COVID-19 pandemic and Romanian stock market volatility: A GARCH approach

Gherghina, Ștefan Cristian; Armeanu, Daniel Ștefan; Joldeș, Camelia Cătălina Article COVID-19 pandemic and Romanian stock market volatility: A GARCH approach Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Gherghina, Ștefan Cristian; Armeanu, Daniel Ștefan; Joldeș, Camelia Cătălina (2021) : COVID-19 pandemic and Romanian stock market volatility: A GARCH approach, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 8, pp. 1-29, https://doi.org/10.3390/jrfm14080341 This Version is available at: https://hdl.handle.net/10419/258445 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach S ,tefan Cristian Gherghina * , Daniel S ,tefan Armeanu and Camelia Cătălina Joldes ,   Citation: Gherghina, S ,tefan Cristian, Daniel S ,tefan Armeanu, and Camelia Cătălina Joldes ,. 2021. COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach. Journal of Risk and Financial Management 14: 341. https:// doi.org/10.3390/jrfm14080341 Academic Editor: Robert Brooks Received: 18 June 2021 Accepted: 16 July 2021 Published: 22 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania; [email protected] (D.S ,.A.); [email protected] (C.C.J.) *Correspondence: [email protected]; Tel.:+40-741-140-737 Abstract: This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest Exchange Trading (BET) index, along with twelve companies traded on BSE. The quantitative investigation was performed using GARCH approach. In the survey, the GARCH model (1,1) was applied to explore the volatility of the BET and BSE traded shares. Conditional volatility for the daily return series showed noticeable evidence of volatility that shifts over the explored period. In the first quarter of 2020, the Romanian equity market volatility increased to a level very close to that recorded during the global financial crisis of 2007–2009. Over the next two quarters, volatility had a downward trend. Besides, after VAR estimation, no causal connection was found among the COVID-19 variables and the BET index. Keywords: Romanian stock market; volatility clustering; autocorrelation; COVID-19; GARCH models; vector autoregression model; Granger causality 1. Introduction The coronavirus malady (COVID-19) is a sanitary and economic turning point that has harmed the basis of the human condition (Verma and Gustafsson 2020), it being one of the most acute health emergencies in the recent past (Vera-Valdés 2021). The occurrence of the disease hurt the global economies and caused insecurity on worldwide equity markets (Engelhardt et al. 2021). The extensive uncertainty of the plague and its related economic failures has triggered markets to turn extremely volatile and unpredictable (Zhang et al. 2020). Baker et al. (2020) suggested that no prior contagious virus outburst, including the Spanish Flu, has disturbed the equity market as strongly as the COVID-19 pandemic. Since it was difficult to expect and has never hitherto arose, this slump was described as a “black swan” event (Yarovaya et al. 2021). As compared with the 2008 crash which commenced in the United States and progressively diffused to other nations with a substantial time postponement, the coronavirus disease rapidly brought the worldwide economy to a stoppage by instantaneously hampering demand and supply lines around the globe due to extensive lockdowns (Ozkan 2021). Anser et al. (2021) noticed that COVID-19 contaminated cases are the central element that impedes financial activities and reduces money allocation, but a growing number of recovered cases offer investors’ trust to boost stock trade across nations. Agarwalla et al. (2021) documented that the rescue package had limited the extreme tail risks, but the volatility level persisted at a high level. Ghorbel and Jeribi (2021) claimed that equity indices and financial assets rely not only on their earlier volatility, but also on the preceding volatility of the fuel prices. Therefore, in the aftermath of SARS-CoV-2 virus diffusion, the unpredictability in stock exchanges substantially increased, thus causing huge shortfalls for investors (Farid et al. 2021) . The decline of the composite indicator of systemic stress among February and April 2020 was equivalent to the failures it recorded at the beginning of the 2008 global financial crisis and the 2011–2012 sovereign debt crisis, whereas the collapse in March 2020 was J. Risk Financial Manag. 2021,14, 341. https://doi.org/10.3390/jrfm14080341 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 341 2 of 29 the fourth-greatest monthly change since the commence of the euro (Borgioli et al. 2020). Pan et al. (2021) emphasized that the level of sovereign credit default swap enlarged throughout periods when the coronavirus pandemic turned out to be more critical. S&P 500 and EURONEXT 100 indices plummeted by nearly 30–38% between January and 15 June 2020 (data.europa.eu 2020), whilst Romania ranked ninth by considering the top ten best-performing leading European indices in the first half of 2020 (Bucharest Stock Exchange 2020). However, although the COVID-19 pandemic undesirably affected worldwide economies and stock exchanges, Fernandez-Perez et al. (2021) proved that culture significantly influences market volatility since nations with reduced individualism and great uncertainty avoidance respond more adversely and with larger instability than nations with high individualism and weak uncertainty avoidance. Thus, Hunjra et al. (2021) supported that East Asian markets reacted differently to manifold sanitation arrangements and virus security strategies. Additionally, Bannigidadmath et al. (2021) explored 25 nations and showed that their reaction to government measures was dissimilar, claiming that in states where the guidelines counted, the impact was mainly harmful. Orhun (2021) supported that equity markets of nations with greater health outflow, better promptitude for diseases and superior GDP per capita are more protected against the coronavirus crisis. Harjoto and Rossi (2021) proved that the current pandemic had a significantly larger adverse effect to the equity markets in emerging nations than in the developed states. COVID-19 induced panic and concerns amidst investors, thus contributing to social mindsets such as the herding behavior (Mnif et al. 2020). Shaikh and Huynh (2021) documented that investors’ concern came out to be greater in the equity sector first—ever since the stock market crash of 1987 and the global financial crisis of 2008–2009. For instance, Subramaniam and Chakraborty (2021) found a robust negative relationship among COVID19 fear and stock returns. Hence, returns were adversely influenced by fear through rising the market risk premium claimed by stockholders (Aggarwal et al. 2021). Bourghelle et al. (2021) found that the COVID-19 shock caused further oil price instability, primarily attributable to intensified