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Current Issues in Tourism
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rcit20
Influence of COVID-19 pandemic on the tourism
sector: evidence from China and United States
stocks
Wang Yiwei, Khakan Najaf, Guilherme F. Frederico & Osama F. Atayah
To cite this article: Wang Yiwei, Khakan Najaf, Guilherme F. Frederico & Osama F. Atayah
(2021): Influence of COVID-19 pandemic on the tourism sector: evidence from China and United
States stocks, Current Issues in Tourism, DOI: 10.1080/13683500.2021.1972944
To link to this article: https://doi.org/10.1080/13683500.2021.1972944
Published online: 06 Sep 2021.
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Influence of COVID-19 pandemic on the tourism sector: evidence
from China and United States stocks
Wang Yiwei
a
, Khakan Najaf
b
, Guilherme F. Frederico
c
and Osama F. Atayah
d
a
School of Tourism and Management, Guangxi Normal University, Guilin, People’s Republic of China;
b
Sunway
University, Subang Jaya, Malaysia;
c
School of Management, Federal University of Paraná –UFPR, Curitiba, Brazil;
d
College of Business, Abu Dhabi University, Abu Dhabi, UAE
ABSTRACT
The coronavirus disease (COVID-19) has adversely impacted the globally
interconnected economy and brought the tourism sector to a
temporary standstill. As such, this study aimed to investigate the
spillover effect of industrial sectors by emphasizing the tourism sector.
The study data was gathered from China and The United States (US)
between 2019 and 2020 (pandemic period) using the Multivariate
Generalised Autoregressive Conditional Heteroscedastic-Dynamic
Conditional Correlation (MGARCH-DCC) and Wavelet Coherence
Transform (CWT) techniques to analyse the investment holding period.
Country-wise, the sectoral return volatility in China was significantly
higher than the US counterpart. Additionally, the intra-sector correlation
analyses demonstrated that Chinese sectors successfully mitigated the
intra-sector correction in the last quarter of 2019. A short-term holding
period was also suggested for investors in China while a long-term
counterpart was recommended for investors in the US. Regarding the
Chinese and US industrial sectors in the first quarter of 2020, it was
mutually concluded that both country stocks reflected high volatility.
The tourism sector was also negatively affected throughout
the pandemic period (between 2019 and 2020). Essentially, this study
offered practical contributions to investors, mutual fund holders, and
brokers.
ARTICLE HISTORY
Received 9 June 2021
Accepted 18 August 2021
KEYWORDS
Economy; Volatility sectors;
COVID-19; Tourism; Spillover
1. Introduction
Undeniably, COVID-19 has impacted the financial aspects of different industrial supply chains. The
pandemic impacts within tourism are highly significant following global and regional measures
involving travel bans and country lockdowns. Given the severe and widespread infection based
on the number of patients and afflicted area sizes, the Director-General of the World Health Organ-
isation proclaimed COVID-19 as a worldwide pandemic on March 11, 2020. Although the Severe
Acute Respiratory Syndrome (SARS) in 2003 and Ebola Virus Disease (EVD) in 2014 demonstrated
adverse implications on financial markets, SARS and EVD outbreak impacts at the epidemic level
were relatively restricted due to geographic timeframe (Chen et al., 2007; Hai et al., 2004; Hanna
& Huang, 2004; Ichev & Marinč,2018; Overby et al., 2004; Pine & McKercher, 2004; Siu & Wong,
2004). In this vein, COVID-19 adversely impacted national and global economies. Various industrial
sectors are currently facing multiple complexities with a specific degree of loss. The tourism sector is
particularly rife with various intricacies: low demand, supply chain and transportation disruption, and
travel ban.
© 2021 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Khakan Najaf khakann@sunway.edu.my
CURRENT ISSUES IN TOURISM
https://doi.org/10.1080/13683500.2021.1972944
The relevance of the tourism industry has been widely studied in the past two decades to inves-
tigate carbon tax within the Chinese tourism sector (Zhang & Zhang, 2020), well-being as a dynamic
assemblage (Li & Chan, 2020), digital free tourism (Egger et al., 2020), foreign direct investment (Cró
& Martins, 2020), income level of different countries (Shi et al., 2020), and the influencing factors of
residents’willingness to aid foreign tourism (Hateftabar & Chapuis, 2020). Notwithstanding, a litera-
ture gap was identified on the tourism industry volatility during COVID-19 compared to other sectors
or nations (Jamal et al., 2021). The issue required examination for investors to acknowledge tourism
stock risks compared to other industrial sectors. Thus, it was deemed crucial to investigate the
tourism industry spillover effect during COVID-19.
