Impact of COVID-19 pandemic on Moroccan sectoral stocks indices.

In this paper, we make an original contribution by identifying the impact of COVID-19 on Moroccan sectoral stocks indices. For this purpose, we collected data of 22 sectors from the Casablanca stock exchange from January 2017 to December 2021 and investigated two regres- sion models that included a dummy variable representing the onset of COVID-19. In addition, we examined performance measures (Sharp ratio and Treynor ratio) and risk measures (CVaR and Beta) of each individual sector before and during COVID-19. Furthermore, a GARCH model is applied to show conditional variance, aiming to emphasize the volatility of the selected stocks indices overall the chosen period. The results allowed us to divide sectors into two samples: the first one, referred to as sample 1, that was negatively impacted, and the second one, referred to as sample 2, that benefited from the pandemic of COVID-19. Further, conditional variance revealed that COVID-19 boosted, significantly but for a short period, the volatility of all sectors, even though the magnitude of the effect on volatility varies by sample and also by sector. Overall, we see COVID-19 as a crisis for some sectors and an opportunity for a new business transformation, as it is a period that results in significant improvement for some specific sectors. Furthermore, our results reflect the behavior of the sectors of an African emerging market during the COVID-19 outbreak, which is relevant for the formulation of strategies to ensure financial sustainability during future sanitary crises of this magnitude, for this type of economy.

Introduction

The COVID-19 disease was identified in Wuhan, China, in December 2019. And in March 2020, the World Health Organization (WHO) classified COVID-19 as a world pandemic and the most extensive emergency of the century. Many studies suggest that stock markets react heavily and immediately to significant events, for instance, political events [6], sports events disasters [28]. Since early 2020, the deadly infectious virus, COVID-19, is causing a health, human, and economic crisis around the world. COVID-19, as a public health incident of international importance, has a significant and long-term unfavorable impact on the global economy [34]. Various efforts were made to limit the spread of COVID-19. However, in some African regions, the measures were ineffective because they were poorly planned and coordinated [33]. This explains why Kulohoma [29] postulated that the management of the COVID-19 pandemic should require more adequate implementation and management frame-work. It is therefore essential to examine the effect of COVID-19 on the stock markets. Our research focuses on the Moroccan stock market and is motivated not only by the fact that the literature on the impact of COVID-19 on African financial markets is insufficient, but also because the Casablanca stock exchange is a barometer of an emerging African economy. Therefore, such an understanding helps African countries identify and advance economic policies during serious health crises. This also enables portfolio managers and investors to make more sophisticated portfolio diversification and risk management strategies, considering their tendency to engage in switching activities between sectoral market indices. As a result, highlighting the effects of government policies on sectoral stock indices would provide knowledge about the factors that drive sectoral returns. This will ultimately optimize decision-making regarding asset pricing and allow better portfolio diversification during outbreaks similar to COVID-19. Additionally, Morocco is the second African country most affected by COVID-19, barely behind South Africa [45]. Also, Takyi and Bentum-Ennin [43] report that COVID-19 has also had a significant and negative impact on the Moroccan stock market. However, COVID-19 has had no significant effect on the stock market performance in South Africa, even though the impact was negative. As in Fig. 1 , COVID-19 was detected and identified in Morocco on March 2, 2020, in Casablanca, and on March 10, 2020 the first death, caused by COVID-19, was reported. As a response, Moroccan authorities immediately declared a total lockdown. Economically, it was anticipated that in 2020 Morocco's economy would suffer. Indeed, the Moroccan High Commissioner for Plan (HCP) has opted to reduce Morocco's forecast growth by a third. Furthermore, the Moroccan government's debt has reached 73% of GDP. In March 2020, the MASI (Casablanca stock exchange's main index) witnessed an unprecedented plunge. Indeed, as illustrated in Fig. 2 , MASI dropped by 28.14% during quarantine. However, this drop was followed by a period of partial recovery immediately following containment. This improvement was enabled by the European Financial Markets Authority, which gave the Moroccan stock market a positive rating in June 2020, allowing European companies to trade on the Casablanca stock exchange [8]. Also, the AMMC (Moroccan Capital Market Authority) has taken several steps to minimize the effects of the pandemic on the Moroccan financial market, such as decreasing the volatility of the most liquid instruments from 10% to 4%. In April 2020, IMF1 indicated that Global GDP would decrease by 3%. Takyi and Bentum-Ennin [43] examined the reactions of 13 African stock markets. They concluded that stock market performance in Africa has declined and that the COVID-19 pandemic has no chance of having a beneficial impact on stock market performance in all studied countries, within the chosen period of October 1, 2020 to June 30, 2020. In March 2020, the DAX, Germany's primary stock index, plunged by over 38% [12]. The COVID-19 has caused circuit breakers four times in the US markets, and the Dow Jones lost nearly 26% in March 2020. Nevertheless, several industries, including food, healthcare, and natural gas, performed well and received favorable outcomes even during the pandemic [32]. In March 2020, the USA appeared as one of the countries most affected by COVID-19, GDP dropped by 4.8%, with an unemployment rate of over 20%. Also, Dow Jones dropped by 20%, an equivalent of 6400 points, nearly 90% of the S&P1500 stocks were impacted negatively [32], we refer readers to Shahzad et al. [38] for more about the effect of COVID-19 on the US equity sectors. COVID-19 had a significant negative impact on Chinese classical industries, but it also provided the opportunity to develop High-Tech business [18] and industries [24]. During COVID-19, several researchers [13,43] were interested in examining stock market volatility. Because the volatility of stock market returns is directly proportional to market in stability, it is a crucial element in most investment portfolio management decisions. A higher level of volatility indicates that the stock price may fluctuate significantly in the immediate term. Lower volatility suggests that market prices do not fluctuate significantly in the short term [22]. Therefore, we propose the following hypothesis based on previous documentation: the emergence and spread of the COVID-19 would significantly impact the Moroccan financial market's sectors. On the one hand, many sectors would be negatively affected, as already discovered for many countries’ financial markets ([7,31]). On the other hand, it is crucial to consider that COVID-19 created many opportunities for new business [24]. Therefore, it is obvious to assume that many sectors have been positively impacted. We similarly assume that COVID-19 would impact all sectors’ volatility, even though the degree of that impact would be more significant for industries that the pandemic has negatively influenced heavily [43].

Fig. 1

Statistics of COVID-19 in Morocco.

Fig. 2

Evolution of MASI over the period: Jan 2017-Dec 2021.

The paper is structured as follows. The next section presents a literature review. Section three introduces data and methodology. Section four discusses empirical results. The last section concludes.

Literature review

COVID-19 is a sanitary issue and a worldwide crisis that has affected the whole world in many different ways. According to Baker et al. [4], no prior infectious disease outbreak had such a significant impact on the stock market as COVID-19. Ashraf [2] analyzed the reaction of many stock markets to the COVID-19 pandemic and showed that stock markets reacted unfavorably to the increase in the number of confirmed cases and the increase in the number of deaths caused by the COVID-19 pandemic. COVID-19 has a negative impact on the performance of various Chinese industries, particularly the tourism and transportation sectors [41]. Also, Gu et al. [23] analysed a vast dataset including 34,000 companies and found that COVID-19 resulted in a 57% drop in electricity use in the first week of COVID-19. Further, Wang et al. [46] examined the insurance sector and discovered that COVID-19 had a negative impact on the Chinese insurance industry. Batten et al. [7] showed that COVID-19 immediately affected the European banking sector, but for a short duration, which can be explained by the governmental financial assistance measures to maintain market liquidity. To reduce the impact of the pandemic, governments responded with several policy approaches, such as travel bans, school closures, and lockdowns. These restrictive government policies caused abnormal trading activity and destabilized markets, [49] and some sectors, such as the tourism industry, [48]. Bouri et al. [10] examined the New Zealand government's responses to COVID-19 and industrial stock returns using a GARCH model, and the results are summarized as follows. First, the dynamic correlation between industrial stock returns shifted from negative to positive in March 2020, indicating greater interdependence between different industrial stock indices. Second, eight industrial returns have a positive and significant impact. In contrast, none of the government's policies (lockdown, the travel ban, and the stimulus package) had a significant impact on real estate, healthcare, or technology returns. Third, the government's lockdown initiative had a positive impact on the NZ50 (New Zealand Exchange, NZSX 50).

