Global evidence of the COVID-19 shock on real equity prices and real exchange rates: A counterfactual analysis with a threshold-augmented GVAR model

Abstract

In this study, we offer a global perspective on the impacts of the COVID-19 pandemic on financial markets using a multi-country Threshold-Augmented Global Vector Autoregressive Model of Chudik et al. (2020). We document a negative impact of the pandemic on real equity prices across countries (except the United States) and country groupings with the highest negative impact recorded in 2020Q2. The biggest losers are the emerging economies while the biggest gainers are the United States whose real stock prices remain positive and the Euro Area that achieved real exchange rate appreciation when the financial markets were mostly vulnerable. Our results support the effectiveness of the quantitative easing policy regime in the Euro Area during the COVID-19 pandemic and also suggest hedging role for the US stocks among other suggested safe assets.

1. Introduction

This study contributes to the existing knowledge with a multi-country analysis of the impacts of COVID-19 pandemic shock on the global financial markets such as the global equity and foreign exchange markets. The addition to knowledge transcends the existing sectoral, countrywide, or regional analysis where the pandemic has been shown to affect financial assets chiefly by raising financial market risks, volatility, and uncertainty.1 The major conclusion from these studies is that the global financial markets experienced significant dip and that the negative effects of the pandemic vary among markets in developing, emerging and advanced economies. The striking finding of Belaid et al. (2021) indicates that the COVID-19 pandemic increased crisis spillovers between advanced and emerging stock markets with further evidence that the European stock markets are the primary transmitter of shock to other regional stock markets. On the foreign exchange market,2 findings from Wei et al. (2020) suggest increased uncertainty risks in the global forex markets in the aftermath of the pandemic with particular rise in spillover of shocks among the Renminbi and the regional Belt and Road (“B&R”) currencies.

We however differ from the extant literature on the impact analysis of the COVID-19 pandemic in two ways. First, we consider a multi-country study where we highlight the impacts of the pandemic for both real equity prices and real exchange rates from a global perspective covering both the emerging and developed economies including the Euro Area, Advanced Economies, Emerging Economies, and Asia Pacific Economies. This attempt offers a broader perspective that allows for meaningful comparisons of financial markets’ responses to the pandemic across the globe and thus investors, particularly intentional portfolio investors, gain better insights into how vulnerable financial markets have been amidst the pandemic and identify where safe investments can be made during this period.

The second contribution lies in the predictability analysis of the counterfactual outcomes of real stock returns and exchange rate over the 2020Q1–2021Q4 pandemic period in relation to the pre 2019Q4 levels. The counterfactual analysis is relevant for this study as it is considered ideal to explore the impact of the introduction of a policy like the adoption of quantitative easing in the United Kingdom in 2009 (see Pesaran and Smith, 2016) or an emergency event like the COVID-19 pandemic (see Feng and Li, 2021). Also, this study is more amenable to counterfactual analysis rather than the use of techniques involving historical data given the stylized fact in the literature showing that financial market uncertainty rose to levels previously unmatched during past financial crises episodes including the Global Financial Crisis (GFC) (Baker et al., 2020). This study is however an improvement on Feng & Li (2021) who also adopt counterfactual analysis and find that the negative impact of COVID-19 on the Chinese stocks exceeds that of the Severe Acute Respiratory Syndrome (SARS) (another variant of corona virus). We situate the study within the Threshold-Augmented Global Vector Autoregressive (TGVAR) framework of Chudik et al. (2020), which is an improvement on the standard multi-country model of Chudik and Pesaran (2016) and Chudik et al. (2016). The former is specifically developed for COVID-19-related counterfactual analysis and we provide its technical details in the immediate section. In the end, the contribution allows us to assess the effectiveness of policy responses put in place by countries to combat the pandemic. We follow this section with the methodology in Section 2. Section 3 discusses the findings and Section 4 presents the conclusion.