The current study was catalyzed by two factors. The first aspect involved recent studies involving
COVID-19 impacts on the tourism sector. Past studies failed to consider the pandemic spillover effect
on various sectors with MGARCH-DCC and CWT techniques. For example, Khalid et al. (2021)
revealed the extent to which the tourism sector size influenced economic policy responses to
COVID-19 with data from 136 countries through multiple panel regression analyses. Resultantly,
the tourism sector was positively associated with both fiscal and monetary policy responses to
the pandemic. The second aspect involved the seminal work of Jaipuria et al. (2021) who examined
foreign tourists’arrival in India and foreign exchange earnings with artificial neural networks. A sub-
stantial decline was identified concerning overseas tourists’arrivals in India following COVID-19 in
2020.
It was deemed appropriate to examine COVID-19 impacts on tourism using novel MGARCH-DCC
and CWT techniques with overriding benefits. For example, MGARCH-DCC captured the time-varying
parameter in mean and variance to facilitate investors’identification of the time-varying correlation
among asset classes. The MGARCH-DCC model could be easily utilized for many asset portfolios.
Meanwhile, the Wavelet technique could manage heterogeneous investment horizons by consider-
ing time and frequency domains. In this regard, the aforementioned techniques potentially offered a
holistic perspective of the pandemic and garnered the interest of scholars, educators, practitioners,
and emerging technological fields.
Despite extensive recent studies on the pandemic spillover effect in various sectors, no study has
revealed the econometric evidence for investors’investment strategies during the pandemic period.
For example, Liu et al. (2020) investigated the short-term COVID-19 impact on 21 leading stock
market indices worldwide. All the indices fell post-outbreak with Asian country stock indices reflect-
ing negative and abnormal returns. Although Akhtaruzzaman et al.’s(2020) research on China and
G7 nations examined how COVID-19 impacted financial and non-financial firms, the study did not
highlight long and short investment holding positions for investors. As COVID-19 predictably
induced negative impacts on various sectoral indices (tourism, finance, service, and construction),
investors needed to know which sector or country required long or short-term investment holding.
This study aimed to resolve a particular investment strategy element by adopting techniques to
highlight the degree to which the diversifying features among sectoral indices could evolve based
on the anticipated duration and emergence of COVID-19 (Kim et al., 2021). Goodell (2020) empha-
sized the need for more scholars to investigate financial and stock impacts following COVID-19.
Although some researchers have investigated the COVID-19 impacts within financial and stock
markets (Liu et al., 2021; Salisu et al., 2021; Matos et al., 2021), none solely emphasized the
tourism sector.
This research aimed to examine the spillover effect in tourism compared to finance, service, and
construction sectors by evaluating two relevant markets: the US and China. The study findings con-
tributed to the current body of tourism and COVID-19 literature in several ways. First, this study
offered archival-based evidence of the pandemic effect on industrial sectors involving the US and
China. Contrarily, past studies utilized text-based and panel regression approaches in the US
setting (Albulescu, 2021; Baker et al., 2020). This study also supplemented the present COVID-19
and tourism literature by revealing (i) cross-country analyses between the US and China and (ii)
how the COVID-19 spillover impact differed country-wise. The finding paralleled past research
2W. YIWEI ET AL.
that suggested quicker regulatory implementations and social activity pattern shifts compared to
the US (McKenzie & Adams, 2020).
Despite distinct considerations on COVID-19 and the tourism sector, no study to date has con-
sidered investment strategies (investors’holding period) during COVID-19. As such, this study con-
tributed to investment strategy-oriented literature. The research also demonstrated how investors
could acquire personal benefits while upholding long-term positions in countries with improved sec-
toral growth and short-term counterparts following high COVID-19 spillover effects. Overall, this
study proved useful to understand audit pricing, particularly in less developed and developed econ-
omies (China and the US, respectively).
The current study adopted MGARCH-DCC and CWT techniques to analyse the evolution of daily
return data from the Thomson Reuters Eikon database between 30 January 2019 and 6 November
2020. Among all the US and Chinese sectors, tourism remained highly volatile and risky (Najaf
et al., 2021; Najaf et al., 2020). As a highly affected sector, tourism tended to offer diversification
opportunities (mainly in construction) similar to the US and China (Dube et al., 2021; Foo et al.,
2020). The wavelet-based findings revealed that investors could gain portfolio diversification
benefits in medium-term investment holding periods (16–64 days), thus suggesting fewer opportu-
nities for speculative activities.