Theoretically, it is important to note that the interdependence between the majority markets may increase. Indeed, Aslam [3] studied 56 indices and discovered that correlations became positive due to the significant uncertainty of COVID-19. Applying the TVP-VAR (see [16]), Bouri et al. in [11] investigated connectedness across various assets (gold, crude oil, world equities, currencies, and bonds) around the COVID-19 outbreak. The overall results reflect the rapid and disturbing effects of the COVID-19 epidemic. Indeed, until early 2020 (before COVID-19), the total dynamic connectedness of the five assets was fairly stable and moderate. The equity and USD indices are the main shock transmitters before the outbreak, while the bond index becomes the main shock transmitter during the COVID-19 outbreak. In addition, using a recently developed newspaper-based index of uncertainty in financial markets due to infectious diseases to capture the recent impact of COVID-19, they found that connectedness is positively related to this index and increases at higher levels of connectedness. As a possible consequence, investors would have limited opportunities for diversification and would be exposed to higher risk. Nonetheless, other studies, such as [35] realized that as COVID-19 cases increased during the pandemic period, investors’ trading activities jumped by 13.9%. Furthermore, Liu et al. [31] demonstrates that the epidemic had a positive effect on High-Tech and health care sectors. From a different point of view, the media has an important part to play in worsening the situation due to the negative image it provides to investors. Ichev and Marincˇ [25] showed that news attention of pandemics affects investor behavior and the stock prices of companies. For the US market, during the first months of COVID-19, the gas, healthcare, food, and technology sectors showed a rapid increase in their market capitalization. On the other hand, hospitality, entertainment, real estate, and crude petroleum are the worst-performing industries [32]. Based on Granger causality [20], the impact of the COVID-19 outbreak on the US equity sectors was studied by Shahzad et al. in [38]. The findings reveal that network structure and spillovers vary significantly depending on market circumstances caused by the pandemic. The financial sector also exhibits an essential switch in dynamics, becoming a visible information receiver rather than a leader, as seen during the great depression 12 years ago. Le et al. [30] examined the dependency networks of some international financial assets and COVID-19, and they found that COVID-19 has an asymmetric impact since the left-tail dependencies become stronger and more prevalent than the right-tail dependencies. On the other hand, they found that Bitcoin, in addition to US Treasury bonds, is disconnected from other assets in the dependency networks, making it a reliable asset for international investors during peak periods of the COVID-19. For Chinese industries, He et al. [24] discovered that COVID-19 positively impacted the healthcare, manufacturing, and technology sectors. However, the environmental industries, transportation, electricity, and mining negatively responded to COVID-19. Additionally, the volatility of stock returns is closely correlated with economic ambiguity [13]. In [39] Shahzad et al. studied the asymmetric volatility spillover among Chinese industries during COVID-19 (from January 2, 2019 to September 30, 2020). The findings reveal that the asymmetric impact of bad and good volatility is time-varying and significantly intense throughout the COVID-19 period. Remarkably, negative volatility spillover shocks dominate positive volatility spillover shocks. This justifies the anticipation of significant volatility during the COVID-19 period. In this context, Ali et al. [1] noted that COVID-19 increased volatility in German and British markets. Also, GARCH models show that COVID-19 has a significant positive influence on the conditional variance for the studied indices in [47], suggesting that the coronavirus has significantly increased stock market volatility. Similarly, Dharani [15] examined the behaviour of the S&P 1200 Global Shariah and non-Shariah sectoral indices using a GARCH (1,1) model from October 1, 2010 to October 29, 2020. The study confirmed that Shariah indices have lower volatility than non-Shariah indices. Likewise, Takyi and Bentum-Ennin [43] documented that for the African stock markets, the rising sectors were significantly less volatile than sectors with intense negative returns during the COVID-19 crisis. Further, based on the GARCH (1,1) model, Chaudhary [13] supported that the volatility of the studied financial market's indices has increased due to the COVID-19 pandemic. Additionally, Baig et al. [5] confirmed the sensitivity of US market volatility to the number of confirmed positive cases. Other studies focused on energy companies, hypothesizing that the pandemic has a direct impact on energy stock returns [14], [50] and volatility [19,21,26,36,36,42], explained that the huge volatility of stock returns is due to the psychological effect on the behavior of investors in the COVID-19 period and not only the result of financial losses.

In this paper, we examine Moroccan stock markets’ reactions to the COVID-19, especially sectoral indices. Our research adds to an evolving literature on market response to COVID-19. Only a few studies explore the African and emerging stock markets regardless of the COVID-19 number of cases or deaths. Our study is, therefore, the first to evaluate how COVID-19 alone affected the Moroccan stock markets.

Data and methodology

Data

We will analyze the time series data of each sector's index in Casablanca stock exchange (Table 1 ) from January 2017 to December 2021, divided into two periods: the pre-COVID period (from January 1, 2017 to March 1, 2020) and during COVID-19 (from March 2, 2020 to December 31, 2021). The study's periods will provide a valuable understanding of Casablanca sectoral indices, as the first case of COVID-19 was reported by Moroccan authorities on March 2, 2020. The data was taking from Casablanca2 stock exchange website.

Table 1

List of the sectors selected from Casablanca stock extchange.

S.No	Sectors	Symbole	Number of Stocks
1	Utilities	UT	1
2	Electricity	ELC	1
3	Mining	MI	4
4	Food Producers & Processors	FD & PRC	7
5	Insurance	INSU	5
6	Telecommunications	TELEC	1
7	Banks	BANK	6
8	Distributors	DIST	7
9	Real Estate Participation & Promotion	RE & PR	3
10	Chemicals	CHEMI	2
11	Transport	TRANS	2
12	Pharmaceutical Industry	PHAR	2
13	Oil & Gas	OL & GZ	2
14	Materials, Software & Computer Services	MS & CS	7
15	Forestry and Paper	FY & PR	1
16	Beverages	BEV	2
17	Transportation Services	Tran Sev	1
18	Holding Companies	HC	2
19	Construction & Building Materials	Co & BM	7
20	Leisures & Hotels	Lr & Ht	1
21	Investment Companies & Other Finance	IC & OFI	4
22	Engineering & Equipment Industrial Goods	En & EIG	2

To analyze the impact of the COVID-19 on the return of sectoral indices, we will consider several models: regression models and the GARCH (1,1) model. Also, measures of performance and risk are investigated.

Regression models

From now on, we consider as the daily return of each index on the day t, calculated using the below formula:

where, and are, respectively, the prices of each index on day t and t-1.

To analyze the impact of the occurrence of COVID-19 on each Moroccan sector, we have employed the following models, where model II was inspired from the CAPM model.

Where, and are, respectively, the intercepts of model I and Model II, is a dummy variable that takes value 1 during the COVID-19 period and 0 otherwise. The slope of the model I is denoted by . is the market factor coefficient that reflects the index's systematic risk, and represents the return of the main index of the Moroccan financial market (MASI). is the coefficient of the dummy variable in Model II. The error term is presented by and .

GARCH model

The GARCH model was initiated by Engle [17] and developed in [9]. In this paper, we employed the GARCH (1,1) model as it is the simplest but often highly applicable GARCH process. To visualize conditional volatility in stock returns, Karmakar [27] advised using GARCH (1,1). As in [9], equation of conditional variance for GARCH(1,1) can be written as follows:

Where, is the error obtained from Eq. (5): and ω 1 is ARCH coefficient and ω 2 is GARCH coefficient supposed to be non-negative ( and ), , and . The sum of and reflects model quality. If is close to one the GARCH model considered persistent.

Performance and risk measures

For the variable , the following measures will be considered.

Where, , , and are, respectively, the expected return, standard deviation, and Beta of (see [40] and [44]). represents risk-free rat.