insecurity, alongside stockholder tension and fear. Chang et al. (2020) argued that different to the Global Financial Crisis, investors panic about assuming risks, so they may imprudently get rid of all their holdings. Karamti and Belhassine (2021) advised that concern in the US market dispersed to the worldwide markets at the longer investment horizons. Hence, Kizys et al. (2021) documented herding conduct in the first three months of 2020, along with Espinosa-Méndez and Arias (2021), which confirmed that the pandemic heightened herding conduct in European equity markets. Ortmann et al. (2020) established that investors raised their brokerage deposits and launched further accounts, whereas mean weekly trading intensity expanded by 13.9% as the number of cases duplicated. Moreover, Pagano et al. (2021) proved that retail investors lessened momentum trading and heightened contrarian trading operations throughout the preliminary stage of this turning point, whilst Smales (2021) claimed that individual traders are more inclined to perform online explorations for facts to settle dwelling insecurity in the course of the corona crisis. On the contrary, Sun et al. (2021) argued that coronavirus-associated reports and economic-related publications do not generate unreasonable investment judgments. Besides, Hong et al. (2021) advised that the pandemic period was related with market inefficiency, establishing rewarding prospects for dealers and opportunists. Prior papers were focused on volatility examination for African equity markets (Lo et al. 2021;Takyi and Bentum-Ennin 2021;Zoungrana et al. 2021), the Australian stock market (Brueckner and Vespignani 2021), BRICS and G7 states (Yu et al. 2021), Canada and the US (Xu 2021), the Chinese stock market (Chen et al. 2021;Liu et al. 2021b; Shahzad et al. 2021), seven emerging countries (Hashmi et al. 2021), euro area stock markets (Duttilo et al. 2021), the Indian financial market (Bora and Basistha 2021), the South Korea stock market (Hoshikawa and Yoshimi 2021), Thailand (Hongsakulvasu et al. 2020), the Tunisian sectorial stock market (Fakhfekh et al. 2021), the US stock market (Curto and Serrasqueiro 2021;Hong et al. 2021), Vietnam and Philippines (Le and Tran 2021), Visegrad J. Risk Financial Manag. 2021,14, 341 3 of 29 Group member states (Czech et al. 2020), or several international markets (Al-Najjar et al. 2021;Al-Qudah and Houcine 2021;Anser et al. 2021;Banerjee 2021;Chowdhury et al. 2021;Contessi and Pace 2021;Engelhardt et al. 2021;Höhler and Lansink 2021;Rouatbi et al. 2021;Szczygielski et al. 2021b;Topcu and Gulal 2020;Vera-Valdés 2021;Youssef et al. 2021;Zhang et al. 2020). This paper aims to examine the volatility throughout the Romanian financial market during the COVID-19 pandemic. Investigating volatility is crucial, as an unexpected and substantial rise in instability may cause a financial meltdown (Uddin et al. 2021). We investigate an emerging stock exchange as long as these markets are more exposed to insecurity of pandemics and epidemics than developed markets (Salisu et al. 2020). In the present article, it was analyzed how the volatility on the Romanian stock market manifested itself due to the COVID-19 pandemic outbreak. Thus, in order to fulfill the objective of the study, coronavirus daily data were used between January 2020 and April 2021 for the following markets: USA, Italy, and Romania. For the Romanian capital market, we selected the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest Exchange Trading (BET) index, as well as a number of twelve shares, these being positioned in the top of the most traded on BSE at the time of this research. Regarding the variables used as proxies for COVID-19, they are related to the evolution of the new number of cases of COVID-19 registered in the USA, Italy, and Romania. Italy was the epicenter of the COVID-19 pandemic in Europe, whereas the USA had the highest number of COVID-19 cases. Moreover, the USA has some of the largest stock markets that have a strong impact on other markets. For instance, Celık (2012) proved that emerging markets appear to be the most affected by the contagion consequences from the U.S. Moreover, Le and Tran (2021) found evidence that Vietnamese and the Philippine stock markets are affected by the contagion effect from the US stock market throughout the COVID-19 pandemic. So far, the evidence for BSE is limited, this being, to the best of our knowledge, among the first studies that address the impact of COVID-19 on the Romanian capital market. The rest of the paper is organized as follows. Section 2reviews the related literature. Section 3describes the dataset and quantitative techniques. Section 4presents and discusses the empirical outcomes. Section 5concludes the study. 2. Literature Review The occurrence of COVID-19 has harmful effects on worldwide markets (Naeem et al. 2021), being expected to be the largest economic shock in human history (Insaidoo et al. 2021). Broadly, Xu (2021) noticed an adverse effect of a rise in the COVID-19 cases on the financial market. Chowdhury et al. (2021) claimed that European financial markets were the most terrible victim related to others. In the same vein, Youssef et al. (2021) noticed that European equity markets, excepting Italy, spread more spillovers to the whole other financial markets than they obtained, mainly through the coronavirus outburst. Szczygielski et al. (2021b) showed that pandemic insecurity has affected nearly all territories via smaller returns and heightened market volatility. Hence, the insecurity triggered by the COVID-19 outbreak and the rapidity with which the novel coronavirus dispersed around the world produced a panic in international financial markets (Lo et al. 2021). As such, Zhang and Hamori (2021) noticed that the effect of disease on the volatility of the oil and stock markets surpassed that of the 2008 global financial crisis. Moreover, Szczygielski et al. (2021a) proved that no national energy market was unharmed by COVID-19 insecurity. Hence, coronavirus disease lessened stock market liquidity involving equally the depth and the tightness facets (Mdaghri et al. 2021). For S&P 500 enterprises, Chebbi et al. (2021) documented a negative link among the quotidian increase in the numbers of coronavirus cases and fatalities and stock liquidity. Moreover, for the Shanghai stock market, Ftiti et al. (2021) confirmed the rise of stock market volatility and liquidity risk justified by a ripple effect triggered by the vulnerability of the sanitary sector. However, Curto and Serrasqueiro (2021) argued that coronavirus occurrence did not strike evenly across all the US segments and stock quotes. For instance, Milcheva (2021) noticed that the most affected segments in the US are J. Risk Financial Manag. 