Past studies attempted to investigate the Chinese stock market spillover impacts on foreign
markets. Based on the Chinese and six more stock markets, Najaf and Chin (2021) examined the
relationship between Chinese stock market returns and foreign counterparts. Three arguments
were presented concerning the Chinese stock market impacts on foreign counterparts. Globally,
the Shanghai stock market denotes the third-largest market (by market capitalization) that easily
influenced other counterparts. The Shanghai stock exchange was also the first to crash among
other indices and subsequently impact global equity markets. Additionally, relevant literature (Groe-
newold et al., 2004; Huyghebaert & Wang, 2010; Johansson & Ljungwall, 2009; Yu et al., 2020; Zhou
et al., 2012) outlined a unidirectional volatility flow from the mainland China stock market to foreign
counterparts. Based on Najaf and Chin’s(2021) seminal work, the spillover impact of cross-industrial
and country sectors were analysed (from China and the US). The subsequent sections are organized
as follows: relevant literature review, hypothesis formulation, research methods, results and discus-
sion, and conclusion.
2. Theoretical background and hypothesis
The expectancy theory predicted activities to be motivated by two factors: (i) expectation (expect-
ancy) or the probability that effort would facilitate anticipated goal attainment and (ii) the perceived
value (valence) of outcome from the actions. Hence, the ‘expectancy-valence’framework was estab-
lished (Snead & Harrell, 1994; Vroom, 1964). The expectancy theory denotes an individual’s behav-
ioural intentions in various domains, including employee motivation, organizational behaviour
(Chen & Fang, 2008), and stock market performance (Fudge & Schlacter, 1999). With further study
and application, the theory could provide insights into decision-making processes towards organiz-
ational goal attainment: exporting strategies (Wood et al., 2015) and stock investment strategies
(Chen et al., 2016).
Unlike most studies that incorporated the ‘expectancy-value’framework to examine the sectoral
stock volatility spillover-investor expectation relationship during COVID-19, the expectancy theory
could be applied to (i) examine stock market investors’adjustment-oriented decisions amidst the
pandemic and (ii) evaluate potential outcomes from the shift. Assumably, investors who expected
abnormal risk volatility spillovers within a sector would be reluctant to make investments. The
China and US equity markets (Shanghai stock market or New York stock exchange) reflect rapid
growth on average. Foreign investors have enjoyed substantial profit given the low oil price and
interest rates (approximately between 3% and 4%) in the past 20 years. In 2003, investors earned
$150 billion and reaped a net profit of $150 billion in 2004 (Bley & Chen, 2006). Apart from
CURRENT ISSUES IN TOURISM 3
foreign investors’profit generation, the China and US stock markets have garnered much attention
in the last few years. For example, several studies described the different US and Chinese sectoral
attributes during COVID-19 with emphasis on the tourism industry.
The COVID-19 impact on tourism was palpable over the last decade following negative multiply-
ing effects on other supporting sectors. Based on recent COVID-19 cases, inbound and outbound
tourism might be promptly reduced due to health risks (viral infections from disease outbreaks). Con-
sequently, tourists refrained from visiting geographic areas or destinations in line with government
restrictions. In the study context, health crises could induce significant market shifts in affected areas
when travellers consciously decided to avoid specific crises (Seraphin, 2021). As such, travellers’atti-
tudes and visiting choices could be affected by the perceived danger associated with health risks
(Reichel et al., 2007; Zhang et al., 2020).
The term ‘crisis’implies abrupt and unpredictable developments that may lead to civil unrest and
public threats. In such circumstances, disease outbreaks proved challenging for people from multiple
geographical areas or worldwide (Baker et al., 2020). Specifically, one specific area depicted a health
crisis before nationwide or worldwide expansion, such as the novel COVID-19 pandemic (Albulescu,
2021; Izzeldin et al., 2021; Wang et al., 2021). Thus, travel and destination choices relied on the
tourism situation of a country (Taylor & Toohey, 2007). As tourists’perspectives of personal safety
and security depended on the crisis reports by mass and social media, health emergencies
proved challenging for the tourism industry following adverse media scrutiny (Novelli et al., 2018).
Different studies revealed the outbreak impact on global tourist industries, specifically in South-
east Asian economies during the SARS epidemic (Hai et al., 2004; Hanna & Huang, 2004; Overby et al.,
2004; Pine & McKercher, 2004; Siu & Wong, 2004). In Abdullah et al. (2004), SARS instigated a global
decline of 2.6% within the first quarter of 2003. A decrease of 10% in March and 50% in April was
documented within the Asia Pacific zone. As the most impacted area, Hong Kong revealed a
64.8% decline in March and 67.9% in April following the SARS outbreak during 2003 (Abdullah
et al., 2004). Changes in Chinese tourism applications (Wen et al., 2020) were anticipated with
many global destinations awaiting industrial revitalization. Notably, tourists should choose less
crowded places for social distancing (Seraphin & Dosquet, 2020). Regarding lodging facilities, travel-
lers might expect a limited employee–client relationship. In Wen et al. (2020), managers needed to
anticipate hotel growth towards the end of COVID-19 and focus on eventual crisis experiences.