Conditional Value-at-Risk ([37]), at a specific threshold can be defined as the following:

Where, and is the cumulative distribution function of.

Descriptive statistics

The analysis of this research was carried out using a variety of statistical techniques, such as descriptive statistics, which included: mean, standard deviation (SD), maximum (Max), minimum (Min), kurtosis, skewness, and median (Tables 2 and 3 ). The correlation was visualized using a correlation matrix (Tables 12 and 13, in the appendix), to show and to compare interdependence between sectors before and during COVID-19. The Jarque–Bera test was used to confirm the non-normal distribution of returns. Further, stationarity test (Augmented Dickey-Fuller) and the ARCH–LM test (Table 9) which are necessary for the validation of the GARCH (1,1) model.

Table 2

Descriptive statistics before COVID-19.

Sectors	Mean	SD	Max	Min	Kurtosis	Skewness	Median
UT	−0.010	0.917	3.951	−4.526	4.780	−0.097	0.000
ELC	0.013	0.671	2.265	−2.948	1.854	−0.127	0.000
MI	−0.003	0.630	2.766	−2.935	4.582	−0.455	0.000
FD & PR	0.017	0.454	2.884	−1.896	3.473	0.158	0.012
INSU	0.004	0.663	2.912	−3.664	5.468	−0.561	0.007
TELEC	0.003	0.386	2.455	−3.397	15.108	−0.813	0.000
BANK	0.005	0.313	1.308	−1.461	2.502	0.078	0.002
DIST	0.022	0.569	3.104	−3.309	6.613	−0.223	0.004
RE & PR	−0.082	0.837	3.908	−3.938	4.038	−0.122	−0.044
CHEMI	0.060	1.275	3.917	−4.373	1.793	0.115	0.000
TRANS	0.017	0.686	3.807	−4.244	7.899	0.349	0.000
PHARMA	0.007	0.508	2.223	−2.343	8.705	0.312	0.000
Ol & Gz	0.017	0.743	3.183	−3.791	3.940	−0.082	0.000
MS & CS	0.051	0.530	2.960	−2.202	3.713	0.389	0.015
Fy & Pr	−0.020	1.608	4.142	−16.275	13.758	−1.032	0.000
BEV	0.009	0.622	3.312	−3.672	7.023	−0.166	0.000
Tran Sev	0.034	0.593	3.858	−4.611	11.710	−0.269	0.000
HC	0.016	0.849	3.817	−4.086	4.226	−0.040	0.000
Co & BM	−0.006	0.631	2.992	−3.202	3.768	−0.272	−0.001
Lr & Ht	0.023	1.092	4.138	−4.568	3.560	0.068	0.000
IC & OFI	0.006	0.451	1.822	−2.346	4.048	−0.337	0.000
En & EIG	−0.107	0.924	3.078	−4.068	2.439	−0.515	0.000
MASI	0.004	0.247	1.300	−0.908	2.900	0.270	0.002

Table 3

Descriptive statistics during COVID-19.

Sectors	Mean	SD	Max	Min	Kurtuisis	Skewness	Median
UT	−0.058	0.836	2.180	−4.573	2.945	−0.782	0.000
ELC	0.014	0.597	2.135	−3.925	3.251	−0.774	0.000
MI	0.030	0.682	2.646	−3.776	1.605	−0.852	0.044
FD & PR	0.015	0.506	2.329	−3.545	4.131	−1.156	0.002
INSU	0.014	0.449	1.531	−3.693	5.302	−1.871	0.010
TELEC	−0.006	0.470	2.218	−4.380	4.046	−1.753	0.000
BANK	−0.002	0.527	2.851	−4.474	3.178	−1.961	0.020
DIST	0.035	0.530	2.658	−2.789	1.062	−0.479	0.012
RE & PR	0.000	0.910	3.947	−4.525	1.166	−0.181	−0.035
CHEMI	0.034	0.841	1.754	−4.266	1.047	−0.853	0.000
TRANS	−0.019	0.761	2.565	−4.351	1.745	−0.534	0.000
PHARMA	0.118	0.491	1.656	−1.675	15.969	0.112	0.000
Ol & Gz	0.026	0.543	1.695	−2.805	1.172	−0.845	0.009
MS & CS	0.047	0.589	1.996	−4.129	4.872	−1.887	0.017
Fy & Pr	0.020	1.120	2.526	−4.534	3.486	−0.454	0.000
BEV	−0.008	0.613	3.350	−3.784	3.365	−1.127	0.000
Tran Sev	0.026	0.716	2.708	−4.563	0.970	−1.478	0.008
HC	−0.016	0.877	2.793	−4.544	0.745	−0.842	0.000
Co & BM	0.012	0.617	1.847	−4.429	1.714	−1.383	0.033
Lr & Ht	−0.045	0.959	2.256	−4.559	2.297	−0.502	0.000
IC & OFI	−0.011	0.421	1.528	−2.775	7.437	−0.806	0.000
En & EIG	0.107	0.915	2.530	−2.687	2.815	−0.022	0.000
MASI	0.008	0.427	2.304	−4.009	3.999	−2.693	0.016

Table 12

Correlation between sctors before COVID 19.

	UT	ELC	MI	FD & PR	INSU	TELEC	BANK	DIST	RE & PR	CHEMI	TRANS
UT	1.000	−0.075	0.057	0.084	0.114	0.061	0.032	0.148	0.083	0.007	0.028
ELC	−0.075	1.000	0.042	0.098	0.103	0.151	0.147	0.036	0.060	0.048	0.021
MI	0.057	0.042	1.000	0.099	0.068	0.108	0.153	0.087	0.116	0.075	0.031
FD & PR	0.084	0.098	0.099	1.000	0.099	0.162	0.297	0.149	0.190	0.168	0.018
INSU	0.114	0.103	0.068	0.099	1.000	0.098	0.085	0.097	0.100	0.017	0.004
TELEC	0.061	0.151	0.108	0.162	0.098	1.000	0.341	0.106	0.184	0.105	−0.029
BANK	0.032	0.147	0.153	0.297	0.085	0.341	1.000	0.081	0.212	0.170	−0.046
DIST	0.148	0.036	0.087	0.149	0.097	0.106	0.081	1.000	0.132	0.035	−0.029
RE & PR	0.083	0.060	0.116	0.190	0.100	0.184	0.212	0.132	1.000	0.168	0.043
CHEMI	0.007	0.048	0.075	0.168	0.017	0.105	0.170	0.035	0.168	1.000	0.079
TRANS	0.028	0.021	0.031	0.018	0.004	−0.029	−0.046	−0.029	0.043	0.079	1.000
PHARMA	0.004	0.038	0.007	0.031	−0.037	−0.027	−0.008	0.000	0.026	0.057	0.109
Ol & Gz	0.163	0.057	0.125	0.189	0.074	0.069	0.104	0.142	0.157	0.108	−0.032
MS & CS	0.120	0.063	0.112	0.035	0.120	0.106	0.088	0.083	0.122	0.043	0.061
Fy & Pr	0.073	0.019	0.017	0.074	0.051	0.045	0.055	0.009	0.110	0.110	0.002
BEV	0.074	0.033	0.037	0.061	0.103	0.055	0.083	0.054	0.029	0.050	0.060
Tran Sev	0.141	0.036	0.101	0.310	0.079	0.229	0.264	0.121	0.199	0.139	0.000
HC	0.085	0.038	0.126	0.127	0.150	0.110	0.105	0.141	0.143	0.078	0.010
Co & BM	0.077	0.091	0.078	0.178	0.165	0.184	0.248	0.123	0.144	0.073	0.003
Lr & Ht	−0.010	−0.021	0.080	0.111	0.036	0.049	0.089	0.030	0.104	0.083	0.073
IC & OFI	0.104	−0.021	0.099	0.142	0.035	−0.018	0.068	0.044	0.086	0.035	0.046
En & EIG	0.012	−0.074	0.098	0.023	0.020	0.009	0.022	0.033	0.060	0.048	0.020
	PHARMA	Ol & Gz	MS & CS	Fy & Pr	BEV	Tran Sev	HC	Co & BM	Lr & Ht	IC & OFI	En & EIG
UT	0.004	0.163	0.120	0.073	0.074	0.141	0.085	0.077	−0.010	0.104	0.012
ELC	0.038	0.057	0.063	0.019	0.033	0.036	0.038	0.091	−0.021	−0.021	−0.074
MI	0.007	0.125	0.112	0.017	0.037	0.101	0.126	0.078	0.080	0.099	0.098
FD & PR	0.031	0.189	0.035	0.074	0.061	0.310	0.127	0.178	0.111	0.142	0.023
INSU	−0.037	0.074	0.120	0.051	0.103	0.079	0.150	0.165	0.036	0.035	0.020
TELEC	−0.027	0.069	0.106	0.045	0.055	0.229	0.110	0.184	0.049	−0.018	0.009
BANK	−0.008	0.104	0.088	0.055	0.083	0.264	0.105	0.248	0.089	0.068	0.022
DIST	0.000	0.142	0.083	0.009	0.054	0.121	0.141	0.123	0.030	0.044	0.033
RE & PR	0.026	0.157	0.122	0.110	0.029	0.199	0.143	0.144	0.104	0.086	0.060
CHEMI	0.057	0.108	0.043	0.110	0.050	0.139	0.078	0.073	0.083	0.035	0.048
TRANS	0.109	−0.032	0.061	0.002	0.060	0.000	0.010	0.003	0.073	0.046	0.020
PHARMA	1.000	−0.052	0.010	0.021	−0.023	−0.009	−0.001	0.020	−0.031	0.033	−0.052
Ol & Gz	−0.052	1.000	0.089	0.061	0.143	0.187	0.120	0.092	0.061	0.069	0.030
MS & CS	0.010	0.089	1.000	0.047	0.156	0.090	0.089	0.134	0.068	0.053	0.098
Fy & Pr	0.021	0.061	0.047	1.000	0.042	0.029	0.002	0.059	−0.014	−0.047	0.014
BEV	−0.023	0.143	0.156	0.042	1.000	0.099	−0.008	0.106	0.038	0.050	0.051
Tran Sev	−0.009	0.187	0.090	0.029	0.099	1.000	0.081	0.190	0.133	0.055	0.007
HC	−0.001	0.120	0.089	0.002	−0.008	0.081	1.000	0.097	0.105	0.104	0.104
Co & BM	0.020	0.092	0.134	0.059	0.106	0.190	0.097	1.000	0.103	0.049	0.004
Lr & Ht	−0.031	0.061	0.068	−0.014	0.038	0.133	0.105	0.103	1.000	0.027	0.053
IC & OFI	0.033	0.069	0.053	−0.047	0.050	0.055	0.104	0.049	0.027	1.000	0.099
En & EIG	−0.052	0.030	0.098	0.014	0.051	0.007	0.104	0.004	0.053	0.099	1.000