2021,14, 341 4 of 29 retail and hotels, but in Asia the most affected segment is the office. Nevertheless, Höhler and Lansink (2021) noted that the food sector was less influenced by the pandemic than other segments. A first strand of literature was focused on the reaction of stock returns to the existing pandemic. Topcu and Gulal (2020) exhibited that Asian markets were the most influenced by the pandemic, succeeded by South America and the Middle East. As such, Hongsakulvasu et al. (2020) revealed that COVID-19 disquiet negatively influenced every kind of Thailand’s stock return. Al-Najjar et al. (2021) claimed that coronavirus incidents exert an adverse impact on equity market indices of G8 countries. Al-Qudah and Houcine (2021) noticed that the surge in established cases of COVID-19 negatively influenced stock returns for the main affected nations in the WHO Regions. For Central, North, and the South American realm, Amin et al. (2021) concluded that COVID-19 cases undesirably influence market indexes. Takyi and Bentum-Ennin (2021) revealed that African financial markets performance lessened between − 2.7% and − 20 % throughout and subsequently the incidence of the pandemic. Czech et al. (2020) reported a negative association among the Visegrad stock market indices and the COVID-19 diffusion. For the case of emerging markets, Hashmi et al. (2021) advised that the number of coronavirus cases negatively influences stock prices mainly when these financial markets are in a bearish condition. Contrariwise, O’Donnell et al. (2021) found that the everyday amounts of COVID-19 cases did not explain the index price variations in China, Spain, Italy, the United Kingdom, and the United States. Zoungrana et al. (2021) revealed for the West African Economic and Monetary Union’s (WAEMU) stock market that weekly validated cases do not influence stock returns, even if the impact of death cases is harmful. However, Brueckner and Vespignani (2021) documented that COVID-19 contaminations had a positive influence on the performance of the Australian equity market. Another strand of research was oriented on how COVID-19 news influences stock returns and oil prices because terrific fear caused by the mass media is related with growing volatility in the financial markets (Haroon and Rizvi 2020). Chundakkadan and Nedumparambil (2021) provided evidence that emphasis on the pandemic has generated a pessimistic reaction between market players and weakened the stock exchanges. Weng et al. (2021) established that news throughout the coronavirus pandemic has more forecasting information, which is essential for the transient volatility estimating of fuel futures, whereas Salisu and Vo (2020) reinforced that considering health reports over illnesses boosts stock return foresight. Baek et al. (2020) advised a negativity tendency since adverse announcements concerning the number of fatalities are twice as impactful as optimistic facts with respect to recoveries. Wu et al. (2021) argued that media can stimulate the forecast of oil cost and usage over the COVID-19 contagion. Atri et al. (2021) noticed that the number of casualties and the COVID-19 panic adversely impact petroleum value, but the COVID-19 media coverage positively influences fuel cost in the short run. Further studies were exploring safe-haven assets throughout ongoing health crises. Huang et al. (2021) suggested that Bitcoin can promote efficient diversification and risk alleviation, whereas Mariana et al. (2021) strengthened that Ethereum is a superior safehaven than Bitcoin. Similarly, Disli et al. (2021) advised that gold, oil, and Bitcoin offer diversification benefits at extended investment perspectives. Ji et al. (2020) underlined that gold and soybean futures may uphold the worth of an investment. Contrariwise, B˛edowska-Sójka and Kliber (2021) claimed that cryptocurrencies rarely performed as weak safe-haven assets during several market disorders, whilst Conlon and McGee (2020) argued that Bitcoin does not behave as a safe haven over the bear market stemming from the coronavirus disease. For financial markets of Africa, Omane-Adjepong and Alagidede (2021) concluded that the safe-haven potential of precious metals, particularly gold, has diminished. In the same vein, Umar et al. (2021) contradicted the safe-haven feature of precious metals over the coronavirus plague, apart from silver. For the case of Chinese portfolios, Pho et al. (2021) found that Bitcoin is appropriate to risk-prone investors, whereas gold is adequate to prudent investors. J. Risk Financial Manag. 2021,14, 341 5 of 29 A summary of prior literature exploring equity market volatility due to the coronavirus pandemic is exhibited in Table 1. Table 1. Brief review of earlier studies towards the effect of the COVID-19 pandemic on stock market volatility. Author(s) Sample Period Quantitative Methods Outcomes Shahzad et al. (2021) CSI 300 sector index series for ten sectors 3 January 2019–30 September 2020 Vector autoregressive model Bad volatility spillover shocks dominate good volatility spillover shocks Salisu and Ogbonna (2021) Prices of Bitcoin, Ethereum, Litecoin and Ripple 2 September 2019–29 September 2020 GARCH MIDAS Return unpredictability of cryptocurrencies is riskier throughout the pandemic as related to prior financial slumps Abuzayed et al. (2021) 14 country-specific stock markets 7 January 2016–1 July 2020 Dynamic conditional correlation (DCC) conditional autoregressive heteroscedastic (GARCH) model Developed stock exchanges in North America and Europe spread and received more marginal extreme risk to and from the worldwide market index compared to Asian equity markets Bai et al. (2021)US, China, UK, and Japan financial markets 4 January 2005–30 April 2020 GARCH-MIDAS Pandemic positively influence perpetual volatility up to 24-month lag Li (2021) G7 and 3 emerging nations (China, India, and Brazil) 1 June 2009–28 August 2020 Asymmetry in volatility spillovers Equity markets of Japan, China, India, and Brazil are risk receivers Stock markets of the US, Germany, the U.K., France, Italy, and Canada are risk spreaders Tian and Ji (2021) MSCI indices of the US, the UK, France, Germany and the MSCI developed markets 2 January 2001–31 December 2020 GARCH copula quantile regression-based CoVaR model Germany exhibits the greatest risk spillovers, succeeded by France, the US and, the UK Malik et al. (2021)Brazil, Russia, India, China, South Africa 1 January 2013–24 April 2020 Baba-Engle-KraftKroner (BEKK) model The US, China and Brazil exhibited the highest individual volatility spillovers Yousfi et al. (2021)S&P 500 index and the CSI 300 index 5 January 2011–21 September 2020 GARCH models, DCC process, and wavelet coherence Higher volatility spillover among US and Chinese equity markets throughout the pandemic period than before it Contessi and Pace (2021) 18 main stock market indices 1 November 2019–29 May 2020 Generalized Supremum ADF (GSADF) test Volatility spread from the Chinese equity market to all other markets Liu et al. (2021a) 16 main equity markets in the world 24 January 2019–30 December 2020 Spillover analysis in time and frequency domain Following the outbreak of COVID-19 pandemic, the integration of global stock markets increases considerably and the market risk contagion between them also raised substantially J. Risk Financial Manag. 