Gössling et al. (2020) assessed the global impact on aviation, airlines, and hospitality based on
past epidemics and pandemics and the COVID-19 impacts following travel constraints and lock-
downs. Gössling et al. (2020) asserted that pandemics or epidemics could change societies, domestic
economies, and the tourism industry based on empirical findings. Predictably, the burden on tourism
and other industries that sustained the poorest economies in the world would increase due to
disease outbreaks. Changing hotel employees’working behaviours would further aggravate the
adverse effects (Stergiou & Farmaki, 2021). Recent studies examined the COVID-19 impact on
different economic aspects and environmental issues, such as (i) environmental pollutant impacts
amidst COVID-19 within industrial economies in Germany (Bashir & Benghoul et al., 2020), California
(Bashir, Jiang, et al., 2020) and New York (Bashir, Ma, et al., 2020), (ii) COVID-19 impact on stock
market spillover (Arslan & Bashir, 2021), and (iii) worldwide social and economic distress (Bashir
et al., 2020).
As previously discussed, the tourism sector and supporting industries are negatively affected by
disease outbreaks. Williams and Kayaoglu (2020) highlighted the impacts of epidemics on tourism
and supporting industries: tourism products, services, and employment. Notably, almost one-
tenth of the non-financial US economy was related to tourism (9.5% of the US workforce) in 2016.
In Williams and Kayaoglu (2020), hotel accommodation and food services (food and beverage)
denoted 19.7% and 58.7%, respectively, among the total US tourism jobs. Palpably, the mass shut-
down of tourism-related and supporting companies following COVID-19 implied extraordinary socio-
economic effects. Tsionas (2020) predicted incremental adaptations in travel, hospitality, and other
relevant industries post-COVID-19. For example, progressive reopening would entail desirable and
4W. YIWEI ET AL.
viable gains although reopening with the same profit pre-COVID-19 remained relatively harder (feas-
ible by reopening 33% of the industry) (Sharif & Naghavi, 2020). Resultantly, Tsionas opined that
reduced capacity required government funding that significantly varied across hotels. In this vein,
Assaf and Scuderi (2020) asserted the current COVID-19 pandemic to be the most detrimental
tourism crisis despite previous setbacks.
The aforementioned outcome corresponded to Karabulut et al. (2020) and Dolnicar and Zare
(2020). The government and tourism sector essentially influenced recovery efforts following indus-
trial shifts post-pandemic (Assaf & Scuderi, 2020). One of the COVID-19 implications demonstrated a
significant decrease in hotel, airline, cruise ship, and car rental prices (Sharma & Nicolau, 2020). Each
sectoral downturn inevitably raised questions on long-term industrial prospects. Primary concerns
were specifically related to the cruise sector. As Najaf and Chin’s(2021) seminal work implied
China to be the first nation affected by COVID-19, the Chinese stock market impacted other
global counterparts based on the unidirectional relationship between Chinese and global stock
markets during the pandemic. Based on Najaf and Chin’s(2021) seminal work, this study investigated
the COVID-19 impact on the Chinese and US sectors. The study proved essential for investors to
identify which sector outperformed others or underperformed to hold short or long-term positions
parallel to equity returns. In this vein, tourism was perceived as a highly vulnerable industry during
COVID-19. A significantly high intra-relationship between the aforementioned sectors was
anticipated during COVID-19 which simultaneously exacerbated industrial performance. The follow-
ing hypothesis is developed based on past literature, influential aspects, and researchers’
expectation:
H
1:
Ceteris paribus, the tourism sector is more vulnerable during the pandemic period than finance, service, and
construction sectors.
3. Method, sample, and data
The proliferating Chinese and US equity markets (Shanghai stock market or New York stock
exchange) were selected based on two reasons. First, the benefits of investing in China and the
US implied strong economic growth with high single-digit economic progress over the past two
decades (fastest-growing major economy in the world). Hence, stock analysis through sectoral per-
formance would benefit investors. Second, the volatility spillover turned bidirectional between the
Chinese equity market and developed counterparts following the first Asian financial crisis in 1997
(Wang & Di Iorio, 2007; Zhou et al., 2012). Likewise, past research suggested that the shock trans-
mission began from the US stock market to the rest of the world following the financial crisis
between 2008 and 2009 (Gamba-Santamaria et al., 2017). Summarily, it was deemed crucial to inves-
tigate the Chinese and US stock markets during COVID-19 as both markets were typically the first to
be affected by financial crises.