Table 13

Correlation between sctors during COVID 19.

	UT	ELC	MI	FD & PR	INSU	TELEC	BANK	DIST	RE & PR	CHEMI	TRANS
UT	1.000	0.098	0.183	0.234	0.228	0.198	0.233	0.221	0.170	0.173	0.064
ELC	0.098	1.000	0.339	0.350	0.350	0.407	0.444	0.269	0.240	0.227	0.194
MI	0.183	0.339	1.000	0.396	0.308	0.405	0.505	0.295	0.301	0.189	0.297
FD & PR	0.234	0.350	0.396	1.000	0.450	0.574	0.676	0.426	0.425	0.306	0.269
INSU	0.228	0.350	0.308	0.450	1.000	0.491	0.534	0.377	0.284	0.292	0.264
TELEC	0.198	0.407	0.405	0.574	0.491	1.000	0.728	0.397	0.406	0.288	0.282
BANK	0.233	0.444	0.505	0.676	0.534	0.728	1.000	0.517	0.491	0.285	0.387
DIST	0.221	0.269	0.295	0.426	0.377	0.397	0.517	1.000	0.330	0.238	0.210
RE & PR	0.170	0.240	0.301	0.425	0.284	0.406	0.491	0.330	1.000	0.260	0.313
CHEMI	0.173	0.227	0.189	0.306	0.292	0.288	0.285	0.238	0.260	1.000	0.100
TRANS	0.064	0.194	0.297	0.269	0.264	0.282	0.387	0.210	0.313	0.100	1.000
PHARMA	0.009	0.012	0.001	0.040	−0.012	0.024	0.016	0.053	0.038	−0.033	0.003
Ol & Gz	0.159	0.258	0.310	0.363	0.383	0.328	0.440	0.295	0.255	0.163	0.250
MS & CS	0.273	0.339	0.403	0.534	0.440	0.518	0.581	0.435	0.413	0.293	0.343
Fy & Pr	0.114	0.100	0.061	0.120	0.158	0.133	0.116	0.095	0.198	0.166	0.016
BEV	0.214	0.240	0.311	0.397	0.300	0.354	0.470	0.353	0.279	0.240	0.285
Tran Sev	0.272	0.417	0.432	0.592	0.449	0.646	0.697	0.418	0.415	0.228	0.283
HC	0.208	0.271	0.263	0.349	0.324	0.353	0.429	0.230	0.240	0.240	0.197
Co & BM	0.192	0.355	0.437	0.599	0.484	0.591	0.720	0.420	0.432	0.221	0.331
Lr & Ht	0.143	0.164	0.162	0.229	0.191	0.171	0.197	0.116	0.231	0.201	0.207
IC & OFI	0.105	0.132	0.227	0.279	0.226	0.262	0.336	0.197	0.223	0.159	0.122
En & EIG	0.043	0.033	0.081	0.074	0.118	0.070	0.117	0.013	0.025	0.032	−0.015
	PHARMA	Ol & Gz	MS & CS	Fy & Pr	BEV	Tran Sev	HC	Co & BM	Lr & Ht	IC & OFI	En & EIG
UT	0.009	0.159	0.273	0.114	0.214	0.272	0.208	0.192	0.143	0.105	0.043
ELC	0.012	0.258	0.339	0.100	0.240	0.417	0.271	0.355	0.164	0.132	0.033
MI	0.001	0.310	0.403	0.061	0.311	0.432	0.263	0.437	0.162	0.227	0.081
FD & PR	0.040	0.363	0.534	0.120	0.397	0.592	0.349	0.599	0.229	0.279	0.074
INSU	−0.012	0.383	0.440	0.158	0.300	0.449	0.324	0.484	0.191	0.226	0.118
TELEC	0.024	0.328	0.518	0.133	0.354	0.646	0.353	0.591	0.171	0.262	0.070
BANK	0.016	0.440	0.581	0.116	0.470	0.697	0.429	0.720	0.197	0.336	0.117
DIST	0.053	0.295	0.435	0.095	0.353	0.418	0.230	0.420	0.116	0.197	0.013
RE & PR	0.038	0.255	0.413	0.198	0.279	0.415	0.240	0.432	0.231	0.223	0.025
CHEMI	−0.033	0.163	0.293	0.166	0.240	0.228	0.240	0.221	0.201	0.159	0.032
TRANS	0.003	0.250	0.343	0.016	0.285	0.283	0.197	0.331	0.207	0.122	−0.015
PHARMA	1.000	−0.042	0.018	−0.051	0.036	0.027	−0.026	0.034	0.038	−0.019	−0.054
Ol & Gz	−0.042	1.000	0.395	0.104	0.266	0.347	0.234	0.376	0.177	0.187	0.113
MS & CS	0.018	0.395	1.000	0.117	0.414	0.525	0.378	0.530	0.217	0.272	0.032
Fy & Pr	−0.051	0.104	0.117	1.000	0.075	0.066	0.073	0.098	0.116	0.132	0.171
BEV	0.036	0.266	0.414	0.075	1.000	0.354	0.255	0.381	0.245	0.280	0.059
Tran Sev	0.027	0.347	0.525	0.066	0.354	1.000	0.387	0.605	0.151	0.268	0.087
HC	−0.026	0.234	0.378	0.073	0.255	0.387	1.000	0.368	0.100	0.191	0.036
Co & BM	0.034	0.376	0.530	0.098	0.381	0.605	0.368	1.000	0.217	0.271	0.100
Lr & Ht	0.038	0.177	0.217	0.116	0.245	0.151	0.100	0.217	1.000	0.139	0.055
IC & OFI	−0.019	0.187	0.272	0.132	0.280	0.268	0.191	0.271	0.139	1.000	0.087
En & EIG	−0.054	0.113	0.032	0.171	0.059	0.087	0.036	0.100	0.055	0.087	1.000

Table 9

Test statistic of return.