2. Methodology

2.1. Model specification issues

We employ the global vector autoregression model with threshold effects (TGVAR) proposed in Chudik et al. (2020) for the counterfactual analysis of the cross-country impacts of the COVID-19 pandemic on financial markets.3 We begin our specification with the standard GVAR model in Eq. (1):

$$\begin{array}{c}{z}_{it}={\alpha }_{y,i}+{\Phi }_i{z}_{i,t-1}+{\mathrm{B}}_i{z}_{i,t-1}^{*}+{\mathrm{A}}_{0,i}{\mathrm{g}}_t+{\mathrm{A}}_{1,i}{\mathrm{g}}_{t-1}+{\mathrm{u}}_{it};\hfill \\ i=0,1,2,...,n;t=1,2,...,T\hfill \end{array}$$ (1)

where z_it is the vector of domestic variables with dimension k_i across the n countries, that is, z_it = (Δy_it,Δr_it,Δeq_it,Δep_it)′ with the corresponding variables defined as the log of real gross domestic product (y_it), nominal long-term interest rate (r_it), log of real equity prices (eq_it), and log of real exchange rates (ep_it), Δ represents first differences in the variables, while the United States is the reference country and it is indexed as i = 0. Also note that ${\mathrm{z}}_{it}^{*}$ is the vector of weakly exogenous foreign variables constructed as trade-weighted counterparts of the domestic variables: $z_{it}^{*}={\left(\Delta y_{it}^{*},\Delta r_{it}^{*},\Delta e{q}_{it}^{*},\Delta e{p}_{it}^{*}\right)}^{\prime }$; such that $z_{it}^{*}={\sum }_{j=1}^n{w}_{ij}{z}_{it}$, w_ij [w_ii = 0, ${\sum }_{j=0}^n{w}_{ij}=1$] is the weighting matrix obtained from the IMF Direction of Trade flows data; g_t, which are not country-specific, are the global variables that capture observed external factors.4

In order to capture the intent of this study, we augment the model with the unobserved global factors, ${\tilde{z}}_t={\left(\Delta {\tilde{y}}_t,\Delta {\tilde{r}}_t,\Delta e{\tilde{q}}_t,\Delta e{\tilde{p}}_t\right)}^{\prime }$ and threshold effects,h _t − 1(γ_i), which represent structural shifts due to the COVID-19 pandemic:

$$z_{it}={\alpha }_{y,i}+{\Phi }_i{z}_{i,t-1}+{\mathrm{B}}_i{z}_{i,t-1}^{*}+{\mathrm{A}}_{0,i}{\mathrm{f}}_t+{\mathrm{A}}_{1,i}{\mathrm{f}}_{t-1}+{\lambda }_i{h}_{t-1}\left({\gamma }_i\right)+{\mathrm{u}}_{it}$$ (2)

where $\tilde{z}{\prime }_t$ is the unobserved vector of global factors which are obtained by weighting the observables z_it = (Δy_it,Δr_it,Δeq_it,Δep_it)′ with the GDP purchasing power parity weights (${\tilde{w}}_i$) across the countries of the domestic variables [${\tilde{z}}_t={\sum }_{i=0}^n{\tilde{w}}_i{z}_{it}$]; λ_i h _t − 1(γ_i) indicates the threshold effect where h _t − 1(γ_i) = I[(0, 1)′g _t − 1 >γ_i] = I(grv _t − 1 >γ_i); grv_t, the measure of global stock market volatility is the threshold variable.

In the specification of Eq. (2), between 1979Q2 and 2019Q4, the reduced form shock (u_t) can be decomposed as in Eq. (3) while shock between 2020Q1 and 2020Q4 split in Eq. (4) as follows:

$${\mathrm{u}}_t=\Psi {\epsilon }_t+{\eta }_t$$ (3)

$${\mathrm{u}}_{T+q}={\varpi }_{T+q}+\Psi {\epsilon }_{T+q}+{\eta }_{T+q}$$ (4)

where ε_t is the global shock, η_t represents the idiosyncratic shock, ϖ_T + q is the COVID-19 pandemic shock over the period of 2020Q1 to 2020Q4.