2021,14, 341 6 of 29 Table 1. Cont. Author(s) Sample Period Quantitative Methods Outcomes Zaremba et al. (2020)67 nations 1 January 2020–3 April 2020 Regression models Government interventions increase worldwide stock markets’ volatility Duttilo et al. (2021)European stock markets 4 January 2016–31 December 2020 Threshold GARCH The first wave of pandemic affected stock market volatility of euro area nations with middle-large financial centers, but the second wave impacted merely stock market volatility of Belgium Banerjee (2021) China and its key trading partners’ index futures contracts 1 August 2015–31 July 2020 Bivariate asymmetric dynamic conditional (ADCC) GARCH model Substantial financial contagion in most developed and emerging markets showing sizeable business relations with China throughout COVID-19 period Liu et al. (2021b) Shanghai A shares 1 January 2017–31 March 2020. GARCH with skewness The pandemic boosts financial market crash risk Hoshikawa and Yoshimi (2021) Volatility index of the South Korean Stock market (KVI) 2 January 2019–31 August 2020 VAR, OLS, GARCH The rise of new infection cases caused an upsurge in stock market volatility Bora and Basistha (2021) Nifty and Sensex stock indices 3 September 2019–10 July 2020 GJR GARCH Indian equity market has undergone volatility throughout the pandemic Fakhfekh et al. (2021)12 sectorial indices 4 January 2016–30 April 2020 EGARCH, FIGARCH, FIEGARCH, TGARCH Subsequent COVID-19 eruption, volatility is frequent in all series Yousaf (2021) Precious metals, industrial metals and, energy markets 22 January 2020–4 January 2021 BEKK-MGARCH Volatility diffusion is significantly negative from the COVID-19 to gold, palladium, and brent oil markets, but positively spread to the WTI oil market Source: Authors’ work based on the literature review. 3. Data and Methodology 3.1. Sample Selection For our study, we selected the most traded companies on the Bucharest Stock Exchange (BSE)—ALR, BRD, BVB, COTE, EL, FP, SNG, SNP, TEL, TLV, TRP and WINE—for the period January 2020–April 2021. To capture the types of causality between the variables regarding COVID-19 and the Romanian stock exchange, we decided to select the latest number of cases of COVID-19 registered in the USA, Italy, and Romania. The selected measures are presented in Table 2. J. Risk Financial Manag. 2021,14, 341 7 of 29 Table 2. Variables’ descriptions. Variables Description Variables regarding Romanian financial market BET Bucharest Exchange Trading is the first index developed by BSE and signifies the reference index for the Romanian equity market. BET indicates the performance of the most traded corporations on BSE’s regulated market, apart from financial investment enterprises. It is a free float market capitalization weighted index, with the highest weight of its components of 20%. ALR Alro is affiliate of Vimetco N.V., a worldwide, vertically-integrated primary and processed aluminium manufacturer. Field of activity: Aluminium production. BRD BRD Groupe Societe Generale is the second leading bank in Romania and the fourth market capitalization on the BSE. Field of activity: Other monetary intermediation. BVB The Bucharest Stock Exchange is the most significant organization of the local capital market. It coordinates and operates the regulated markets of financial instruments under European guidelines. Field of activity: Administration of financial markets. COTE CONPET delivers specific gas transport services via tubes and by rail, ensuring the supply of the factories with domestic and imported crude oil and derivatives. It manages a 3800 km pipeline grid encompassing 24 Romanian counties. Field of activity: Transport via pipeline. EL Societatea Energetica Electrica is a major participant in the energy sharing and supply market in Romania. Field of activity: Business and other management consultancy activities. FP Fondul Proprietatea is a joint stock company running as a closed-end investment company (Alternative Investment Fund) short of a set period, integrated in Romania, trading on the BSE since January 2011, and on the London Stock Exchange since April 2015. Field of activity: Trusts, funds and similar financial entities. SNG Romgaz is the leading natural gas manufacturer and the key provider in Romania. It is a joint stock corporation whose majority stockholder is the Romanian State owning a 70% share. Field of activity: Extraction of natural gas. SNP OMV Petrom is the leading energy corporation in Southeastern Europe. The firm is involved along the whole energy value chain: from exploration and fabrication of oil and gas, to processing and fuels supply, and further on to power production and advertising of gas and power. Field of activity: Extraction of crude petroleum. TEL Transelectrica is the Romanian Transmission and System Operator which performs a vital position in the Romanian electricity market. It operates and runs the energy spread system and delivers the electricity connections among the Central and Eastern European nations as a member of European Network of Transmission and System Operators for Electricity. Field of activity: Transmission of electricity. TLV Banca Transilvania is the first largest bank in Romania in terms of total assets. Field of activity: Other monetary intermediation. TRP TeraPlast SA is the parent corporation of the TeraPlast Group, respectively the major Romanian manufacturer of construction materials. Field of activity: Manufacture of plastic plates, sheets, tubes and profiles. WINE Purcari Wineries Group is a prominent participant in the wine and brandy sectors in the Central and Eastern Europe area, handling around 1,300 hectares of vineyards and 4 wineries placed in Romania and the Republic of Moldova. Field of activity: wineries. Variables regarding COVID-19 pandemic RO_COVID Number of new cases of COVID-19 in Romania IT_COVID Number of new cases of COVID-19 in Italy US_COVID Number of new cases of COVID-19 in USA Source: Authors’ own work. J. Risk Financial Manag. 2021,14, 341 8 of 29 The data consist of daily observations. For the variables regarding the Romanian financial market, the data source was Thomson Reuters Datastream, whereas for the variables regarding the COVID-19 pandemic, the data source was Our World in Data. The formula for daily yields is in line with Banerjee (2021); Bora and Basistha (2021); Curto and Serrasqueiro (2021); Duttilo et al. (2021); Fakhfekh et al. (2021); Ftiti et al. (2021); Ghorbel and Jeribi (2021); Höhler and Lansink (2021); Hong et al. (2021); Hongsakulvasu et al. (2020); Le and Tran (2021); Orhun (2021); Tian and Ji (2021); Yousfi et al. (2021); Yu et al. (2021); Zoungrana et al. (2021): Ri,l=lnPi,l Pi,l−1(1) where Ri,l is the yield of the index/asset iin period l, Pi,l is the asset price/stock market index value iin period land Pi,l−1 is the price of the asset/stock market index value in the l-1 period. Logarithmic yields were used because they are expected to have a normal distribution. 