Stationarity test (Dickey-Fuller)			ARCH–LM test		Normality test (Jarque-Bera)
	Statistic	P.value	X-squared	P.value	X-squared	P.value
UT	−13.61	0.01	19.496	0.00	1094.47	0.00
ELC	−12.03	0.01	24.739	0.00	588.71	0.00
MI	−8.55	0.01	49.455	0.00	1210.60	0.00
FD & PR	−10.49	0.01	27.47	0.00	2662.13	0.00
INSU	−10.48	0.01	67.334	0.00	2946.24	0.00
TELEC	−11.13	0.01	36.038	0.00	19,485.85	0.00
BANK	−10.38	0.01	53.358	0.00	23,678.20	0.00
DIST	−11.33	0.01	52.669	0.00	2015.29	0.00
RE & PR	−10.34	0.01	91.941	0.00	523.79	0.00
CHEMI	−11.32	0.01	163.13	0.00	312.44	0.00
TRANS	−10.98	0.01	19.663	0.00	1780.19	0.00
PHARMA	−10.87	0.01	28.536	0.00	2277.38	0.00
Ol & Gz	−10.40	0.01	18.007	0.00	1018.45	0.00
MS & CS	−10.36	0.01	41.73	0.00	4091.65	0.00
FY & PR	−11.47	0.01	12.941	0.00	9655.80	0.00
BEV	−11.60	0.01	5.6348	0.02	3587.96	0.00
Tran Sev	−9.28	0.01	59.703	0.00	6607.70	0.00
HC	−11.91	0.01	93.027	0.00	818.75	0.00
Co & BM	−10.61	0.01	55.407	0.00	1660.17	0.00
Lr & Ht	−10.67	0.01	144.43	0.00	547.81	0.00
IC & OFI	−10.30	0.01	9.1489	0.00	1002.53	0.00
En & EIG	−8.53	0.01	47.88	0.00	172.16	0.00

Empirical results and discussion

Summary statistics

As noted previously, the data is divided into two sections: the COVID-19 period and the ordinary period preceding COVID-19. Since the pandemic in Morocco started on March 2, 2020, we considered the COVID period for this study to be defined from March 2, 2020 to December 31, 2021. Table 2 reports the summary statistics of each sector index before COVID-19. The results show that the chemicals sector provides a higher return of 0.06% with an important standard deviation of 1.275%. In contrast, the engineering & equipment industrial goods sector earns a lower return of −0.107% with a standard deviation of 0.924%. Further, the forestry & paper sector shows a higher standard deviation of 1.608% with a low return of 0.02%.

Table 3 highlights the summary statistics of each sector index during the COVID-19 period. The results indicated that the pharmaceutical industry sector earned the highest return with a relatively medium standard deviation (0.491%). Furthermore, the banking, transport, beverages, telecommunication, holding companies, leisures & hotels, investment companies & other finance sectors yielded negative returns after positive ones before COVID-19. Whereas, the forestry & paper, mining, construction & building materials, and engineering & equipment industrial goods sectors showed positive returns during the COVID-19 period, following negative ones before COVID-19.

Furthermore, based on the results, we suggest that the pharmaceutical industry and the engineering & equipment industrial goods sectors become very important investment areas because they experience a higher return after the pandemic onset. Based on the variation of average returns (Table 4 ), we can clearly identify that a particular improvement was realized by the pharmaceutical industry sector (from 0.007 before COVID-19 to 0.118 during COVID-19) and the engineering & equipment industrial goods sectors (from −0.107 before COVID-19 to 0.107 during COVID-19). This leads us to assume that the onset of COVID-19 significantly benefited these two sectors. This suggests that the pharmaceutical industry sector and the engineering & equipment industrial goods sector will become attractive investment sectors during the sanitary crisis for Moroccan investors. This point can also be supported in subsection 4.3, as these sectors combine increased performance with decreased risk. Tables 2 and 3 reveals an interesting observation that the average return of MASI was increased from 0.004% before COVID-19 to 0.008% during the COVID-19 period, which can be considered an unexpected finding for an unfamiliar reader, but this can be explained by AMMC's effort to reduce the effects of the pandemic in the Moroccan financial market and the positive rating given to the Moroccan financial market by the European Financial Markets Authority [8]. Also, it can be understood by the heavily increasing returns of the two recommended sectors: the pharmaceutical industry sector, and the engineering & equipment industrial goods sector. We note that for most of the series during two periods, the mean differs from the median, the asymmetry coefficient (skewness) is different from zero, and the kurtosis coefficient is greater than 3. These results indicate that the majority of returns are not normally distributed. The Jarque-Bera test in Table 9 confirms the suggestion of the non-normality distribution of returns.

Table 4

Average return before and during COVID-19. Average return (%).

Sample 1				Sample 2
Sectors	Before COVID-19	During COVID-19	Variation‡	Sectors	Before COVID-19	During COVID-19	Variation‡
Lr & Ht	0.023	−0.045	−6.742	ELC	0.013	0.014	0.164
UT	−0.010	−0.058	−4.854	Ol & Gz	0.017	0.026	0.917
TRANS	0.017	−0.019	−3.553	INSU	0.004	0.014	0.989
HC	0.016	−0.016	−3.208	DIST	0.022	0.035	1.258
CHEMI	0.060	0.034	−2.626	Co & BM	−0.006	0.012	1.810
IC & OFI	0.006	−0.011	−1.708	MI	−0.003	0.030	3.308
BEV	0.009	−0.008	−1.684	Fy & Pr	−0.020	0.020	3.959
TELEC	0.003	−0.006	−0.973	RE & PR	−0.082	0.000	8.200
Tran Sev	0.034	0.026	−0.812	PHARMA	0.007	0.118	11.062
BANK	0.005	−0.002	−0.697	En & EIG	−0.107	0.107	21.381
MS & CS	0.051	0.047	−0.433
FD & PR	0.017	0.015	−0.205

Variation = (Average return (During COVID-19)− Average return (Before COVID-19))*100.

According to Tables 2 and 3, we may classify sectors into two samples: the first one, referred to as sample 1, includes industries with a decreasing average return during COVID-19, and the second one, referred to as sample 2, includes industries with an increasing average return during COVID-19.

The first conclusion that can be drawn from Table 4 is that all sectors categorized in sample 1 exhibit a negative variation factor, whereas all sectors classified in sample 2 show a positive variation factor. For specifically, the variation factor in Table 4 indicates that leisures & hotels (−6.742), utilities (−4.854), transport (−3.553), and holding companies (−3.208) are the sectors worst affected by COVID-19. On the other hand, Table 4 also confirms that engineering and equipment industrial goods (21.381), pharmaceutical industry (11.062), and real estate participation and promotion (8.20) are the sectors most benefited from the pandemic.

Further, Tables 12 and 13 show that the correlation between returns was significantly increased during the COVID-19 period. For example, the largest correlation coefficient before COVID-19 was 31.10% between banks and telecommunications, but during COVID-19 the correlation coefficient between these sectors became much higher: 72.28%. This may be interpreted as an indication of a high degree of interconnectedness between the sectors in each of these samples during COVID-19, as found in [10] (see also [38] for more details about the interconnectedness between equity sectors during COVID-19). Consequently, investors would have fewer choices for diversification and would be exposed to more risk [3].

