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

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

International Journal of Management and Economics
Volume 61 (2025): Issue 5 (December 2025)
By: Aneta Dzik-Walczak and  Anna Gaweł  
Open Access
|Dec 2025

Figures & Tables

Figure 1

Closing prices of WIG20 index and S&P500 index.
Closing prices of WIG20 index and S&P500 index.

Figure 2

DCC for WIG20 index and S&P500 index.
DCC for WIG20 index and S&P500 index.

Figure 3

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

Figure 4

Wavelet coherence analysis for vaccinations.
Wavelet coherence analysis for vaccinations.

DCC modeling results for analyzed indices, infections, and deaths for four waves_

PolandHungaryUnited KingdomCzech RepublicPolandHungaryUnited KingdomCzech Republic
Stock indices - InfectionsStock indices - Deaths
First wave
a 0.2883***0.00000.1931*0.3208***0.1227*0.00000.02650.0000
b 0.6608***0.86750.5398*0.25550.71860.9326***0.8553***0.9374
Stock indices - Infections with exogenous variables Stock indices - Deaths with exogenous variables
a 0.03440.00530.11900.0379**0.02910.0308***0.00000.0236*
b 0.89130.9137***0.53610.8369***0.88940.8691***0.9007***0.9130***
Second wave
a 0.00000.00000.00000.02610.00000.00000.00000.0578
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 0.00130.00320.00000.00980.00450.00840.00490.0053
b 0.88360.89480.91520.9902***0.8653***0.76350.87480.9947***
Third wave
a 0.16610.00000.00000.00000.27060.00000.06780.0000
b 0.00000.93180.8974***0.9418***0.00000.9223***0.5131**0.9686***
Stock indices - Infections with exogenous variables Stock indices - Deaths with exogenous variables
a 0.00000.01190.00000.02950.00000.01810.00000.0169
b 0.92530.8182***0.8966***0.64650.93320.79750.9018***0.8403**
Fourth wave
a 0.75730.01850.00000.00000.00000.02100.00000.0000
b 0.00000.8969**0.8849***0.94060.9106***0.9635***0.9103***0.9493***
Stock indices - Infections with exogenous variables Stock indices - Deaths with exogenous variables
a 0.0262**0.0281***0.00000.0242*0.0262**0.02080.00000.0215
b 0.8451***0.8936***0.9137***0.8858***0.8451***0.8923***0.9049***0.8594***

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 vaccinationsWIG20 – the second-dose COVID-19 vaccinationsWIG20 – booster COVID-19 vaccinations
Estimates P-valueEstimates P-valueEstimates P-value
a 0.01620.19130.05420.02310.05230.4289
b 0.96400.00000.89940.00000.63080.0012

Summary descriptive statistics for WIG20 and S&P500 returns and the number of COVID-19 cases and deaths_

Full samplePre-pandemic timePandemic time
WIG20S&P500WIG20S&P500WIG20S&P500InfectionsDeaths
Number of observations796796283283513513513513
Minimum−14.2456−12.7652−4.5152−4.5168−14.2456−12.76520.00000.0000
Maximum8.09958.96834.43474.50118.09958.9683160.943869.3147
1st quantile−0.7063−0.4318−0.6069−0.2892−0.8302−0.53210.20940.1568
3rd quantile0.72170.73110.58140.62220.84230.82201.79541.3506
Mean−0.00670.0758−0.06580.06390.02590.08243.04142.2721
Median−0.01900.1333−0.06770.10470.01980.14950.79540.6659
Variance2.56892.03501.23480.87433.30572.6781122.313348.0013
Standard deviation1.60281.42651.11120.93511.81821.636511.05956.9283
Skewness−1.2600−1.0222−0.3107−0.7433−1.3338−0.98229.56626.8743
Kurtosis13.032817.17352.89875.168211.656914.6068111.401854.6338