This study applied four sectoral equity returns from the US as follows: (i) DJGL UNITED STATES
TRVL&TRSM $ –PRICE INDEX (tourism); (ii) DJGL UNITED STATES FINANCIALS $ - PRICE INDEX
(finance); (iii) DJGL UNITED STATES SERVICES $ –PRICE INDEX (service); (iv) DJGL UNITED STATES
CONSTRUCTION $ –PRICE INDEX (construction). Meanwhile, the Chinese counterparts are as
follows: (i) CHINA-DS Travel & Tourism –PRICE INDEX (tourism); (ii) CHINA-DS Financials –PRICE
INDEX (Finance); (iii) CHINA-DS Ind. GOODS and SERVICE –PRICE INDEX (service); (iv) CHINA-DS Con-
struction –PRICE INDEX (construction). The study data collected from the Thomson Reuters Eikon
database ranged between 30 January 2019 and 6 November 2020.
Two study methods [MGARCH- DCC and Continuous Wavelet Transform (CWT)] were utilized to
explore time-varying volatility and the correlation between assets and time-varying portfolio oppor-
tunities across investment holding periods. The methods would be thoroughly elaborated in the fol-
lowing sub-sections.
CURRENT ISSUES IN TOURISM 5
3.1. The MGARCH-DCC model
The model was proposed by Engle (2002) and extended by Pesaran and Pesaran (2009) to examine
volatility and the correlation of asset shifts with time (direction and magnitude of positive or nega-
tive and strong or weak relationships). The model application implied several benefits. First,
MGARCH-DCC highlighted the time-varying parameter in mean and variance. The model also
assisted investors to identify the time-varying correlation among asset classes. Lastly, MGARCH-
DCC could be easily used for the portfolio of multiple assets.
This study extensively applied the MGARCH-DCC model to examine portfolio opportunities and
address the research questions following Hsu Ku and Wang (2008) and Najeeb et al. (2015). The
model is computed as follows:
rt=
b
0+
k
i=1
b
irt−1+ut=
m
t+ut
m
t=E[rt|Vt−1|]
m
tIVt−1N(0, Ht)
Ht=GtRtGt
Gt=diag
hii,t
Zt=G−1
tut
Specifically, h
ii
,t indicates the approximate conditional variance of the univariate GARCH model,
Gtrepresents the diagonal matrix of conditional standard deviation, and R
t
and Z
t
imply the time-
variant correlation matrix and uniform residual vector where the mean-zero and variance-one fea-
tures are maintained. The following equation is computable following Hsu Ku and Wang (2008):
Rt=(diag(QT))−1/2Qt(diag(Qt))−1/2
Qt=(qij,t)
(diag(Qt))−1/2=diag 1
q11,t
√,,,,, 1
qnn,t
√
q(ij,t)=p−ij =
a
(Z(i,t−1) Z(j,t−1) −p−ij )+
b
(q(ij,t−1) −p−ij)
Essentially, p−ij and pi,j,t=qi,j,tI
qii,tqjj,t
√represent unconditional and conditional correlation
coefficients, respectively. As the assumption of a normal distribution is at stake following the fat-
tailed pattern of financial asset returns, Student’s T- distribution is duly applied. In other words,
the conditional distribution ut|Vt−1N(0, Ht) is replaced with ut|Vt−1student −t(ut; v), (0, Ht)
where the degree of freedom is represented by v.
3.1. The CWT
Investors and fund managers revealed different preferences regarding time scales, investment hor-
izons, or stockholding periods following risk premium variances. As such, a truly dynamic and parallel
relationship proved necessary to identify the opportunity. The aforementioned correlation proved
possible among different assets when the data were decomposed into various time scales or
multi-holding periods. Hence, CWT could manage heterogeneous investment horizons by consider-
ing time and frequency domains. Following past CWT technique research (Ali et al., 2019; Aloui &
Hkiri, 2014; Najeeb et al., 2015; Rahim & Masih, 2016; Vacha & Barunik, 2012), this study applied
CWT to examine the heterogeneous investment horizons.
6W. YIWEI ET AL.
Notably, CWT wx(u,s)was achieved by estimating a mother wavelet C(a linear transformation in
the basic function is the scaled and shifted version of one function) within the examined time series
x(t)[l2(R) (see Najeeb et al., 2015) where:
Wx(u,s)=1
−1
x(t)1
s
√
c
t−u
s
dt
Specifically, uimplies the domain of time while sdenotes the location within the frequency domain.
