Dynamics and co-movements between the COVID-19 outbreak and Polish stock market: A dynamic conditional correlation modeling and wavelet coherence analysis

	Full sample		Pre-pandemic time		Pandemic time
	WIG20	S&P500	WIG20	S&P500	WIG20	S&P500	Infections	Deaths
Number of observations	796	796	283	283	513	513	513	513
Minimum	−14.2456	−12.7652	−4.5152	−4.5168	−14.2456	−12.7652	0.0000	0.0000
Maximum	8.0995	8.9683	4.4347	4.5011	8.0995	8.9683	160.9438	69.3147
1st quantile	−0.7063	−0.4318	−0.6069	−0.2892	−0.8302	−0.5321	0.2094	0.1568
3rd quantile	0.7217	0.7311	0.5814	0.6222	0.8423	0.8220	1.7954	1.3506
Mean	−0.0067	0.0758	−0.0658	0.0639	0.0259	0.0824	3.0414	2.2721
Median	−0.0190	0.1333	−0.0677	0.1047	0.0198	0.1495	0.7954	0.6659
Variance	2.5689	2.0350	1.2348	0.8743	3.3057	2.6781	122.3133	48.0013
Standard deviation	1.6028	1.4265	1.1112	0.9351	1.8182	1.6365	11.0595	6.9283
Skewness	−1.2600	−1.0222	−0.3107	−0.7433	−1.3338	−0.9822	9.5662	6.8743
Kurtosis	13.0328	17.1735	2.8987	5.1682	11.6569	14.6068	111.4018	54.6338

Source: own elaboration.

Table 2

DCC and ADCC modeling results for the analyzed countries.