DCC and ADCC modeling results for the analyzed countries_

PolandHungaryUKCzech RepublicPolandHungaryUKCzech 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 {\mu }_{\text{country}} 0.01100.0623*0.0701***0.084***0.01100.0623*0.0701***0.0840***
(0.0374)(0.0361)(0.0279)(0.0242)(0.0376)(0.0360)(0.0279)(0.0242)
A R ( 1 ) country AR{(1)}_{\text{country}} 0.3929***−0.8023* 0.3929***−0.8023*
(0.1240)(0.4848) (0.1241)(0.4850)
M A ( 1 ) country MA{(1)}_{\text{country}} −0.4476***0.8348* −0.4476***0.8348*
(0.1205)(0.4457) (0.1204)(04459)
ω country {\omega }_{\text{country}} 0.0761**0.0926**0.0523***0.0532***0.0761*0.0926**0.0523***0.0532***
(0.0401)(0.0470)(0.0202)(0.0199)(0.0401)(0.0470)(0.0202)(0.0199)
α country {\alpha }_{\text{country}} 0.0974***0.1486***0.1495***0.1977***0.0974***0.1486***0.1495***0.1977***
(0.0380)(0.0425)(0.0448)(0.0504)(0.0380)(0.0425)(0.0445)(0.0503)
β country {\beta }_{\text{country}} 0.8726***0.7944***0.8124***0.7579***0.8726***0.7944***0.8124***0.7580***
(0.0477)(0.0653)(0.0485)(0.0555)(0.0477)(0.0652)(0.0483)(0.0556)
shape \text{shape} 4.4122***6.7275***5.4617***5.1220***4.4122***6.7275***5.4617***5.1220***
(0.5929)(1.5729)(0.8774)(0.8574)(0.6118)(1.5808)(0.8878)(0.8627)
μ S&P500 {\mu }_{\text{S\&P500}} 0.1419**0.1419**0.1434***0.1434***0.1419**0.1419**0.1434***0.1434***
(0.0598)(0.0599)(0.0251)(0.0251)(0.0598)(0.0600)(0.0250)(0.0251)
A R ( 1 ) S&P 500 AR{(1)}_{\text{S\&P}500} 0.8719**0.8719** 0.8719**0.8719**
(0.3624)(0.3627) (0.3622)(0.3630)
M A ( 1 ) S&P 500 MA{(1)}_{\text{S\&P}500} −0.9276***−0.9276*** −0.9276***−0.9276***
(0.2795)(0.2797) (0.2794)(0.2800)
ω S&P 500 {\omega }_{\text{S\&P}500} 0.0518***0.0518***0.0545***0.0545***0.0518***0.0518***0.0545***0.0545***
(0.0159)(0.0160)(0.0165)(0.0165)(0.0159)(0.0159)(0.0164)(0.0165)
α S&P 500 {\alpha }_{\text{S\&P}500} 0.2403***0.2403***0.2428***0.2428***0.2403***0.2403***0.2428***0.2428***
(0.0522)(0.0522)(0.0520)(0.0519)(0.0516)(0.0517)(0.0512)(0.0517)
β S&P 500 {\beta }_{\text{S\&P}500} 0.7416***0.7416***0.7354***0.7354***0.7416***0.7416***0.7354***0.7354***
(0.0434)(0.0433)(0.0438)(0.0438)(0.0431)(0.0432)(0.0436)(0.0437)
shape \text{shape} 5.6651***5.6651***6.0295***6.0295***5.6651***5.6651***6.0295***6.0295***
(1.0134)(1.0318)(1.0559)(1.0471)(1.0155)(1.0334)(1.0563)(1.0473)
a a 0.00570.0118**0.00300.0136**0.00570.00990.00110.0131**
(0.0054)(0.0055)(0.0058)(0.0067)(0.0071)(0.0063)(0.0150)(0.0069)
b b 0.9751***0.9760***0.98***0.9750***0.9751***0.9739***0.9708***0.9746***
(0.0113)(0.0095)(0.0266)(0.0146)(0.0459)(0.0124)(0.0422)(0.0152)
c c — 0.00000.00430.00270.0011
(0.0181)(0.0087)(0.0159)(0.0069)
m shape m\text{shape} 5.7024***6.2533***5.9472***5.6219***5.7024***6.3229***5.9864***5.6353***
(0.6054)(0.7427)(0.0595)(0.5705)(0.6664)(0.7838)(0.6372)(0.5802)
Models with exogenous variables
a a 0.01620.0217***0.00000.0245***0.0255***0.0217***0.0395***0.0447***
(0.1287)(0.0030)(0.0000)(0.0017)(0.0074)(0.0035)(0.0131)(0.0056)
b b 0.9409***0.9248***0.8833***0.9147***0.9036***0.9248***0.9131***0.9491***
(0.1477)(0.0126)(0.0875)(0.0209)(0.0410)(0.0149)(0.0149)(0.0310)
c c 0.00000.00000.0068**0.0070***
(0.0032)(0.0024)(0.0033)(0.0022)
DOI: https://doi.org/10.2478/ijme-2025-0023 | Journal eISSN: 2543-5361 | Journal ISSN: 2299-9701
Journal RSS Feed
Language: English
Page range: 26 - 39
Submitted on: Jun 28, 2024
|
Accepted on: Jun 8, 2025
|
Published on: Dec 30, 2025
Published by: 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
Related subjects:
Business and economics,
Political economics,
Economic theory, systems and structures,
Business management,
Management accounting, financial controlling, cost calculation, investment

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

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