.3359 | –7.4425 | –1.5976 | –7.0528 | ||
Prob | 0.8295 | 0.0000 | 0.1612 | 0.0000 | 0.4810 | 0.0000 |
To assess the dynamic correlations between the forex exchange market and COVID-19 daily contaminations and vaccinations in the studied countries, we will use the DCC-GARCH model (
In our study, we will use the DCC-GARCH (1,1) model commonly used in the economic literature and that we write as follows:
Ht = , (1)
where
h 11,t = α01 + α11 ϵ21, t –1 + β1,1 h11, t –1 (2)
h 22,t = α02 + α21 ϵ22, t –1 + β2,1 h22, t –1. (3)
To efficiently analyze the results of the model, we will consider that depending on the value of the conditional correlation, the impacts of the daily COVID-19 contaminations and vaccinations are weak if 0 ≤ βdcc < 0.5, moderate if 0.5 ≤ βdcc < 0.7, strong if 0.7 ≤ βdcc < 0.9 and finally very strong if 0.9 ≤ βdcc. The chronological evolution of dynamic correlations between the studied variables is plotted in Figs
Graph of conditional correlations between daily COVID-19 contaminations and exchange rates of the selected country sample.
Source: Authors’ calculations.
Graph of conditional correlations between daily COVID-19 vaccinations and exchange rates of the selected country sample.
Source: Authors’ calculations.
The DCC GARCH model results in Table
Results of DCC-GARCH Model between new COVID-19 Contaminations, daily COVID-19 vaccinations and exchange rates for the selected countries.
Country | Parity | Market sentiment (MS), % | Conditional correlations between daily new COVID-19 contaminations and F.X. exchange rates (FX-DC) | Conditional correlations between daily COVID-19 vaccinations and F.X. exchange rates (FX-DV) | ||||
βdcc | Log-likelihood | βdcc | Log-likelihood | |||||
Worldwide | Gold price | –1.81 | Value | 0.8297 | 2583.69 | 0.8147 | 1588.69 | |
Prob | 0.0001 | 0.0168 | ||||||
Developed Markets | ||||||||
United States | VIX Index | 6.35 | Value | 0.8280 | 2847.66 | 0.8806 | 1346.21 | |
Prob | 0.0000 | 0.0133 | ||||||
Europe | USD/EUR | 13.11 | Value | 0.8266 | 1816.5 | 0.9350 | 1122.11 | |
Prob | 0.0000 | 0.0000 | ||||||
Switzerland | USD/CHF | –5.64 | Value | 0.8523 | 454.15 | 0.8042 | 647.265 | |
Prob | 0.0600 | 0.0722 | ||||||
United Kingdom | USD/GBP | 141.39 | Value | 0.3624 | 1345.48 | 0.8748 | 1038.27 | |
Prob | 0.0038 | 0.2128 | ||||||
South Korea | USD/KRW | 45.97 | Value | 0.6006 | 587.82 | 0.8767 | 626.187 | |
Prob | 0.0005 | 0.0000 | ||||||
Japan | USD/JPY | 8.90 | Value | 0.7625 | 482.04 | 0.8304 | 639.129 | |
Prob | 0.0879 | 0.0000 | ||||||
Emerging Markets | ||||||||
Russia | USD/RUB | –12.49 | Value | 0.9489 | 1263.63 | 0.8304 | 1084.56 | |
Prob | 0.0000 | 0.0000 | ||||||
South Africa | USD/ZAR | 13.52 | Value | 0.7360 | 1164.25 | 0.8355 | 692.871 | |
Prob | 0.0000 | 0.0894 | ||||||
India | USD/INR | –5.79 | Value | 0.9134 | 1555.07 | 0.8605 | 1075.56 | |
Prob | 0.0000 | 0.0023 | ||||||
China | USD/CNY | 1.19 | Value | 0.8303 | 517.42 | 0.8402 | 1293.17 | |
Prob | 0.0001 | 0.0829 | ||||||
Turkey | USD/TRY | 5.05 | Value | 0.8122 | 1258.27 | 0.8532 | 1131.84 | |
Prob | 0.0101 | 0.0221 |
We should also note the moderate impact of daily COVID-19 contaminations on forex market volatility in South Korea. This result could be driven by the successful monitoring conducted by Korean authorities since the first wave of COVID-19 contaminations. They have deployed considerable efforts, such as introducing mobile technology to track and trace the infected population, and the systematic testing of individuals further to various containment measures implemented earlier. Other factors may have also contributed to this result, as listed by
Another finding is the strong impact of the daily COVID-19 contaminations on forex exchange market volatility for most countries listed in the study regardless of their degree of development, namely the United States, Europe, Switzerland, Japan, South Africa, China and Turkey. All those countries (including those forming the Europe proxy) have open economies with heavy reliance on global supply chains. Therefore, as the pandemic evolved and authorities took strict sanitary measures (lockdowns, travel bans, capacity reduction), their domestic forex market became more responsive to COVID-19 data, mainly the daily COVID-19 contaminations. Furthermore, investors seeking safe haven assets triggered significant pressure on gold, U.S. dollars and the Swiss franc, especially when COVID-19 contaminations soared. These results corroborate those of
We also observe a powerful impact of Daily COVID-19 contaminations in the cases of Russia and India, as shown in Table
Regarding the impact of COVID-19 vaccinations on the forex market, the results listed in Table
The next step in our study is to assess market sentiment in the studied countries. To do so, we will create a metric that we name “Market sentiment” (MS). This variable will determine what has a greater impact on the domestic forex market, COVID-19 contaminations or vaccinations data. We will define a pessimistic market sentiment when the daily COVID-19 contaminations impact the market more, exhibiting the prevailing fear factor among market actors in this country. On the other hand, we will define an optimistic market sentiment when the daily COVID-19 vaccinations impact the market more, which shows the market actors in this country are looking forward to the economy’s recovery and that vaccination is the primary tool to get back to some “normalcy.” We can synthesize the variable definition as follows:
(6)
The results listed in Table
For the emerging markets, we observe mixed results, with South Africa, China and Turkey exhibiting an optimistic market sentiment while India and Russia exhibit a notable pessimistic mood. This contrast can be attributed to the disproportionate number of contaminations in both countries compared to the fully vaccinated population. As of June 30, 2021, India already had 30.4 million contaminations and only 57.7 million fully vaccinated individuals, representing only 4.2% of the total population. Russia had 5.4 million contaminations, and 17.3 million fully vaccinated individuals, representing 12% of the population. On the same date, the U.S. had 48% of its citizens fully vaccinated, the UK had 49%, and Turkey had 18%.
This article provided an updated analysis of the impact of COVID-19 daily contaminations and vaccinations on the financial markets by incorporating the third wave observed in 2021. We adopted a synthetic approach based on many articles in the literature with remarkable differences regarding the sample size and methodology.
Our significant findings are that COVID-19 contaminations continued to affect economies strongly on different levels (except for the UK and South Korea), with an even more substantial impact in the cases of Russia and India. We also found that COVID-19 vaccinations greatly influenced all the studied countries, especially those forming the Euro bloc. Finally, our results showed that an optimistic market sentiment prevailed among our sample concerning the evolution of the pandemic and its impact on the financial market, except for Switzerland, Russia and India.
This study is one of the first to incorporate data from the heavy third wave characterized by the delta variant, making our results significant for market actors and policymakers worldwide. Further studies could focus on the micro-level to shed light on the atypical financial market behavior exhibited in our research in the cases of the UK, Russia and India.