	Poland	Hungary	UK	Czech Republic	Poland	Hungary	UK	Czech Republic
	ARMA(1,1)-DCC-GARCH(1,1)		DCC-GARCH(1,1)		ARMA(1,1)-ADCC-GARCH(1,1)		ADCC-GARCH(1,1)
$μ_{country}$	0.0110	0.0623*	0.0701***	0.084***	0.0110	0.0623*	0.0701***	0.0840***
$μ_{country}$	(0.0374)	(0.0361)	(0.0279)	(0.0242)	(0.0376)	(0.0360)	(0.0279)	(0.0242)
$A R {(1)}_{country}$	0.3929***	−0.8023*			0.3929***	−0.8023*
$A R {(1)}_{country}$	(0.1240)	(0.4848)			(0.1241)	(0.4850)
$M A {(1)}_{country}$	−0.4476***	0.8348*			−0.4476***	0.8348*
$M A {(1)}_{country}$	(0.1205)	(0.4457)			(0.1204)	(04459)
$ω_{country}$	0.0761**	0.0926**	0.0523***	0.0532***	0.0761*	0.0926**	0.0523***	0.0532***
$ω_{country}$	(0.0401)	(0.0470)	(0.0202)	(0.0199)	(0.0401)	(0.0470)	(0.0202)	(0.0199)
$α_{country}$	0.0974***	0.1486***	0.1495***	0.1977***	0.0974***	0.1486***	0.1495***	0.1977***
$α_{country}$	(0.0380)	(0.0425)	(0.0448)	(0.0504)	(0.0380)	(0.0425)	(0.0445)	(0.0503)
$β_{country}$	0.8726***	0.7944***	0.8124***	0.7579***	0.8726***	0.7944***	0.8124***	0.7580***
$β_{country}$	(0.0477)	(0.0653)	(0.0485)	(0.0555)	(0.0477)	(0.0652)	(0.0483)	(0.0556)
$shape$	4.4122***	6.7275***	5.4617***	5.1220***	4.4122***	6.7275***	5.4617***	5.1220***
$shape$	(0.5929)	(1.5729)	(0.8774)	(0.8574)	(0.6118)	(1.5808)	(0.8878)	(0.8627)
$μ_{S&P500}$	0.1419**	0.1419**	0.1434***	0.1434***	0.1419**	0.1419**	0.1434***	0.1434***
$μ_{S&P500}$	(0.0598)	(0.0599)	(0.0251)	(0.0251)	(0.0598)	(0.0600)	(0.0250)	(0.0251)
$A R {(1)}_{S&P 500}$	0.8719**	0.8719**			0.8719**	0.8719**
$A R {(1)}_{S&P 500}$	(0.3624)	(0.3627)			(0.3622)	(0.3630)
$M A {(1)}_{S&P 500}$	−0.9276***	−0.9276***			−0.9276***	−0.9276***
$M A {(1)}_{S&P 500}$	(0.2795)	(0.2797)			(0.2794)	(0.2800)
$ω_{S&P 500}$	0.0518***	0.0518***	0.0545***	0.0545***	0.0518***	0.0518***	0.0545***	0.0545***
$ω_{S&P 500}$	(0.0159)	(0.0160)	(0.0165)	(0.0165)	(0.0159)	(0.0159)	(0.0164)	(0.0165)
$α_{S&P 500}$	0.2403***	0.2403***	0.2428***	0.2428***	0.2403***	0.2403***	0.2428***	0.2428***
$α_{S&P 500}$	(0.0522)	(0.0522)	(0.0520)	(0.0519)	(0.0516)	(0.0517)	(0.0512)	(0.0517)
$β_{S&P 500}$	0.7416***	0.7416***	0.7354***	0.7354***	0.7416***	0.7416***	0.7354***	0.7354***
$β_{S&P 500}$	(0.0434)	(0.0433)	(0.0438)	(0.0438)	(0.0431)	(0.0432)	(0.0436)	(0.0437)
$shape$	5.6651***	5.6651***	6.0295***	6.0295***	5.6651***	5.6651***	6.0295***	6.0295***
$shape$	(1.0134)	(1.0318)	(1.0559)	(1.0471)	(1.0155)	(1.0334)	(1.0563)	(1.0473)
$a$	0.0057	0.0118**	0.0030	0.0136**	0.0057	0.0099	0.0011	0.0131**
$a$	(0.0054)	(0.0055)	(0.0058)	(0.0067)	(0.0071)	(0.0063)	(0.0150)	(0.0069)
$b$	0.9751***	0.9760***	0.98***	0.9750***	0.9751***	0.9739***	0.9708***	0.9746***
$b$	(0.0113)	(0.0095)	(0.0266)	(0.0146)	(0.0459)	(0.0124)	(0.0422)	(0.0152)
$c$	—				0.0000	0.0043	0.0027	0.0011
$c$					(0.0181)	(0.0087)	(0.0159)	(0.0069)
$m shape$	5.7024***	6.2533***	5.9472***	5.6219***	5.7024***	6.3229***	5.9864***	5.6353***
$m shape$	(0.6054)	(0.7427)	(0.0595)	(0.5705)	(0.6664)	(0.7838)	(0.6372)	(0.5802)
Models with exogenous variables
$a$	0.0162	0.0217***	0.0000	0.0245***	0.0255***	0.0217***	0.0395***	0.0447***
$a$	(0.1287)	(0.0030)	(0.0000)	(0.0017)	(0.0074)	(0.0035)	(0.0131)	(0.0056)
$b$	0.9409***	0.9248***	0.8833***	0.9147***	0.9036***	0.9248***	0.9131***	0.9491***
$b$	(0.1477)	(0.0126)	(0.0875)	(0.0209)	(0.0410)	(0.0149)	(0.0149)	(0.0310)
$c$					0.0000	0.0000	0.0068**	0.0070***
$c$					(0.0032)	(0.0024)	(0.0033)	(0.0022)

Notes: Standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1.

Source: own elaboration.

Table 3

DCC modeling results for analyzed indices, infections, and deaths for four waves.