Responses of sectors to the spreading of COVID-19 in Morocco

Model I: the responses to the onset of COVID-19

The impact of COVID-19 on the returns of the selected sectors is examined using the regression model (Model I). Columns three and six in Table 5 provides the estimation results for the relationship between the presence of COVID-19 in Morocco and the return of each sector from both samples. We can confirm that the onset of COVID-19 in Morocco has a negative, but statistically insignificant, impact on the returns of sample 1. Indeed, the results show that every sector that was included in sample 1 has a negative coefficient factor of dummy variable (statistically insignificant): leisure & hotels (−0.067), utilities (−0.049), transport (−0.036), holding companies (−0.032), chemicals (−0.026), investment companies & other finance (−0.017), beverages (−0.017), telecommunications (−0.010), transportation services (−0.008), banks (−0.007), materials & software & computer services (−0.004), and food producers & processors (−0.002). On the other hand, each sector in sample 2 has a positive coefficient factor of dummy variable: engineering & equipment industrial goods (0.214), pharmaceutical industry (0.111), real estate participation & promotion (0.082), forestry & paper (0.040), mining (0.033), construction & building materials (0.018), distributors (0.013), insurance (0.01), and oil & gas (0.009) (for the Chinese and US markets see [7,32]).

Table 5

COIVD-19 and return of each sector indix using Model I. Model I: X = α1 + γ1 + є1.

	Sample 1			Sample 2
Sectors	Intercepte	COVID-19	Sectors	Intercepte	COVID-19
Lr & Ht	0.023	−0.067	ELC	0.013	0.002
UT	−0.010	−0.049	Ol & Gz	0.017	0.009
TRANS	0.017	−0.036	INSU	0.004	0.010
HC	0.016	−0.032	DIST	0.022	0.013
CHEMI	0.060	−0.026	Co & BM	−0.006	0.018
BEV	0.009	−0.017	MI	−0.003	0.033
IC & OFI	0.006	−0.017	FY & PR	−0.020	0.040
TELEC	0.003	−0.010	RE & PR	−0.082**	0.082
Tran Sev	0.034	−0.008	PHARMA	0.007	0.111***
BANK	0.005	−0.007	En & EIG	−0.107**	0.214***
MS & CS	0.051	−0.004
FD & PR	0.017	−0.002

Note: +, *, **, and *** indicate significance at the 10%, 5%, 1%, and 0.1% levels respectively.

Model II: the responses to the market and the onset of COVID-19

For Model II, dummy variable in Table 6 reports the same information as the model I (Table 5) the only exception is the electricity sector, with such a tiny negative non-significant coefficient factor of dummy variable (−0.001). Further, for model II (in Table 6) one sector from sample 1 shows a negative intercept which is utilities sector (−0.010), but for sample II, five sectors show a negative intercept: mining (−0.003), construction & building materials (−0.006), forestry & paper (−0.02), real estate participation & promotion (−0.082), and engineering & equipment industrial goods (−0.107). Additionally, we observe a positive, highly significant, market factor coefficient for all sectors from both samples. The only exception here is the pharmaceutical industry, which shows a lower market factor coefficient (0.027) but a highly significant coefficient factor of dummy variable (0.111).

Table 6

COIVD-19 and return of each sector indix using Model II. Model II: X = α2 + β + γ2 + є2.

Sample 1				Sample 2
Sectors	Intercepte	MASI	COVID-19	Sectors	Intercepte	MASI	COVID-19
Lr & Ht	0.020	0.596***	−0.070	ELC	0.010	0.683***	−0.001
UT	−0.012	0.558***	−0.051	Ol & Gz	0.014	0.727***	0.006
TRANS	0.015	0.444***	−0.037	INSU	0.001	0.712***	0.007
HC	0.013	0.873***	−0.035	DIST	0.020	0.664***	0.010
CHEMI	0.057	0.852***	−0.029	Co & BM	−0.011	1.346***	0.013
BEV	0.007	0.636***	−0.019	MI	−0.006	0.805***	0.030
IC & OFI	0.005	0.299***	−0.018	Fy & Pr	−0.022	0.483***	0.038
TELEC	0.000	0.923***	−0.013	RE & PR	−0.086**	1.205***	0.077+
Tran Sev	0.030	1.208***	−0.013	PHARMA	0.007	0.027	0.111***
BANK	0.001	1.095***	−0.011	En & EIG	−0.108**	0.208**	0.213***
MS & CS	0.048**	0.752***	−0.007
FD & PR	0.014	0.885***	−0.005

Note: +, *, **, and *** indicate significance at the 10%, 5%, 1%, and 0.1% levels respectively.

Performance and risk before and during COVID-19

Performance before and during COVID-19

Based on Table 7 , all sectors included in sample 1 show a decrease in the Sharp ratio during the COVID-19 period compared to the period before COVID-19. This means that COVID-19 has negatively impacted the performance of sample 1 [41,46,48]. However, when compared to the period preceding COVID-19, all sectors in sample 2 had a high Sharp ratio during COVID-19, which reveals that, at least in terms of performance, the COVID-19 period benefited sample 2. This can be seen in the same context as the finding of [24,35]. Indeed, we remark (Table 7) that all industries of sample 1 show a negative Sharp variation, whereas all sectors in sample 2 show a positive Sharp variation (Table 8 ). It is also important to highlight that engineering & equipment industrial goods (0.232) and the pharmaceutical industry (0.226) have made significant progress based on the Sharp variation. In contrast, the lowest Sharp variations belong to sectors from sample 1: leisures & hotels (−0.067), utilities (−0.056), and transport (−0.049). Therefore, the results of Table 7 suggest that, during COVID-19, the performance of sectors included in sample 2 was better than sectors included in sample 2. Furthermore, sample 2 shows a high Sharp variation, which means the importance of those sec‑ tors during pandemic periods on the Moroccan market. Based on Table 8, the minor Treynor variation is observed in sample 1 for leisure & hotels (−0.115) and utilities (−0.092) . The pharmaceutical industry has the highest Treynor variation (2.171), followed by engineering and industrial goods (1.159). We note that most of the sectors in sample 1 have a negative Treynor variation. The only exception is the transport sector, as it has a very low negative Beta of the market (Table 7) before COVID-19. On the contrary, the sectors in sample 2 have positive Treynor variation, which indicates that the pandemic has favorably impacted sample 2.

Table 7

Performpnce and risk of sample 1 before and during COVID-19.

Sharp ratio (%)				Treynor ratio (%)			CVaR(%)		Beta
Sectors	Before	During	Sharp Variation ‡‡	Before	During	Treynor Variation ‡	Before	During	Before	During
Lr & Ht	0.021	−0.047	−0.067	0.034	−0.081	−0.115	0.041	0.037	0.675	0.549
UT	−0.011	−0.070	−0.059	−0.016	−0.108	−0.092	3.473	3.545	0.591	0.538
TRANS	0.025	−0.025	−0.049	−0.612	−0.026	0.586	2.558	2.807	−0.028	0.712
HC	0.019	−0.018	−0.037	0.022	−0.017	−0.039	0.033	0.037	0.739	0.948
CHEMI	0.047	0.040	−0.007	0.051	0.051	0.000	4.215	3.268	1.178	0.663
IC & OFI	0.013	−0.026	−0.040	0.029	−0.032	−0.060	0.018	0.017	0.209	0.350
BEV	0.015	−0.012	−0.027	0.019	−0.011	−0.030	2.533	3.024	0.484	0.721
TELEC	0.009	−0.014	−0.022	0.003	−0.007	−0.011	1.888	2.439	0.944	0.908
Tran Sev	0.058	0.037	−0.021	0.033	0.020	−0.013	2.545	3.633	1.046	1.297
BANK	0.016	−0.004	−0.020	0.005	−0.002	−0.007	1.103	2.842	0.962	1.168
MS & CS	0.096	0.079	−0.017	0.111	0.051	−0.061	1.730	3.111	0.457	0.918
FD & PR	0.037	0.030	−0.008	0.020	0.017	−0.004	1.512	2.357	0.847	0.904

Sharp Variation = Sharp ratio (During During COVID-19)− Sharp ratio (Before Before COVID-19).

Treynor Variation = Treynor ratio (During COVID-19)− Treynor ratio (Before COVID-19).