Thus, CWT simultaneously provided details on time and frequency by mapping the original series
into uand sfunctions. This research employed a bivariate framework (Wavelet Coherence) to identify
the relationship between two-time series (how closely and strongly X and Y were interconnected by
linear evolution). Parallel to Torrence and Webster (1999), the two-time series wavelet coherence is
described as follows:
R2
n(s)=IS(s−1Wxy
n(s))I2
S(s−1IWx
n(s))I2.S(s−1IWy
n(s))I2
Specifically, S represents the smoothing operator, srepresents wavelet scale, WWnx(S) is a continu-
ous evolution of the time series X, Wny(S) is the continuous wavelet transform of the time series Y,
and Ynxy (s) indicates the cross wavelet transform of the two-time series X and Y (see Gençay et al.,
2002 and In & Kim, 2013). Past research on interactions or interrelations between assets typically
applied Ordinary Least Square, cointegration test, generalized VAR, BEKK-GARCH, ARMA, and VEC,
Copula, or EGARCH models. Regardless, the poor fit in examining hedging opportunities (Sadorsky,
2012) led to extended literature reviews on a wavelet-based approach. As such, this research offered
time-varying prospects for diversification and examined the returns features in multiple investment
horizons (remarkably lacking in past studies).
4. Results and discussions
This section reports the statistical results and analysis of the test findings. The descriptive statistics in
Table 1 outlines the mean, standard deviation, and skewness and kurtosis of all four US and Chinese
sectors. The US tourism implied the lowest mean (0.00039) while the Chinese service sector reflected
the lowest mean (0.00050). Meanwhile, the tourism sector implied the highest standard deviation for
both the US and China (0.02442 and 0.02863, respectively). The skewness and kurtosis value implied
normal data distribution.
This study aimed to examine the relationship among all four sectors for portfolio diversification
and time-varying volatility and correlation during COVID-19. The maximum likelihood estimates of
lambda (λ) and delta (δ) involving the four sectoral returns of the two largest economies are sum-
marized in Table 2. The t-statistics indicated volatility decay over a long period. Specifically, t-tests
highlighted all the parameters to be highly significant. The Table 2 outcomes implied volatility par-
ameters to be highly substantial (gradual volatility decay over time). As such, the presence of risk in
returns was gradually neutralized following market disruption (sectoral return volatility shifted over
time). The sum of lambda1_ Tourism and lambda2_Tourism (0.83100+.058330=0.88933) were under
Table 1. Descriptive statistics of all sectors.
Sectors Country Mean SD Min Max Skewness Kurtosis
Tourism US .00039 .02442 −.1254 .12180 −.36628 10.698
China .00074 .02863 −.10091 .19776 .67841 8.22374
Finance US .00040 .02010 −.14660 .12007 −.65245 17.115
China .00086 .01299 −.04134 .072007 .40399 6.2238
Service US .00097 .01542 −.11056 .07521 −1.0661 15.070
China .002310 .02225 −.06834 .11890 .30384 5.5073
Construction US .00095 .02175 −.12768 .09930 −.644831 9.9144
China .000503 .01765 −.08748 .080100 .38116 6.6211
CURRENT ISSUES IN TOURISM 7
1, hence implying that the Bitcoin return volatility did not follow the Integrated Generalised Auto
Regressive Conditional Heteroskedasticity (IGARCH) model. Alternatively, volatility shocks proved
transitory as the dynamics indicated a mean-reverting feature. In other words, asset returns would
significantly ascend and descend albeit appearing to converge into the mean value in the long
run (did not completely crash to zero). Otherwise, the shock features proved irreversible and possibly
led investors and fund managers to lose investments. Meanwhile, speculators would welcome such
personally beneficial temporary shocks (Najeeb et al., 2015).
4.1. The MGARCH_DCC model results
This section presents the MGARCH_DCC application to detect investors’portfolio diversification
opportunities. The unconditional volatility and correlations are illustrated in Table 3.
The diagonal elements represented unconditional asset volatility while off-diagonal elements in
Table 3 indicated the unconditional correlation between assets. The unconditional volatility of 1 or
near to 1 implied higher asset volatility while the unconditional volatility of zero or near to zero
reflected weaker asset volatility. Resultantly, the tourism sector posited the highest volatility for
Table 2. Multivariate GARCH with underlying multivariate t-distribution.
Volatility decay factors unrestricted, different for each variable. Correlation decay factors unrestricted, same for all variables. Based on
463 observations from 30-Jan-19–06-Nov-20.