2.2. Data

The full description of the data is contained in the Mohaddes and Raissi (2020) paper which presents the GVAR updated database. The global model comprises thirty-three (33) countries nicely connected by trade linkages (see Table 1 for the list of countries). In this study, we focus on the real equity prices and real exchange rate among the four domestic variables in the TGVAR model. The log of real equity price (eq_it) is computed as $LN\left(Equit{y}_{it}/CP{I}_{it}\right)$ where LN denotes natural logarithm and Equity_it is the nominal equity price deflated by consumer price index(CPI_it). The real exchange rate (ep_it) is computed as the multiplication of bilateral nominal exchange rate (EXR_it) and the ratio of foreign consumer prices and domestic consumer prices, that is, $e{p}_{it}=LN\left[EX{R}_{it}*\left(CP{I}_{it}^f/CP{I}_{it}^d\right)\right]$ and the US dollar is the reference currency.5 In whole, the estimation covers the pre-pandemic (1979Q2 to 2019Q4) and the pandemic (2020Q1–2021Q4) periods where 2020Q1 data involves the growth forecast revisions of IMF in order to identify the COVID-19 shock for the counterfactual analysis (see Chudik et al., 2020).

Table 1.

Countries in the GVAR model.

Advanced countries			Emerging countries
Euro Area	Advanced Asia-Pacific	Others	Emerging economies (less China)	Emerging Asia (less China)	Others
Austria	Australia	Canada	Argentina	India	China
Belgium	Japan	Norway	Brazil	Indonesia
Finland	Korea	Sweden	Chile	Malaysia
France	New Zealand	Switzerland	India	Philippines
Germany	Singapore	UK	Indonesia	Thailand
Italy		US	Malaysia
Netherlands			Mexico
Spain			Peru
			Philippines
			South Africa
			Saudi Arabia
			Thailand
			Turkey

3. Results and discussion

As previously noted, we conduct counterfactual analyses in line with the threshold global VAR framework of Chudik et al. (2020) 6 to isolate the impacts of the COVID-19 shock on real equity prices and real exchange rates. Theoretically, we would expect negative effects of the pandemic on the global equity markets given the negative impacts of the pandemic on global output (see Caggiano et al., 2020) and similar negative implication on the global trade (see Feng et al., 2021). For instance, Caggiano et al. (2020) show a 14% cumulative drop in global output due to the pandemic. Similarly, Feng et al. (2021) document an 18.5% decline in global trade for 2020Q2 based on year-on-year comparison with 20219Q2. These disruptions to world output and trade would not only impact stock markets negatively (see Narayan et al., 2020b) but also affect some drivers of real exchange rate disequilibrium (international investment position, exchange rate volatility and capital flows) (see Narayan, 2020; Narayan et al., 2020a). Hence, we find out by how much the real global equity prices and real exchange rates may have been influenced by the pandemic.

We report generalized impulse response functions (GIRFs) for different panels: World, advanced economies (Euro Area & Advanced Asia-Pacific Countries) and emerging economies (Emerging Economies & Emerging Asia less China to remove the effect of outlier) (see Fig. 1a ). We also produce impulse responses for the individual countries (see Table 2 and the appendix). As expected, the pandemic has negative effects on the global equity markets throughout the forecast horizon and the decline in global real equity prices became most severe in the second period (h = 2) equivalent to 2020q2. In the advanced economies, the biggest negative impacts of the pandemic also came in 2020Q2 where the real equity prices of the Euro Area declined by 70% and the Advanced Asia Pacific by 120%. The movers of the COVID-19 pandemic shock among the advanced economies appear to be Norway whose real equity price fell by 178.5%, Austria, Finland, and Spain in the Euro Area with decline of 133.7%, 117.9% and 97.7% respectively. The Asia Pacific are the hardest hit among the advanced economies with Korea & Japan (−189.9% & −101.2% respectively) being the biggest shakers in the region. Comparatively, the emerging stock markets are the worst hit compared with the advanced. The biggest pandemic shock impacts in emerging economies were recorded in Argentina (−175.2%), and Emerging Asia (India, −162.2% and Thailand, −142.3% in 2020Q2).