Table 8

Performpnce and risk of sample 2 before and during COVID-19.

Sharp ratio (%)				Treynor ratio (%)			CVaR (%)		Beta
Sectors	Before	During	Sharp Variation	Before	During	Treynor Variation	Before	During	Before	During
ELC	0.019	0.024	0.005	0.020	0.021	0.001	2.265	2.493	0.652	0.699
Ol & Gz	0.023	0.049	0.025	0.019	0.042	0.022	2.751	2.126	0.886	0.634
INSU	0.006	0.031	0.025	0.005	0.022	0.017	2.824	2.398	0.829	0.644
DIST	0.040	0.066	0.027	0.038	0.050	0.012	2.421	2.113	0.591	0.704
Co & BM	−0.010	0.020	0.029	−0.004	0.010	0.014	0.025	0.028	1.612	1.189
MI	−0.004	0.044	0.049	−0.004	0.034	0.038	2.718	2.849	0.657	0.887
FY & PR	−0.012	0.017	0.030	−0.030	0.052	0.082	5.674	3.419	0.664	0.378
RE & PR	−0.097	0.000	0.098	−0.062	0.000	0.063	3.256	2.941	1.309	1.141
PHARMA	0.014	0.240	0.226	0.900	3.071	2.171	2.094	1.624	0.008	0.038
En & EIG	−0.116	0.117	0.232	−0.717	0.442	1.159	0.034	0.025	0.149	0.241

Remark 1. Some studies suggest that the performance of the real estate participation & promotion sector was negative during the COVID-19 period [32], but these results are only valid for a short period from the beginning of the pandemic. The increase in real estate participation & promotion can be explained by the Moroccan government's decision to lower interest rates, which could encourage households to borrow from banks to buy houses and buildings instead of renting (for example, we refer the reader to Bouri et al. (2021(b)) [11] for the effect of New Zealand government policies on the real estate sector).

Risk before and during COVID-19

Tables 7 and 8 describe the risk, based on the CVaR at a threshold α = 5%, during the two periods. Firstly, we can observe that the CVaR was not significantly impacted by COVID-19. Also, Table 7 revealed that the smallest CVaR before COVID-19 belonged to the investment companies & other finance sector (0.018), but after the collapse of COVID-19, the engineering & equipment industrial goods sector marked the very interesting CVaR (0.025) among all sectors. Therefore, this sector can be seen as a safe haven against risk during the COVID-19 crisis, which is analogous to the behavior of bitcoin and US Treasury bonds in the finding of Le et al.(2021) [30]. The CVaR of the majority of the sectors in sample 2 decreased, with the notable exceptions of the electricity and construction & building materials sectors. On the contrary, most of the sectors listed in sample 1 exhibit a higher CVaR during COVID-19 than before COVID-19 (Table 7). The notable exception is the chemical sector, which significantly reduced its CVaR (from 4.125 before COVID-19 to 3.268 during COVID-19).

It is interesting to see the summary of results (Sharp ratio, Tryenor ratio, and CVaR) across sectors. This allows us to more classify the sectors as follows: Sectors where performance is improved and risk is reduced: oil and gas (Sharp ratio: from 0.023 to 0.049; Treynor ratio: from 0.019 to 0.042; CVaR: from 2.751 to 2.126), insurance (Sharp ratio: from 0.006 to 0.031; Treynor ratio: from 0.005 to 0.022; CVaR: from 2.824 to 2.398), distributors (Sharp ratio: from 0.04 to 0.066; Treynor ratio: from 0.038 to 0.050; CVaR: from 2.421 to 2.113), forestry & paper (Sharp ratio: from −0.012 to 0.017; Treynor ratio: from −0.03 to 0.052, CVaR: from 5.674 to 3.419), real estate participation & promotion (Sharp ratio: from −0.097 to 0.000; Treynor ratio: from −0.062 to 0.00; CVaR: from 3.256 to 2.941), pharmaceutical industry (Sharp ratio: from 0.014 to 0.240; Treynor ratio: from 0.900 to 3.071; CVaR: from 2.094 to 1.624), and engineering & equipment industrial goods (Sharp ratio: from −0.116 to 0.117; Treynor ratio: from −0.717 to 0.442; CVaR: from 0.034 to 0.25). Sectors where performance is improved, but risk is raised: electricity (Sharp ratio: from 0.019 to 0.024; Treynor ratio: from 0.020 to 0.021; CVaR: from 2.265 to 2.493), construction & building materials (Sharp ratio: from −0.01 to 0.02; Treynor ratio from: −0.004 to 0.01; CVaR: from 0.025 to 0.028), mining (Sharp ratio: from −0.004 to 0.044; Treynor ratio: from −0.004 to 0.034, CVaR from 2.718 to 2.849). Sectors where performance is decreased but risk is reduced: leisures & hotels (Sharp ratio: from 0.021 to −0.047; Treynore ratio: from 0.034 to −0.081; CVaR: from 0.041 to 0.037), chemicals (CVaR: from 4.215 to 3.268; Sharp ratio: from 0.047 to 0.04; Treynore ratio: from 0.051 to 0.051), and investment companies & other finance (Sharp ratio: from 0.013 to −0.026; Treynor ratio: from 0.029 to −0.032; CVaR: from 0.018 to 0.017).

Sectors where performance is decreased and risk is increased: utilities (Sharp ratio: from −0.01 to −0.07; Treynor ratio: from 0.016 to −0.108; CVaR: from 3.473 to 3.545), holding companies (Sharp ratio: from 0.019 to −0.018; Treynor ratio: from 0.022 to −0.017; CVaR: from 0.033 to 0.037), beverages (Sharp ratio: from 0.015 to −0.012; Treynor ratio: from 0.019 to −0.011; CVaR: from 2.533 to 3.024), telecommunications (Sharp ratio: from 0.009 to −0.014; Treynor ratio from: 0.003 to −0.007; CVaR: from 1.888 to 2.439), transportation services (Sharp ratio: from 0.058 to 0.037; Treynore ratio: from 0.033 to 0.020; CVaR: from 2.545 to 3.633), bank (Sharp ratio: from 0.016 to −0.004; Treynore ratio: from 0.005 to −0.002; CVaR: from 1.103 to 2.842), materials, software & computer services (Sharp ratio: from 0.096 to 0.079; Tryenor ratio: from 0.111 to 0.051; CVaR: from 1.730 to 3.111), and food producers & processors (Sharp ratio: from 0.037 to 0.030; Treynore ratio from 0.02 to 0.017; CVaR: from 1.512 to 2.357).

Impact of the COVID-19 on the volatility

Tests statistic

Before estimating the impact of COVID-19 on the volatility of sectors, the stationarity test (Augmented Dickey-Fuller) and the ARCH-LM test are applied in Table 9 to ensure both the stationarity and the presence of the ARCH effect, as they are two important tests for the application of the GARCH model.

According to Table 9 we see that all P.values (Dickey-Fuller) are less than 5%, then we can confirm that every return series is stationary. The null hypothesis of no ARCH effect is rejected since the probability values of the ARCH-LM test are also significant.

Conditional volatility overall the period

In this section, using the GARCH (1,1) model, we evaluate the volatility of each sector index during the COVID-19 period (since the necessary tests for this model are verified in Table 9). This estimation aims to show and compare the volatility of sectors included in samples 1 and 2 before and during COVID-19. Table 10 shows the estimated model's results, which reveal that the majority of GARCH and ARCH coefficients are highly significant, confirming the model's predicting abilities. Furthermore, the sum of the GARCH and ARCH coefficients is less than one, suggesting that the volatility is persistent.

Table 10

Test statistic of return.

