*Article* **Performance Management for Growth: A Framework Based on EVA**

**Mihaela Brindusa Tudose 1, Valentina Diana Rusu 2,\* and Silvia Avasilcai <sup>1</sup>**


**Abstract:** Some of the constructs in the field of performance management are intuitive or not empirically validated. This study provides a data-driven framework for measuring and improving the performance through synchronized strategies. The ultimate goal was to provide support for increasing business performance. Empirical research materializes in an exploratory case study and a statistical analysis with econometric models. The case study revealed that a company can improve its performance, even in periods of growth, being characterized by consistent investments. The statistical analysis, performed on a restricted sample of companies, confirmed the results that were provided by the case study. The measurement of performance was made by capitalizing on financial and non-financial data precisely to intensify the interest for corporate sustainability. The obtained results, contrary to previous research that showed that economic value added (EVA) is negatively influenced by the increase in invested capital, open up new research perspectives to find out whether, at the industry level, performance appraisal that is based on EVA stimulates the development of a business's economic capital. The research has a double utility: scientific (by providing an overview of the state of the art in the field of performance management) and practical (by providing a reference model for measuring and monitoring performance).

**Keywords:** performance; measurement of performance; EVA; strategies; business success

#### **1. Introduction**

Business success depends on the quality of methods and techniques used for performance measurement, as well as on the ability of managers to manage the internal state and results of a company. Although increasingly complex methods have been developed, they failed to fully integrate (scientifically and practically) the 'multidimensional' feature of performance. Performance management has been accepted as a holistic process put at the disposal of managers due to diversity of elements defining high overall performance. Although scientific research is generous on methods of measuring performance, the companies are far from harnessing on the positive effects of implementing different methods. For performance measurement to become a good practice within companies, more awareness is needed regarding the role of measuring business performance. This is because performance measurement systems not only have an evaluative purpose, but they also help organizations to establish and use the most appropriate set of measurement indicators that reflect their objectives (Kennerley and Neely 2003). At the same time, measuring and monitoring performance facilitates the implementation of organizational strategy (Rodrigues 2010) and strengthens business confidence (Vukši´c et al. 2013).

This study focuses on customizing and detailing the performance measurement methodology that is based on EVA for companies in the automotive industry, while taking the strength of this industry (Adane and Nicolescu 2018) in the national and international

**Citation:** Tudose, Mihaela Brindusa, Valentina Diana Rusu, and Silvia Avasilcai. 2021. Performance Management for Growth: A Framework Based on EVA. *Journal of Risk and Financial Management* 14: 102. https://doi.org/10.3390/ jrfm14030102

Academic Editor: ¸Stefan Cristian Gherghina

Received: 26 January 2021 Accepted: 28 February 2021 Published: 4 March 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/).

169

economic context into account (Bostan et al. 2018). The choice for this research direction was justified from two points of view. First, we took the fact that, in the research on corporate performance, the traditional system of performance measurement indicators (focused on profit and return on assets/capital) is mainly used into account (Geng et al. 2021; Tudose and Avasilcai 2020). In this context, the research methodology focuses on the analysis of financial ratios (Batchimeg 2017; Luo et al. 2017; Egbunike and Okerekeoti 2018; Xu and Wang 2018; Kassi et al. 2019), which does not take all the costs of a business (such as the costs of capitals) into account and that allows for distorting or hiding of the real performance (Novyarni and Ningsih 2020). Second, out of all the modern methods for measuring performance, we considered the method that is based on the EVA because it allows the analysis of the results, but also of the way in which these results are obtained, being useful to both shareholders (for measuring real performance) and potential investors (in selecting investment opportunities). It is also a relatively simple method, and it can be implemented without incurring additional costs (that are related to the purchase of software or the investment of a person with exclusive monitoring responsibilities). Further proof of the reliability of this method is given by the fact that many organizations (such as Coca Cola, DuPont, Eli Lilly, Polaroid, Pharmacia and Whirlpool) have adopted EVA as a method of measuring performance (Annamalah et al. 2018).

Empirical research is focused on companies in the Romanian automotive industry. The arguments for this direction of research were multiple. First, because it has been reported that performance analysis depends on a multitude of factors that make it difficult to generalize results (Aguinis et al. 2012; Kijewska 2016), we opted for a case study and an econometric analysis for a restricted sample that was exposed to a common macroeconomic context. Second, many of the Romanian companies have not yet adopted a performance measurement model. Moreover, some authors (Crisan et al. 2010) opine that the problem for Romanian companies is not whether or not they have implemented a performance measurement system, but whether they make general measurements of business performance. Therefore, a reference model for measuring and monitoring performance is useful for both researchers and practitioners.

This study contributes to the existing literature in the following ways. First, it presents an original review of the management and performance measurement literature. Secondly, it presents the peculiarities of performance evaluation that is based on EVA for the automotive industry. Third, the paper provides evidence of the dynamics of performance (as assessed by EVA) during periods of growth throughout the company's life cycle. Because the results of our research are in contrast with the results of previous research, it is evidence that this area of research is far from exhausted; therefore, this study fills the research gap that is generated by the differences between theory and practice. The work was organized, so that the research has a double utility: scientific (by providing an overview of the state of the art in the field) and practical (by providing a reference model for measuring and monitoring performance). Therefore, the second section presents the results of the bibliographic research regarding the main methods of measuring performance and the specific features of EVA-based performance measurement. Afterwards, the research methodology and details the terminology used in this paper are presented. The next section presents the analysis, interprets the results of the performance measurement, and initiates discussions so that the case study and econometric analysis can be used as reference models for measuring and monitoring performance for companies in the automotive industry (to obtain higher earnings, to reduce the cost of capital, and to create value for stakeholders). The last section presents the conclusions and considerations on future research directions.