Fig. 1a.

Counterfactual analysis of the Impact of COVID-19 on real equity prices (percent deviation from baseline).

Table 2.

Impacts of COVID-19 shock in 2020Q2 (h = 2).

Advanced countries			Emerging countries
Countries	eqit	epit	Countries	eqit	epit
Australia	−71.6	30.3	Argentina	−175.2	17.3
Austria	−133.7	−36.8	Brazil	*	−26.3
Belgium	−99.4	−32.5	Chile	−90.4	47.0
Canada	−60.4	26.7	India	−162.2	33.7
Finland	−117.9	−5.9	Indonesia	*	32.2
France	−75.9	−35.8	Malaysia	−86.9	−1.14
Germany	−69.8	−33.4	Mexico	*	70.5
Italy	−64.4	−33.5	Peru	*	23.4
Japan	−101.2	−14.2	Philippines	−66.9	22.9
Korea	−189.9	19.4	Saudi Arabia	*	−4.5
Netherlands	−69.6	−33.7	South Africa	−57.04	11.0
New Zealand	−4.34	30.1	Thailand	−142.3	13.2
Norway	−178.5	−2.36	Turkey	*	4.9
Singapore	−81.8	−1.2
Spain	−97.7	−14.3
Sweden	−52.2	19.2
Switzerland	−19.1	−28.8
UK	−28.6	11.6
US	12.8	*
Panels
Advanced economies	−45	−6.5	Emerging (less China)	−140	20.7
Euro Area	−70	−30.7	Emerging Asia (less China)	−150	29.3
Advanced Asia-Pacific	−120	0.91

Note: “*“ is included to indicate countries with no data for the particular variable in the updated GVAR database. The exception is the US exchange rate which is the reference currency. Note that eq_itand ep_itare real equity prices and real exchange rate, respectively. The former is the nominal equity price deflated by the domestic consumer price, that is, real equity price = nominal equity price/domestic consumer price while the latter is computed by multiplying the nominal exchange rate by the ratio of foreign consumer prices and domestic consumer prices. The foreign consumer price is for the US since the US dollar is the reference currency for the nominal exchange rate.

We attribute the lesser negative impact of the pandemic on advanced economies to the effectiveness of the adoption of both unconventional monetary policy and fiscal policy implemented in these economies relative to their emerging counterparts (see Chudik et al., 2021; Padhan and Prabheesh, 2021). In all, we highlight some hedging role for the US stocks – the only market whose prices remain positive at the height of the pandemic in horizon 2. Some previous studies also indicate currencies like the USD or Japanese Yen (JPY) or other safe assets such as gold to hedge the pandemic risk on the global equity markets (see Tanin et al., 2021; Salisu et al., 2021b).

In Fig. 1b , we quantify the impacts of the pandemic on real exchange rates over the 2020Q1 to 2021Q4 forecast horizon and particularly highlight 2020Q2 in Table 2 when the negative effect of the COVID-19 pandemic reached its highest level (on world output as obtainable in Caggiano et al., 2020, on global trade as shown in Feng et al., 2021, and on equity prices as shown in the present study). Results show that the pandemic that reduced stock prices globally (except the US) in 2020Q2 produced real exchange rate appreciation in the Euro Area and Advanced Economies’ panels. The implication of these results is that tradables cost less in the Euro Area and advanced economies (other than Asia) viz-a-viz the US particularly during the initial response to the pandemic. These results also signify that the countries in the European Union are major drivers of exchange rate dynamics among advanced economies as all of them (Austria, Belgium, Finland, France, Germany, Italy, Netherlands, & Spain) report real exchange rate appreciation against the US dollar as the reference currency (see Column 3 of Table 2). On the other hand, the currencies of advanced & emerging Asian countries and emerging countries in general were shown to be overvalued relative to the dollar during the same period. Hence, from the perspective of most investors, multinational firms, or creditors who consider the US dollar as the international currency for settlements, they may be better-off with the advanced countries’ currencies where the same basket of tradables (goods, stocks or services) can be purchased more cheaply than emerging countries.