US
Parameter Estimate Standard Error T-Ratio [Prob]
lambda1_ TOURISM .83100 .059057 14.0711[.000]
lambda1_ FINANCE .79093 .034707 22.7889[.000]
lambda1_ SERVICE .83189 .026741 31.1087[.000]
lambda1_ CONST .84124 .041738 20.1551[.000]
lambda2_ TOURISM .058330 .015709 3.7130[.000]
lambda2_ FINANCE .14248 .022515 6.3281[.000]
lambda2_ SERVICE .10468 .015674 6.6785[.000]
lambda2_ CONST .081612 .017246 4.7323[.000]
delta .97177 .0037590 258.5190[.000]
China
lambda1_ TOURISM −.95615 .042624 −22.4325[.000]
lambda1_ FINANCE .99139 .0047209 210.0012[.000]
lambda1_ SERVICE .90228 .047177 19.1255[.000]
lambda1_ CONST .98633 .0054954 179.4844[.000]
lambda2_ TOURISM .025327 .015205 1.6658[.096]
lambda2_ FINANCE .017139 .0039364 4.3539[.000]
lambda2_ SERVICE .044854 .015969 2.8088[.005]
lambda2_ CONST .018028 .0039285 4.5889[.000]
delta .97832 .0030871 316.9122[.000]
Maximized Log-Likelihood = 4951.2.
df is the degrees of freedom of the multivariate t distribution.
Table 3. Unconditional volatility and correlation.
Sector Country Tourism Finance Service Construction
Tourism US .024655 .78127 .75752 .70933
China .028743 .38359 .53501 .30580
Finance US .78127 .020414 .85488 .85261
China .38359 .013041 .35745 .65850
Service US .75752 .85488 .015574 .74825
China .53501 .35745 .022132 .33003
Construction US .70933 .85261 .74825 .021966
China .30580 .65850 .33003 .017867
Estimated Unconditional Volatility Matrix 1908 observations used for estimation from 30-Jan-19–06-Nov-20. Unconditional Vola-
tilities (Standard Errors) on the Diagonal Elements, Unconditional Correlations on the Off-Diagonal Elements.
8W. YIWEI ET AL.
both the US and China. Contrarily, the Chinese economic sector and US service sector demonstrated
the lowest volatility.
Figure 1 (a) and (b) outlined the time-varying volatility for all sectoral equity returns involving the
full sample period between 30 January 2019 and 6 November 2020. The conditional volatility of all
sectoral equity returns moved together during the entire observation period with some exceptions.
As this research aimed to further explore the volatility pattern during COVID-19, the sample was seg-
regated from the 1st of January 2020. The sub-sample graph presented in Figure 2 (a) and (b) implied
substantially high volatility (for both countries) with a big spark in the first quarter of 2020 and per-
sistently remained high until the end of the sampling period.
The tourism sector remained highly volatile out of all the US and Chinese sectors, thus reflecting
high tourism sector risks. Meanwhile, the time-varying correlation depicted in Figure 3 (a) and (b)
demonstrated asset association to change over time. Based on the result derived during COVID-
19, the first quarter demonstrated low correlated assets that continued to rise and decline in the
last quarter of 2020. Regardless, the slightly varied correlation outcome from China revealed the cor-
relation to persistently decline from the first to the fourth quarter of 2020. The difference affirmed
the ongoing arrival of improved diversification opportunities for China. Furthermore, the US and
Chinese tourism sectors primarily offered diversification opportunities in construction Figure 4.
4.2. The CWT results
Apart from incorporating MGARCH_DCC (see Section 4.1), this study also implemented CWT to
examine the presence of diversification benefits based on heterogeneous investment holding
periods. In Figures 5–7, the horizontal axis displayed the number of trading days while the vertical
counterpart indicated the investment horizon. The curved line represented a 5% significance level
(produced by simulations with Monte Carlo). Particularly, the figures displayed a colour code on
the right ranging from blue (low correlation) to yellow (high correlation). The right-pointing
vectors demonstrated the indices to be in phase while the left-pointing counterparts suggested
the indices to be out of phase. In other words, vectors that pointed to the right and upwards
implied first-series lags while vectors that pointed downwards and to the right reflected series
lags. Vectors that pointed to the left and upwards indicated first-series leads while vectors that
pointed to the left and downwards implied first-series lags (Gallegati et al., 2014). The analysis high-
lighted portfolio returns over different investment holding periods (four to 16, 16–32, 32–64, and 64–
256 days).
Despite the high tourism-finance correlation, the relationship relied on investment horizons.
Based on Figures 5a, 6a, and 7a, comparatively longer investment horizons offered more investment
benefits between the US tourism and finance, tourism and service, and tourism and construction
market. As such, investors could acquire portfolio diversification benefits in long investment
holding periods and suggest lesser opportunities for speculative activities. Regarding China, the
outcome showed the diversification opportunities to prevail in medium-term investment holding
(16–64 days) (see Figures 5b, 6b, and 7b).
Figure 1. (a): The time-varying volatility of sectoral returns during full sample periods. US. (b): The time-varying volatility of sec-
toral returns during full sample periods. China.
CURRENT ISSUES IN TOURISM 9
Figure 2. (a): The time-varying volatility of sectoral returns during COVID-19. (b): The time-varying volatility of sectoral returns
during COVID-19.