Volatility using GARCH(1,1)
Sample 1				Sample 2
Sectors	Intercepte	ARCH	GARCH	Sectors	Intercepte	ARCH	GARCH
Lr & Ht	0.923***	0.168***	0.000	ELC	0.035***	0.114***	0.799***
UT	0.075***	0.057***	0.845***	Ol & Gz	0.010***	0.088***	0.894***
TRANS	0.043***	0.080***	0.834***	INSU	0.036***	0.158***	0.745**
HC	0.133***	0.178***	0.631***	DIST	0.057***	0.176***	0.645***
CHEMI	0.111***	0.222***	0.697***	Co & BM	0.070***	0.231***	0.590***
IC & OFI	0.040***	0.162***	0.638***	MI	0.044***	0.132***	0.766***
BEV	0.013***	0.033***	0.033***	Fy & Pr	1.848***	0.176***	0.000
TELEC	0.007***	0.092***	0.873***	RE & PR	0.064***	0.225***	0.700***
Tran Sev	0.093***	0.319***	0.462***	PHARMA	0.033***	0.118***	0.755***
BANK	0.011***	0.121***	0.792***	En & EIG	0.027***	0.102***	0.870***
MS & CS	0.052***	0.235***	0.599***
FD & PR	0.063***	0.274***	0.429***

Note: +, *, **, and *** indicate significance at the 10%, 5%, 1%, and 0.1% levels respectively.

Many researchers ([1,5,26,36]) have pointed out that COVID-19 enhanced stock market volatility since it's an uncertain incident that can raise investors’ risk aversion [13,42]. Also, as previously stated, in [43], sectors with positive profitability during COVID-19 are less volatile than sectors with lower returns during COVID-19. In this context, we evaluate the influence of COVID-19 on the volatility of the chosen industries from January 2017 to December 2021. Fig. 3 highlights the conditional variance before and during COVID-19 for all sectors in sample 1. The first observation we can provide is that the volatility of all sectors was significantly boosted by the start of COVID-19 in Morocco in March 2020.

Fig. 3

Conditional variance before and during COVID-19 for sample 1.

Excluding the chemicals industry (9.1% in January 2017), all sectors in sample 1 have never seen such a magnitude of volatility since January 2017. The most volatile sectors of sample 1, as illustrated in Table 11 , are: transportation services (8.3%), chemicals (8.1%), leisures & hotels (7.5%), and materials, software & computer services (6%) during March 2020. Fig. 4 illustrates the conditional variance before and during COVID-19, including all sectors in sample 2. The first conclusion we can make is that the initiation of COVID-19 in Morocco has significantly enhanced the volatility of all industries. Since 2017, sample 2′s sectors have never witnessed similar extreme volatility, except for the forestry & paper (45% in October 2017 and 8% in March 2020) and pharmaceutical industries (1.3% in January 2020, 8% in August 2020). After the collapse of COVID-19, sectors most volatile, as indicated in Table 11, of this sample are forestry & paper (8%), construction & building materials (5.9%), and engineering & equipment industrial goods (4%).

Table 11

Highest volatility before and during COVID-19.

Highest volatility (%)
Sample 1					Sample 2
Before COVID-19			During COVID-19		Before COVID-19			During COVID-19
Sectors	Date	Volatility	Date	Volatility	Sectors	Date	Volatility	Date	Volatility
Lr & Ht	Sep-17	6.80	Mar-20	7.50	ELC	June-18	1.50	Mar-20	3.70
UT	Jan-17	3.50	Mar-20	3.20	Ol & Gz	Apr-19	2.35	Mar-20	1.85
TRANS	Sep-17	2.48	Mar-20	2.90	INSU	Jun-08	2.40	Mar-20	3.70
HC	Sep-18	3.80	Mar-20	5.30	DIST	Sep-19	2.20	Mar-20	2.35
CHEMI	Jan-17	9.10	Mar-20	8.10	Co & BM	May-17	3.10	Mar-20	5.90
IC & OFI	Sep-18	1.15	Mar-20	1.45	MI	Apr-18	2.00	Mar-20	4.10
BEV	Jun-18	0.80	Mar-20	1.40	FY & PR	Oct-17	45.00	Mar-20	8.00
TELEC	Jun-19	0.80	Mar-20	2.75	RE & PR	Oct-19	2.15	Mar-20	2.35
Tran Sev	May-18	6.30	Mar-20	8.30	PHARMA	Jan-19	1.30	Aug-21	1.00
BANK	Jan-17	0.75	Mar-20	3.90	En & EIG	Jun-19	3.00	Oct-21	4.00
MS & CS	Dec-18	2.30	Mar-20	6.00
FD & PR	Jan-17	3.20	Mar-20	4.20

Fig. 4

Conditional variance before and during COVID-19 for sample 2.

For our study, results illustrated by Figs. 3 and 4, and Table 11 show that COVID-19 has boosted the volatility of all sectors but for a short-term [24]. Indeed, we can observe that in March 2020, which is equivalent to the confinement period, the volatility increased significantly. The lockdown that Morocco implemented has made the sectors more volatile, suggesting that governmental policies, such as those of the New Zealand government, which are the subject of Bouri et al. (2021b) [11], also has an effect on how the sectors behave. In fact, the evolution of MASI clarifies the considerable volatility effect in Fig. 2, which exhibits a drop in March 2020. In addition, the volatility behaviors of each sector have changed after the onset of COVID-19. We additionally notice that the number of sectors classified in sample 1 is also greater than the number of sectors classified in sample 2, suggesting that good volatility shocks could be dominated by bad volatility, as noticed by Shahzad et al. (2021 (b)) in [39], for the Chinese stock market.

The results also confirm that, on average, the volatility or risk of sample 2 is less even during the pandemic period. Consequently, we suggest investors incorporate sectors of sample 2, based on the recommended sectors, into their portfolios in order to protect their investments throughout the pandemic crisis.

Conclusion

The first case of COVID-19 in Morocco was reported by Moroccan authorities on March 2, 2020. This paper focuses on determining the impact of this pandemic on Moroccan sectoral stocks indices by analyzing each index over the period from January 2017 to December 2021. As highlighted in the literature, the pandemic could have a positive or negative impact depending on the sector. We first suggested, based on descriptive statistics, before the onset of COVID-19 in Morocco (from January 1, 2017 to March 1, 2020) and during COVID-19 (from March 2, 2020 to December 31, 2021) that sectors could be classified into two main samples. Sample 1: leisures & hotels, utilities, transport, chemicals, investment companies & other finance, beverages, transportation services, banks, materials & software & computer services, and food producers & processors (classified from the most affected to the less affected). And sample 2: electricity oil & gas, insurance, construction & building materials, mining, forestry & paper, real estate participation & promotion, pharmaceuticals industry, and engineering & equipment industrial goods (classified from the less affected to the most affected). Secondly, we used two regression models (Model I and Model II). The results confirmed that COVID- 19 has a negative impact on sample 1, even though it is not statistically significant (as we reported in Tables 7 and 8), sample 2 was, on the other hand, positively impacted and highly significant for the pharmaceutical industry and engineering & equipment industrial goods.

Thirdly, based on Sharp ratio, Treynor ratio, CVaR, and Beta, we found that sample 1 is better than sample 2 in terms of performance and risk during COVID-19. Fourthly, using the GARCH (1,1) model, we illustrated the conditional variance of each sector overall in the period from January 2017 to December 2021 (Figs. 3 and 4). Results show that COVID-19 has increased the volatility of all sectors, but only for a short period. This conclusion is in line with the latest studies ([26,36,43]). Additionally, we note that the magnitude of the volatility of sample 2 is less than that of sample 1. Finally, we recommend sectors of sample 2 during a pandemic crisis in order to protect investors’ investment.

Generally, our findings provide additional helpful information to policymakers and investors concerning risk management, sectoral variety, and optimal asset allocation. In fact, investors may be advised and given suggestions by fund managers, portfolio managers, stockbrokers, and investment advisors to restructure their portfolios by including sectors that can benefit from outbreak periods. Therefore, the risk will be diversified for a given expected return of the stocks. The COVID-19 period does not occur under regular circumstances, which implies that the presence of rare events cannot be ignored. Consequently, we encourage future work to take into consideration the fact that the epidemic period has specific properties that may improve the relevance of the given results. The degree of connectivity between sector returns should be investigated during COVID-19 to make the results more meaningful. Therefore, we recommend the use of dynamic conditional correlation (DCC-GARCH) in future work.

Declaration of Competing Interest

None.