3.2. Quantitative Methods To explore the selected financial time series, we will employ ARCH/GARCH models as in prior studies (Salisu and Ogbonna 2021;Abuzayed et al. 2021;Bai et al. 2021;Banerjee 2021;Bora and Basistha 2021;Curto and Serrasqueiro 2021;Czech et al. 2020;Duttilo et al. 2021;Fakhfekh et al. 2021;Farid et al. 2021;Ghorbel and Jeribi 2021;Harjoto and Rossi 2021; Haroon and Rizvi 2020;Hongsakulvasu et al. 2020;Insaidoo et al. 2021;Le and Tran 2021; Liu et al. 2021b;Malik et al. 2021;Mariana et al. 2021;Omane-Adjepong and Alagidede 2021;Szczygielski et al. 2021a,2021b;Uddin et al. 2021;Vera-Valdés 2021;Xu 2021;Yousaf 2021;Yousfi et al. 2021;Yu et al. 2021;Zhang and Hamori 2021;Zoungrana et al. 2021). These models simultaneously evaluate and test processes of yields and volatility processes. ARCH models were introduced by Engle (1982) and Generalized (GARCH) by Bollerslev (1986). A GARCH model allows conditional variation to be dependent on its previous lags. GARCH models transform the AR process from the ARCH model into an ARMA process by adding an MA process. The GARCH model (p,q) has the following form: yt=µ+εt∼N0, σ2t(2) σ2t=ω+α1ε2t−1+· · · +αqε2t−q+β1σ2t−1+· · · +βpσ2t−p(3) where ω> 0 and αi≥0, βi≥0. From Equations (2) and (3), it can be seen that the conditioned variance of random perturbations depends both on the historical values of the shocks and on the values of the variance in the past. The coefficients of σ2t−p represent persistence of volatility, whereas the coefficients of ε2t−q signify the rate of reaction of volatility to shocks in the financial market. Parameter pis the order of the terms GARCH and qis the order of the ARCH terms. According to Baybogan (2013), the core issue with an ARCH specification is that it involves a substantial number of lags to seize the type of the volatility, whereas the GARCH framework is generally much more parsimonious for the reason that it integrates much of the evidence that a larger ARCH model with considerable lags would cover. In order to analyze the causality between the BET index and the number of new COVID-19 cases, we will estimate in the first instance three vector autoregression (VAR) models, much like those found in Anser et al. (2021), Chen et al. (2021), Chowdhury et al. (2021), and Youssef et al. (2021), incorporating the stock market index and each COVID-19 pandemic measure, as described below: BETt=δ1+ k ∑ j=1 βjBETt−j+ k ∑ j=1 γjCOVIDt−j+u1t(4) J. Risk Financial Manag. 2021,14, 341 15 of 29 Table 6shows the outcomes of GARCH approach. The model used was GARCH (1,1), restriction-Variance target, error distribution: Student’s t being selected to register among the smallest AICs among the other available variants, similar to Czech et al. (2020) and Xu (2021) . Only valid models, whose coefficients are statistically significant and different from 0, have been selected. Following the application of a GARCH model (1,1), we estimated the conditioned volatilities which are plotted in Figure 7. J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 19 of 30 Figure 7. Conditional volatility of stock market shares and BET stock market index over January 2020–April 2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. Further, Figure 8 exhibits the daily evolution of selected shares’ yields and the BET index for the period 2007–2021 in order to highlight the fact that the volatility in the period 2007–2009 was much more significant than that during the COVID-19 pandemic. This fact supports Le and Tran (2021), which pointed out for the case of Vietnam that the contagion effect throughout the coronavirus period was lesser than that over the global financial crisis. Figure 8. Daily values of the selected data over the period 2007–2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. Thus, the first two quarters of 2020 were marked by an increase in volatility on international financial markets, more pronounced in March and April, and the companies FP, ALR, SNP, and BVB had the highest volatilities during this period. August, September, 0% 20% 40% 60% 80% 100% 120% 140% 160% ALR BET BRD BVB COTE EL FP SNP WINE SNG TEL TLV –90% –70% –50% –30% –10% 10% 30% 50% 70% BET FP TLV SNP BRD SNG EL TRP TEL ALR WINE COTE BVB Figure 7. Conditional volatility of stock market shares and BET stock market index over January 2020–April 2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. We notice that in the first quarter of 2020, the volatility of the Romanian capital market increased to a level very close to that recorded during the global financial crisis of 2007–2009. Similarly, Curto and Serrasqueiro (2021) noticed an intensification of volatility following February 2020. Hence, this outcome is in line with both of the proposed hypotheses H1 and H2. Our results are consistent with Czech et al. (2020), which noted that Visegrad Group member countries were hit by the COVID-19 disease at the outset of March 2020 when the first case was registered. Besides, in the next two quarters, volatility had a downward trend, argued by the fact that COVID-19 vaccine findings were declared (Yu et al. 2021). In the same vein, Rouatbi et al. (2021) reinforced that the launch and expansion of the vaccinations reduce stock market volatility. Further, Figure 8exhibits the daily evolution of selected shares’ yields and the BET index for the period 2007–2021 in order to highlight the fact that the volatility in the period 2007–2009 was much more significant than that during the COVID-19 pandemic. This fact supports Le and Tran (2021), which pointed out for the case of Vietnam that the contagion effect throughout the coronavirus period was lesser than that over the global financial crisis. Thus, the first two quarters of 2020 were marked by an increase in volatility on international financial markets, more pronounced in March and April, and the companies FP, ALR, SNP, and BVB had the highest volatilities during this period. August, September, and October show moderate volatility, being higher than before the outbreak of the COVID19 pandemic. J. Risk Financial Manag. 