Fig. 1b.

Counterfactual analysis of the Impact of COVID-19 on real exchange rates (percent deviation from baseline).

Most monetary authorities responded to COVID-19 pandemic by lowering their policy rate in a bid to reverse the negative economic impacts of the pandemic (Yilmazkuday, 2021). However, the European Central Bank resumed her unconventional monetary policy (Quantitative Easing) in the Euro Area in response to the current pandemic. Quantitative Easing is an unconventional monetary policy useful for injecting excess liquidity into the economy through huge purchase of sovereign debts to reverse economic downturn, ease credit conditions, and exchange rate appreciation (Urbschat and Watzka 2020). Interestingly, the deployment of the unconventional monetary policy appears to pay off given that all the countries in the Euro Area experienced real exchange rate appreciation at the lowest ebb of financial markets in 2020Q2 (see also Chudik et al., 2021; Padhan and Prabheesh, 2021). Nonetheless, the estimated model which does not take cognizance of the unconventional monetary and fiscal policies fully adopted in the advanced economies appears to predict that the stock markets may not fully recover from the pandemic shock whereas, the real exchange rate disequilibrium between the Euro and the US dollar would be corrected to pre-2019Q4 levels (see Figs. 2a & b ).

Fig. 2a.

Dynamics of Real Equity Prices Following the COVID-19 Shock (in logs; 2019Q4=1).

Fig. 2b.

Dynamics of Real Exchange Rates Following the COVID-19 Shock (in logs; 2019Q4=1).

4. Conclusion

This study investigates the impact of the COVID-19 pandemic, occasioned by the spike in global financial market volatility, on the real equity prices and real exchange rates using the TGVAR framework of Chudik et al. (2020). The large country and large regional cases considered comprise the Euro Area, advanced economies, emerging economies excluding China, emerging Asia without China and the Asia-Pacific economies. We produce generalized impulse responses from the TGVAR model to trace the impact of the pandemic and assess the possibility or otherwise for the relevant variables to revert to their pre-2019Q4 (i.e. pre-COVID) equilibria.

We show that the pandemic has widespread negative impacts on the global real equity prices across the different country groupings. A striking discovery from the analysis is that the greatest negative impact of the pandemic is reported on global output, global trade, and global stock markets in 2020Q2. The advanced Asia-Pacific countries are the worst hit among advanced economies with 110% decline in 2020Q2 while the overall losers are the emerging countries with emerging economies (less China) suffering by 140% and emerging Asia (less China) declining by 150%. We attribute the lesser negative impacts of the pandemic on the advanced countries to the deployment of unconventional monetary policy and fiscal policies. Our results specifically highlight the effectiveness of these policies in the Euro Area (particularly due to the European Central Bank's unconventional monetary policy) where all the eight countries achieved real exchange rate appreciation (and the ability to revert to pre-pandemic levels) despite the financial markets being at the lowest ebb in 2020Q2. An updated multi-country impact analysis of the COVID-19 shock covering different waves, would further enrich the outcome of this study and in particular offer greater insights into how individual countries have sustained their response to the pandemic.

Author Statement

We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

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CRediT authorship contribution statement

Afees A. Salisu: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing. Taofeek O. Ayinde: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Rangan Gupta: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Mark E. Wohar: Validation, Writing – original draft, Writing – review & editing.