	Poland	Hungary	United Kingdom	Czech Republic	Poland	Hungary	United Kingdom	Czech Republic
Stock indices - Infections					Stock indices - Deaths
First wave
a	0.2883***	0.0000	0.1931*	0.3208***	0.1227*	0.0000	0.0265	0.0000
b	0.6608***	0.8675	0.5398*	0.2555	0.7186	0.9326***	0.8553***	0.9374
Stock indices - Infections with exogenous variables					Stock indices - Deaths with exogenous variables
a	0.0344	0.0053	0.1190	0.0379**	0.0291	0.0308***	0.0000	0.0236*
b	0.8913	0.9137***	0.5361	0.8369***	0.8894	0.8691***	0.9007***	0.9130***
Second wave
a	0.0000	0.0000	0.0000	0.0261	0.0000	0.0000	0.0000	0.0578
b	0.9029***	0.8995***	0.8765***	0.9251***	0.8882***	0.9119***	0.8794***	0.7223***
Stock indices - Infections with exogenous variables					Stock indices - Deaths with exogenous variables
a	0.0013	0.0032	0.0000	0.0098	0.0045	0.0084	0.0049	0.0053
b	0.8836	0.8948	0.9152	0.9902***	0.8653***	0.7635	0.8748	0.9947***
Third wave
a	0.1661	0.0000	0.0000	0.0000	0.2706	0.0000	0.0678	0.0000
b	0.0000	0.9318	0.8974***	0.9418***	0.0000	0.9223***	0.5131**	0.9686***
Stock indices - Infections with exogenous variables					Stock indices - Deaths with exogenous variables
a	0.0000	0.0119	0.0000	0.0295	0.0000	0.0181	0.0000	0.0169
b	0.9253	0.8182***	0.8966***	0.6465	0.9332	0.7975	0.9018***	0.8403**
Fourth wave
a	0.7573	0.0185	0.0000	0.0000	0.0000	0.0210	0.0000	0.0000
b	0.0000	0.8969**	0.8849***	0.9406	0.9106***	0.9635***	0.9103***	0.9493***
Stock indices - Infections with exogenous variables					Stock indices - Deaths with exogenous variables
a	0.0262**	0.0281***	0.0000	0.0242*	0.0262**	0.0208	0.0000	0.0215
b	0.8451***	0.8936***	0.9137***	0.8858***	0.8451***	0.8923***	0.9049***	0.8594***

Source: own elaboration.

Table 4

DCC modeling results for WIG20, the first-dose COVID-19 vaccinations, second-dose COVID-19 vaccinations, and booster COVID-19 vaccinations.

	WIG20 – the first-dose COVID-19 vaccinations		WIG20 – the second-dose COVID-19 vaccinations		WIG20 – booster COVID-19 vaccinations
	Estimates	P-value	Estimates	P-value	Estimates	P-value
a	0.0162	0.1913	0.0542	0.0231	0.0523	0.4289
b	0.9640	0.0000	0.8994	0.0000	0.6308	0.0012

Source: own elaboration.

Wavelet coherence analysis. (a) The full sample. (b) The first wave. (c) The second wave. (d) The third wave. (e) The fourth wave.

Wavelet coherence analysis for vaccinations.

References

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Articles in this issue

DOI: https://doi.org/10.2478/ijme-2025-0023 | Journal eISSN: 2543-5361 (formerly 2299-9701) | Journal ISSN: 2299-9701

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Language: English

Page range: 28 - 41

Submitted on: Jun 28, 2024

Accepted on: Jun 8, 2025

Published on: Dec 30, 2025

Published by: SGH Warsaw School of Economics

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

COVID-19 pandemic,

Stock market,

WIG20 index,

Dynamic conditional correlation,

Wavelet coherence

JEL:

G14,

G15,

G18

Related subjects:

Business and economics,

Business management,

Management accounting, financial controlling, cost calculation, investment

© 2025 Aneta Dzik-Walczak, Anna Gaweł, published by SGH Warsaw School of Economics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 61 (2025): Issue 5 (December 2025)

Dynamics and co-movements between the COVID-19 outbreak and Polish stock market: A dynamic conditional correlation modeling and wavelet coherence analysis

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