2021,14, 341 16 of 29 Table 6. GARCH estimations. Dependent Variable: BET Dependent Variable: ALRO Variable Coeff Std. Error z-Stat Prob. Variable Coeff Std. Error z-Stat Prob. C 0.00118 0.000391 3.014619 0.0026 C 0.000289 0.000749 0.385655 0.6998 Variance Equation Variance Equation C4.76 ×10−6C4.51 ×10−5 RESID(−1)ˆ2 0.184229 0.054926 3.35414 0.0008 RESID(−1)ˆ2 0.139141 0.050071 2.778885 0.0055 GARCH(−1) 0.7911 0.062407 12.67647 0 GARCH(−1) 0.782887 0.077865 10.05445 0 T-DIST. DOF 4.264243 0.887964 4.80227 0 T-DIST. DOF 2.915888 0.214055 13.62215 0 R-sq −0.003773 Mean dependent var 0.000328 R-sq −0.000231 Mean dependent var 0.000654 Adj R-sq −0.003773 S.D. dependent var 0.013891 Adj R-sq −0.000231 S.D. dependent var 0.024085 S.E. of regr 0.013917 Akaike info crit −6.537876 S.E. of regr 0.024088 Akaike info crit −5.236626 Sum sq resid 0.065275 Schwarz crit −6.492633 Sum sq resid 0.195534 Schwarz crit −5.191383 Log likelihood 1108.901 Hannan–Quinn crit −6.519845 Log likelihood 888.9898 Hannan–Quinn crit −5.218595 DW stat 2.106484 DW stat 1.936172 Dependent Variable: BRD Dependent Variable: BVB Variable Coeff Std. Error z-Stat Prob. Variable Coeff Std. Error z-Stat Prob. C 0.00077 0.000709 1.085874 0.2775 C −0.000109 0.000504 −0.21581 0.8291 Variance Equation Variance Equation C1.43 ×10−5C2.12 ×10−5 RESID(−1)ˆ2 0.150634 0.043495 3.463245 0.0005 RESID(−1)ˆ2 0.292829 0.072636 4.031475 0.0001 GARCH(−1) 0.806065 0.061517 13.10319 0 GARCH(−1) 0.606027 0.102678 5.902193 0 T-DIST. DOF 5.300367 1.31796 4.021645 0.0001 T-DIST. DOF 4.976087 1.251612 3.975743 0.0001 R-sq −0.002232 Mean dependent var −8.72 ×10−5R-sq −0.000008 Mean dependent var −6.82 ×10−5 Adj R-sq −0.002232 S.D. dependent var 0.018169 Adj R-sq −0.000008 S.D. dependent var 0.014499 S.E. of regr 0.018189 Akaike info crit −5.52442 S.E. of regr 0.014499 Akaike info crit −6.126467 Sum sq resid 0.11149 Schwarz crit −5.479177 Sum sq resid 0.07084 Schwarz crit −6.081224 Log likelihood 937.627 Hannan−Quinn crit −5.506389 Log likelihood 1039.373 Hannan–Quinn crit −6.108436 DW stat 2.011758 DW stat 2.525927 J. Risk Financial Manag. 2021,14, 341 17 of 29 Table 6. Cont. Dependent Variable: COTE Dependent Variable: EL Variable Coeff Std. Error z-Stat Prob. Variable Coeff Std. Error z-Stat Prob. C 0.000205 0.00034 0.604738 0.5454 C 0.001186 0.000632 1.87812 0.0604 Variance Equation Variance Equation C4.99 ×10−6C2.13 ×10−5 RESID(−1)ˆ2 0.250596 0.04803 5.217482 0 RESID(−1)ˆ2 0.209315 0.052881 3.958218 0.0001 GARCH(−1) 0.729937 0.053054 13.75829 0 GARCH(−1) 0.708624 0.078695 9.004646 0 T-DIST. DOF 3.486178 0.373561 9.332278 0 T-DIST. DOF 5.627834 1.419299 3.965221 0.0001 R-sq −0.000229 Mean dependent var 0.000448 R-sq −0.000828 Mean dependent var 0.000723 Adj R-sq −0.000229 S.D. dependent var 0.016037 Adj R-sq −0.000828 S.D. dependent var 0.016134 S.E. of regr 0.016039 Akaike info crit −6.427494 S.E. of regr 0.016141 Akaike info crit −5.754231 Sum sq resid 0.086694 Schwarz crit −6.382251 Sum sq resid 0.087795 Schwarz crit −5.708987 Log likelihood 1090.246 Hannan–Quinn crit −6.409463 Log likelihood 976.465 Hannan–Quinn crit −5.736199 DW stat 1.767033 DW stat 2.51603 Dependent Variable: FP Dependent Variable: SNP Variable Coeff Std. Error z-Stat Prob. Variable Coeff Std. Error z-Stat Prob. C 0.001227 0.000524 2.341939 0.0192 C 0.000648 0.001333 0.486339 0.6267 Variance Equation Variance Equation C2.92 ×10−5C 0.00016 RESID(−1)ˆ2 0.191734 0.06115 3.13549 0.0017 RESID(−1)ˆ2 0.000401 0.007995 0.050126 0.96 GARCH(−1) 0.737594 0.078636 9.379818 0 GARCH(−1) 0.6 0.56513 1.061702 0.2884 T-DIST. DOF 2.80621 0.225963 12.41887 0 T-DIST. DOF 20 3.777537 5.294455 0 R-sq −0.00013 Mean dependent var 0.000995 R-sq −0.001325 Mean dependent var −8.05 ×10−5 Adj R-sq −0.00013 S.D. dependent var 0.020352 Adj R-sq −0.001325 S.D. dependent var 0.020049 S.E. of regr 0.020353 Akaike info crit −5.925241 S.E. of regr 0.020062 Akaike info crit −5.118899 Sum sq resid 0.139607 Schwarz crit −5.879998 Sum sq resid 0.135641 Schwarz crit −5.073656 Log likelihood 1005.366 Hannan–Quinn crit −5.90721 Log likelihood 869.0939 Hannan–Quinn crit −5.100868 DW stat 2.534692 DW stat 1.912486 J. Risk Financial Manag. 2021,14, 341 18 of 29 Table 6. Cont. Dependent Variable: SNG Dependent Variable: WINE Variable Coefficient Std. Error z-Statistic Prob. Variable Coeff Std. Error z-Stat Prob. C−0.00011 0.000579 −0.190745 0.8487 C 0.00093 0.000575 1.616535 0.106 Variance Equation Variance Equation C 0.0000175 C 2.53E-05 RESID(−1)ˆ2 0.206748 0.053863 3.838396 0.0001 RESID(−1)ˆ2 0.274618 0.063641 4.315126 0 GARCH(−1) 0.709914 0.084466 8.404724 0 GARCH(−1) 0.629318 0.093136 6.757009 0 T-DIST. DOF 4.572915 0.814926 5.611448 0 T-DIST. DOF 4.076108 0.615755 6.61969 0 R-squared −0.000296 Mean dependent var −0.000359 R-sq −0.001074 Mean dependent var 0.000398 Adjusted R-squared −0.000296 S.D. dependent var 0.01451 Adj R-sq −0.001074 S.D. dependent var 0.016247 S.E. of regression 0.014512 Akaike info criterion −5.903807 S.E. of regr 0.016256 Akaike info crit −5.761172 Sum squared resid 0.070969 Schwarz criterion −5.858564 Sum sq resid 0.089052 Schwarz crit −5.715929 Log likelihood 1001.743 Hannan–Quinn criter. −5.885776 Log likelihood 977.6381 Hannan–Quinn crit −5.743141 Durbin-Watson stat 1.830748 DW stat 2.292014 Dependent Variable: TEL Dependent Variable: TLV Variable Coefficient Std. Error z-Statistic Prob. Variable Coefficient Std. Error z-Statistic Prob. C 0.000993 0.00053 1.873592 0.061 C 0.000385 0.00059 0.652159 0.5143 Variance Equation Variance Equation C 0.0000155 C 0.00000789 RESID(−1)ˆ2 0.184978 0.055272 3.346684 0.0008 RESID(−1)ˆ2 0.169068 0.040267 4.198672 0 GARCH(−1) 0.750384 0.07748 9.684826 0 GARCH(−1) 0.810254 0.046858 17.2916 0 T-DIST. DOF 3.593242 0.439082 8.183526 0 T-DIST. DOF 3.940655 0.593309 6.64183 0 R-squared −0.000102 Mean dependent var 0.000837 R-squared −0.000272 Mean dependent var 0.0000632 Adjusted R-squared −0.000102 S.D. dependent var 0.01552 Adjusted R-squared −0.000272 S.D. dependent var 0.01956 S.E. of regression 0.015521 Akaike info criterion −5.879238 S.E. of regression 0.019563 Akaike info criterion −5.561135 Sum squared resid 0.081186 Schwarz criterion −5.833995 Sum squared resid 0.128969 Schwarz criterion −5.515892 Log likelihood 997.5912 Hannan–Quinn criter. −5.861207 Log likelihood 943.8319 Hannan–Quinn criter. −5.543104 Durbin-Watson stat 1.953884 Durbin-Watson stat 1.899838 Source: Authors’ calculations. Notes: Notes: Variables’ descriptions are provided in Table 2. Method: ML ARCH—Student’s t distribution (BFGS/Marquardt steps). Included observations: 338. Coefficient covariance computed using outer product of gradients. Presample variance: backcast (parameter = 0.7). J. Risk Financial Manag. 