Figure 3. (a): The time-varying correlations of sectoral returns during full sample periods. US. (b): The time-varying correlations of
sectoral returns during full sample periods. China
Figure 4. (a): The time-varying correlation of sectoral returns during COVID-19. (b): The time-varying correlation of sectoral
returns during COVID-19.
Figure 5. (a) CWT between tourism and finance from 30-Jan-2019–06-Nov-2020 (US). (b) CWT between tourism and finance from
30-Jan-2019–06-Nov-2020 (China).
10 W. YIWEI ET AL.
4.3. Discussion
The last year (2020) revealed a significant increase in empirical development and research invol-
ving the COVID-19 influence on economic performance (Atayah et al., 2021; Najaf et al., 2021).
Recent studies have also highlighted the necessity of implementing appropriate investment strat-
egies to disclose the COVID-19-stock performance correlation (Al-Awadhi et al., 2020; Quinn et al.,
2018; Ramaano, 2021). The study novelty involved utilizing MGARCH-DCC and Wavelet Coherence
methods to evaluate the COVID-19 spillover effect on industrial sectors. This research identified
four main outcomes. First, the analysis demonstrated the Chinese sector volatility to be much
higher than the US counterpart. Second, the intra-sector correlation analysis revealed the
Chinese sectors to better moderate the intra-sector correction in the fourth quarter of 2020.
The findings were comprehensible as the Chinese government was quicker to implement regu-
lations and alter social activity patterns compared to the US counterpart (McKenzie & Adams,
2020).
A short-term holding period was recommended for investors in China while a long-term holding
duration was suggested for investors in the US. Undeniably, the tourism industry reflected low per-
formance compared to the finance, service, and construction counterparts in China and the US. The
findings corresponded to past study data in India and other countries where tourism denoted the
most vulnerable sector during a pandemic (Jaipuria et al., 2021; Khalid et al., 2021). Assumably,
the implementation of international travel restrictions to deter the COVID-19 outbreak almost
halted the global tourism sector.
Figure 7. (a) CWT between tourism and construction from 30-Jan-2019–06-Nov-2020 (US). (b) CWT between tourism and con-
struction from 30-Jan-2019–06-Nov-2020 (China).
Figure 6. (a) CWT between tourism and service from 30-Jan-2019–06-Nov-2020 (US). (b) CWT between tourism and service from
30-Jan-2019–06-Nov-2020 (China).
CURRENT ISSUES IN TOURISM 11
5. Conclusion
Given that no past infectious disease outbreaks, including the Spanish Flu, had impacted the stock
market as forcefully as COVID-19, this study offered some insightful conclusions. For example, cross-
country analyses between China and the US demonstrated the spillover effect in Chinese sectors to
be significantly higher than the US counterparts. The situation occurred in other sector analyses,
such as finance, service, and construction. Specifically, the Chinese stock market proved more vulner-
able than its US counterpart amidst financial crises. Meanwhile, intra-sector correlation analyses
revealed the Chinese sectors to successfully mitigate the intra-sector correction within the last
quarter of 2019. Notably, the COVID-19 spillover effect did not transfer from one sector to
another in China. A short-term holding period was recommended for investors in China while a
long-term counterpart was recommended for investors in the US. Lastly, the COVID-19 spillover
impact adversely affected the Chinese and US industrial stocks throughout the pandemic period
(between 2019 and 2020), specifically in tourism.
This study contributed to the emerging literature on COVID-19 and sectoral stock spillover. The
Chinese stock market also facilitated shareholder rights protection and mitigated the correlation
among industrial sectors. Practically, mutual fund investors could uphold long-term positions for
the Chinese stock and short-term positions for the US counterpart for abnormal and positive investor
returns. Notwithstanding, this study encountered multiple limitations. First, the researchers did not
have access to unlisted industrial stocks in comparing between listed and non-listed industrial sector
spillover or performance. Second, this research did not analyse the non-linear relationship between
listed stocks and subsequent market performance (Tobin’s Q or PE ratio). As such, further studies
could resolve such limitations and expand the practical and theoretical contributions on spillover
phenomenon in different industrial sectors during substantial disruptions (COVID-19).
Future research on the COVID-19 spillover effect-industrial sector performance relationship could
offer exciting findings by comparing G-20 nations. Additionally, a longitudinal approach could be
considered to ascertain the COVID-19-stock performance correlation. The effect of country-level gov-
ernance with mediating or moderating impacts on COVID-19 could also be explored. For example,
the government response quality to COVID-19 in potentially mitigating adverse COVID-19 impli-
cations could be a future research subject.
Disclosure statement
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of
this article.
ORCID
Guilherme F. Frederico http://orcid.org/0000-0002-5330-4601
Osama F. Atayah http://orcid.org/0000-0002-8787-9956
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