2021,14, 341 19 of 29 J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 19 of 30 Figure 7. Conditional volatility of stock market shares and BET stock market index over January 2020–April 2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. Further, Figure 8 exhibits the daily evolution of selected shares’ yields and the BET index for the period 2007–2021 in order to highlight the fact that the volatility in the period 2007–2009 was much more significant than that during the COVID-19 pandemic. This fact supports Le and Tran (2021), which pointed out for the case of Vietnam that the contagion effect throughout the coronavirus period was lesser than that over the global financial crisis. Figure 8. Daily values of the selected data over the period 2007–2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. Thus, the first two quarters of 2020 were marked by an increase in volatility on international financial markets, more pronounced in March and April, and the companies FP, ALR, SNP, and BVB had the highest volatilities during this period. August, September, 0% 20% 40% 60% 80% 100% 120% 140% 160% ALR BET BRD BVB COTE EL FP SNP WINE SNG TEL TLV –90% –70% –50% –30% –10% 10% 30% 50% 70% BET FP TLV SNP BRD SNG EL TRP TEL ALR WINE COTE BVB Figure 8. Daily values of the selected data over the period 2007–2021. Source: Authors’ own work. Notes: Variables’ descriptions are provided in Table 2. 4.3. Causality Analysis Given that the health crisis has a significant impact on the global economy, we also aimed to explore the causal relationships that are established between the variables regarding COVID-19 and the BET stock market index. Primarily, it was checked if the stock market index and each COVID-19 pandemic measure were cointegrated. In this regard, Table 7exhibits the outcomes of the Phillips–Ouliaris cointegration test. Accordingly, we reject the null hypothesis and decide that the series are cointegrated. Table 7. The outcomes of the Phillips–Ouliaris cointegration test. Series: RBET, DRO_COVID Included observations: 312 after adjustments Dependent tau-Statistic Prob.* z-Statistic Prob.* RBET −20.03971 0 −387.7359 0 DRO_COVID −37.24025 0 −316.0871 0 Series: RBET, DIT_COVID Included observations: 316 after adjustments Dependent tau-Statistic Prob.* z-Statistic Prob.* RBET −18.96646 0 −400.8999 0 DIT_COVID −23.96736 0 −284.9561 0 Series: RBET, DUS_COVID Included observations: 316 after adjustments Dependent tau-Statistic Prob.* z-Statistic Prob.* RBET −18.93794 0 −400.6069 0 DUS_COVID −27.8585 0 −369.0163 0 Source: Authors’ calculations. Notes: * MacKinnon (1996) p-values. Sample (adjusted): 6 January 2020–9 April 2021. Cointegrating equation deterministics: C. Long-run variance estimate (Bartlett kernel, Newey–West fixed bandwidth). No d.f. adjustment for variances. Variables’ descriptions are provided in Table 2. Further, the lag selection criterion is explored. Table 8reveals the related lag order selection criteria. Hence, the Schwarz information criterion suggests five and seven lags. J. Risk Financial Manag. 2021,14, 341 20 of 29 Table 8. VAR lag order selection criteria. Endogenous variables: RBET, DRO_COVID Exogenous variables: C Included observations: 296 Lag LogL LR FPE AIC SC HQ 0−1607.62 NA 181.2816 10.87581 10.90074 10.88579 1−1587.74 39.35295 162.8402 10.76852 10.84333 10.79847 2−1559.53 55.4685 138.2672 10.60494 10.72961 10.65485 3−1551.44 15.79743 134.5 10.5773 10.75184 10.64718 4−1458.68 179.8884 73.8342 9.977539 10.20195 10.06739 5−1439.81 36.32215 66.78202 9.87712 10.1514 9.986937 6−1429.8 19.15297 64.12454 9.836468 10.16062 9.966253 7−1399.38 57.75557 * 53.64450 * 9.657959 * 10.03198 * 9.807711 * 8−1398.9 0.893729 54.94222 9.681783 10.10568 9.851501 Endogenous variables: RBET, DIT_COVID Exogenous variables: C Included observations: 308 Lag LogL LR FPE AIC SC HQ 0−2012.61 NA 1645.988 13.08185 13.10607 13.09153 1−2007.18 10.74477 1630.827 13.0726 13.14526 13.10165 2−1987.05 39.60666 1468.66 12.96785 13.08896 13.01628 3−1975.61 22.36096 1399.403 12.91954 13.08909 12.98733 4−1947.2 55.16232 1194.285 12.76102 12.97902 12.84819 5−1902.16 86.8554 914.9435 12.49456 12.76099 * 12.60109 6−1892.38 18.73185 881.2824 12.45703 12.77191 12.58293 7−1884.43 15.12514 * 859.0133 * 12.43138 * 12.79471 12.57666 * 8−1883.75 1.295962 877.7618 12.4529 12.86467 12.61755 Endogenous variables: RBET, DUS_COVID Exogenous variables: C Included observations: 308 Lag LogL LR FPE AIC SC HQ 0−2539.78 NA 50478.62 16.50506 16.52928 16.51474 1−2520.57 38.04213 45731.93 16.4063 16.47897 16.43536 2−2508.57 23.61618 43416.26 16.35434 16.47544 16.40276 3−2503.86 9.195366 43218.5 16.34976 16.51931 16.41756 4−2483.95 38.65642 38977.11 16.24645 16.46444 16.33361 5−2453.01 59.68167 32721.35 16.07148 16.33791 * 16.17801 6−2442.42 20.28741 * 31351.76 * 16.02868 * 16.34356 16.15458 * 7−2441.24 2.234599 31934.01 16.04703 16.41035 16.1923 8−2439.71 2.89233 32452.48 16.06306 16.47483 16.2277 Source: Authors’ calculations. Notes: Sample: 3 January 2020–9 April 2021. * indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level). FPE: Final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan–Quinn information criterion. Variables’ descriptions are provided in Table 2. After estimating the VAR model for the stock market index and each COVID-19 variable (see Tables A1–A3), we proceed to explore the Granger causality relationships. According to Freeman (1983), a variable, X, which evolves over time, causes another variable in evolution, Y, if the predictions of the value Ybased on its own past values and on the previous values of Xare better than the predictions of Ybased only on Y’s own past values. Table 9shows the empirical results of the Granger causality test after VAR estimation. Thus, for the analyzed period January 2020–April 2021, no causal relationship was identified between the COVID-19 variables and the BET index. This outcome is not consistent with Liu et al. (2021b), who found that fear sentiment causes stock market crash risk. Therefore, Yu et al. (2021) cannot be maintained either since it was found that the COVID-19 Anxiety Index causes stock market returns. J. Risk Financial Manag. 2021,14, 341 21 of 29 Table 9. The results of the VAR Granger causality/block exogeneity Wald tests. Sample: 3 January 2020–9 April 2021 Included observations: 298 Dependent variable: RBET Excluded Chi-sq df Prob. DRO_COVID 2.811942 7 0.9018 All 2.811942 7 0.9018 Dependent variable: DRO_COVID Excluded Chi-sq df Prob. RBET 4.566812 7 0.7127 All 4.566812 7 0.7127 Sample: 3 January 2020–9 April 2021 Included observations: 311 Dependent variable: RBET Excluded Chi-sq df Prob. DIT_COVID 1.199299 5 0.9449 All 1.199299 5 0.9449 Dependent variable: DIT_COVID Excluded Chi-sq df Prob. RBET 3.959237 5 0.5553 All 3.959237 5 0.5553 Sample: 3 January 2020–9 April 2021 Included observations: 311 Dependent variable: RBET Excluded Chi-sq df Prob. DUS_COVID 1.266153 5 0.9384 All 1.266153 5 0.9384 Dependent variable: DUS_COVID Excluded Chi-sq df Prob. RBET 0.801005 5 0.977 All 0.801005 5 0.977 Source: Authors’ calculations. Notes: Variables’ descriptions are provided in Table 2. 5. Concluding Remarks The COVID-19 virus has spread very rapidly around the globe, negatively impacting the economy, and according to the latest information, it undergoes various mutations, with new variants of COVID-19 always appearing. The study of volatility has always been a hotly debated topic by experts, especially now in these times of uncertainty. The impact of COVID-19 on the capital markets did not take long to appear, so it initially manifested itself on the largest stock markets in the world, then, due to the contagion effect, it was transmitted to the other smaller markets. To our knowledge, the studies conducted on the Romanian capital market related to the research of volatility during the pandemic are extremely limited, which led us to focus on analyzing the volatility of the BSE indices. Our main goal of the article was to analyze the BSE volatilities during the COVID-19 pandemic, selecting indices and a group of traded shares (these being among the most traded on BSE, which are also found in the BET stock index). To study volatility, we used the GARCH model (1,1), and the graphical outputs capture the episodes of volatility. Finally, through the Granger causality test, after VAR estimation, we were able to identify the relationships to be established between BSE stock index, respectively, the shares traded on BSE and variables that capture the evolution of the COVID-19 pandemic in the USA, Italy, and Romania. This research contributes to the existing literature, which is the reason that we studied the volatility of the main companies traded on the Bucharest Stock Exchange, between January 2020 and April 2021, a period subject to a major change due to the COVID-19 pandemic, using GARCH models. We found that the distribution of the daily return series for the Romanian stock market is leptokurtic, it is not normally distributed, and has J. Risk Financial Manag. 2021,14, 341 22 of 29 significant time dependencies. The GARCH (1,1) model was used to model volatility on the Romanian stock market. The study revealed strong evidence of volatility that lasts over time, a trend of high and low volatility periods, and a high persistence of volatility on the Bucharest Stock Exchange. In the first quarter of 2020, capital market volatility in Romania increased to a level very close to that recorded during the global financial crisis of 2007–2009. In the next two quarters, volatility had a downward trend. Nevertheless, no causal association was noticed between the COVID-19 variables and the BET index. The empirical outcomes could help investors and asset managers to adjust their trading strategies. Moreover, the government should consider economic relief packages and formulate policies to lessen severe falls in prices (Hashmi et al. 2021). Author Contributions: Conceptualization, S , .C.G., D. S , .A. and C.C.J.; methodology, S , .C.G., D. S , .A. and C.C.J.; software, S , .C.G., D. S , .A. and C.C.J.; validation, S , .C.G., D. S , .A. and C.C.J.; formal analysis, S , .C.G., D. S , .A. and C.C.J.; investigation, S , .C.G., D. S , .A. and C.C.J.; resources, S , .C.G., D. S , .A. and C.C.J.; data curation, S , .C.G., D. S , .A. and C.C.J.; writing—original draft preparation, S , .C.G., D. S , .A. and C.C.J.; writing—review and editing, S , .C.G., D. S , .A. and C.C.J.; visualization, S , .C.G., D. S , .A. and C.C.J.; supervision, S , .C.G., D. S , .A. and C.C.J.; project administration, S , .C.G., D. S , .A. and C.C.J.; funding acquisition, S , .C.G., D. S , .A. and C.C.J. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Vector autoregression estimates for stock market index and the number of new cases of COVID-19 in Romania. RBET DRO_COVID RBET(−1) −0.039042 −1347.071 −0.05944 −3387.07 [−0.65686] [−0.39771] RBET(−2) 0.104624 −2907.972 −0.05457 −3109.71 [1.91723] [−0.93513] RBET(−3) −0.008179 −1776.737 −0.05343 −3044.46 [−0.15309] [−0.58360] RBET(−4) −0.017674 −1706.189 −0.05245 −2988.76 [−0.33699] [−0.57087] RBET(−5) 0.099649 −3998.206 −0.05212 −2970.19 [1.91184] [−1.34611] RBET(−6) −0.195458 −2384.377 −0.05129 −2922.5 [−3.81120] [−0.81587] J. Risk Financial Manag. 2021,14, 341 23 of 29 Table A1. Cont. RBET DRO_COVID RBET(−7) −0.012525 1802.935 −0.05213 −2970.85 [−0.24025] [0.60687] DRO_COVID(−1) −8.20 ×10−7−0.57646 −9.40 ×10−7−0.05368 [−0.87075] [−10.7389] DRO_COVID(−2) 1.33 ×10−7−0.803286 −1.10 ×10−6−0.06107 [0.12428] [−13.1529] DRO_COVID(−3) 2.53 ×10−8−0.150507 −1.20 ×10−6−0.06772 [0.02129] [−2.22260] DRO_COVID(−4) 5.87 ×10−7−0.261028 −1.20 ×10−6−0.06638 [0.50356] [−3.93240] DRO_COVID(−5) −1.44 ×10−70.637747 −1.20 ×10−6−0.06798 [−0.12112] [9.38180] DRO_COVID(−6) 4.04 ×10−70.306384 −1.10 ×10−6−0.06128 [0.37555] [4.99998] DRO_COVID(−7) 3.67 ×10−70.4326 −9.50 ×10−7−0.05413 [0.38643] [7.99193] C 0.001192 31.51544 −0.00066 −37.8873 [1.79229] [0.83182] R-squared 0.107799 0.729229 Adj. R-squared 0.063661 0.715834 Sum sq. resids 0.034824 1.13 ×108 S.E. equation 0.011093 632.1363 F-statistic 2.442354 54.44024 Log likelihood 926.2818 −2336.982 Akaike AIC −6.115985 15.78511 Schwarz SC −5.92989 15.97121 Mean dependent 0.00113 16.43289 S.D. dependent 0.011464 1185.836 Determinant resid covariance (dof adj.) 49.15166 Determinant resid covariance 44.32804 Log likelihood −1410.638 Akaike information criterion 9.668714 Schwarz criterion 10.0409 Number of coefficients 30 Source: Authors’ calculations. Notes: Standard errors in ( ) and t-statistics in [ ]. Sample (adjusted): 15 January 2020–9 April 2021. Included observations: 298 after adjustments. Variables’ descriptions are provided in Table 2. J. Risk Financial Manag. 2021,14, 341 24 of 29 Table A2. Vector autoregression estimates for stock market index and the number of new cases of COVID-19 in Italy. RBET DIT_COVID RBET(−1) −0.033198 −4632.009 −0.0569 −8536.69 [−0.58340] [−0.54260] RBET(−2) 0.246594 −2994.404 −0.05647 −8471.69 [4.36677] [−0.35346] RBET(−3) 0.029426 −4806.22 −0.05822 −8734.25 [0.50542] [−0.55027] RBET(−4) −0.110034 −10,837.98 −0.05661 −8492.63 [−1.94372] [−1.27616] RBET(−5) 0.177356 −8666.519 −0.05708 −8562.82 [3.10725] [−1.01211] DIT_COVID(−1) 8.74 ×10−8−0.211916 −3.40 ×10−7−0.05074 [0.25850] [−4.17662] DIT_COVID(−2) 1.75 ×10−8−0.302194 −3.40 ×10−7−0.05081 [0.05158] [−5.94763] DIT_COVID(−3) −3.00 ×10−8−0.169155 −3.50 ×10−7−0.05314 [−0.08468] [−3.18296] DIT_COVID(−4) −2.90 ×10−7−0.204575 −3.50 ×10−7−0.05182 [−0.84012] [−3.94743] DIT_COVID(−5) 1.35 ×10−90.487342 −3.50 ×10−7−0.05253 [0.00386] [9.27781] C 0.000266 76.10105 −0.00079 −118.787 [0.33573] [0.64065] R-squared 0.108079 0.452144 Adj. R-squared 0.078348 0.433882 Sum sq. resids 0.0579 1.30 ×109 S.E. equation 0.013892 2084.126 F-statistic 3.635264 24.75893 Log likelihood 894.2746 −2812.385 Akaike AIC −5.680222 18.15682 Schwarz SC −5.547947 18.28909 Mean dependent 0.000392 60.84887 S.D. dependent 0.014471 2769.942 Determinant resid covariance (dof adj.) 836.7493 Determinant resid covariance 778.6048 Log likelihood −1917.822 Akaike information criterion 12.47474 Schwarz criterion 12.73929 Number of coefficients 22 Source: Authors’ calculations. Notes: Standard errors in ( ) and t-statistics in [ ]. Sample (adjusted): 13 January 2020–9 April 2021. Included observations: 311 after adjustments. Variables’ descriptions are provided in Table 2.