#### **2. Materials and Methods**

#### *2.1. State of the Art Regarding Performance Measurement*

The interest in performance measurement has intensified since 1980. In the early stage, efforts were made to measure the performance of the entire business. Later research placed performance measurement among the priorities of managers at all levels, so that

its scope widened, covering such issues as decision-making process, organizational roles, work maturity, business environment, increased competition, and advanced technology (Schläfke et al. 2013; Bhasin 2017; Taouab and Issor 2019).

Performance management aims to align performance (individual and team) with the strategic objectives of the organization, and performance management systems have multiple purposes (strategic, administrative, informational, developmental, organizational maintenance, and documentational purposes), according to Aguinis (2013) and Armstrong (2015). In the context of this research, the focus is mainly on the strategic purpose of performance management systems, which provide support for the design and adoption of growth strategies along the life cycle of the company.

Modern methods of performance measurement included various financial and nonfinancial indicators and provided organizations the support that is needed for their orientation (Searcy 2012). Growth in the number of stakeholders interested in the performance of an organization (Lozano 2015) and the growth of interdependencies of determining factors of performance increased the difficulty of measuring performance of an organization (Sorooshian et al. 2016). This is the context in which accurate measurement of performance became the main condition for growth of performance (Taticchi et al. 2010). The first modern methods of performance measurement were built around four items: economic added value, activity-based costing, market, and shareholder added value. Subsequently, other methods have been developed, such as: the methods that are based on the concept of 'total quality management' (ISO standards model, European model of quality management, model of service quality, 'six sigma' model); the methods based on the theory of causal relations (method of success critical factors and factor-results model); the measurement methods that are centered on processes (such as the reference model for supply chain operations and pyramid of performance measurement); the methods based on system balancing (Balance Score Card; performance prism; model of dynamic multidimensional performance); and, the multicriterial methods (such as TOPSIS, ELECTRE, PROMETHEE, VIKTOR, and COPRAS) (Narkuniene and Ulbinait ˙ e 2018 ˙ ).

The modern methods pointed out that the companies do not have to sacrifice long-term growth to maximize current earnings (O'Byrne 2016). They also emphasized that there is a significant positive relationship between the quality of management tools and techniques that are utilized and organizational performance (Afonina 2015). Moreover, the use of performance appraisal methods depends on the management structure (Suriyankietkaew and Avery 2016; Dobija and Kravchenko 2017), the structure of the board of shareholders (Liu et al. 2019), and size of business (Lee 2009). Researchers have also shown that the use of certain methods allow for reaching higher performance (Rajnoha et al. 2016) and the positive perception of companies on their business environment may stimulate their financial performance and accelerate their positive influence on the whole society (Belas et al. 2015).

#### *2.2. State of the Art Regarding EVA*

In its initial form, the measure EVA stipulated that a company can create wealth if it generates real economic profit and if its earnings in a business deal (respectively, net operation profits) are higher than the remuneration expected by funders. One hundred years later, the method was developed by the consultancy firm Stern Value Management (which owns the brand name EVA™) (SVM 2016) by moving it to the area of performance measurement from the perspective of company's ability to generate value for the shareholders. According to the methodological framework, performance as a measure of economic profit is determined as a difference between net operation profit after tax and opportunity cost of capital investment. If EVA is positive, then it is accepted that an organization creates value. Otherwise (net operating profit lower than the opportunity cost of capital investment), it is accepted that an organization (through its management) loses value. Therefore, the rate of growth of wealth should be higher than the rate of growth of invested capital in order to create value.

Some researchers (O'Byrne 2016; Daraban 2018; Jankalová and Kurotová 2020) revealed that EVA differs from other traditional performance values (such as gain per share, gross operating surplus, and return on sales), as it measures all company administration costs (operational costs and funding costs) and focuses on the control of production time, as well as operational and capital costs. Others report that EVA is an efficient measure for evaluating performance, as it: (a) involves all used resources and decentralizes management decision-making (Morard and Balu 2010; Malichova et al. 2017); (b) neutralizes differences in the level of risk of each strategic business unit (Mocciaro Li Destri et al. 2012); improves the quality of decisions taken at the managerial level, which facilitates the harmonization of interests of parties involved in the creation of value (Hasani and Fathi 2012); allows the performance of such managerial functions as monitoring, planning and signaling of strategic changes (Alam and Nizamuddin 2012); is useful to both shareholders (for measuring real performance) and potential investors (in selecting investment opportunities) (Novyarni and Ningsih 2020); is a relatively mature tool for evaluating listed companies (Geng et al. 2021); and, reflects the true economic profit of a business (Orazalin et al. 2019).

Recent research that is based on the use of EVA as a performance measurement tool has resulted in analyzes being performed on a single company (Wang and Yang 2014; Ion and Man 2019; Jankalová and Kurotová 2020; Novyarni and Ningsih 2020; Radneantu et al. 2010) or in sample-level analyzes (Pavelková et al. 2018; Geng et al. 2021). From the point of view of the analyzed field of activity, these studies focused on various fields, such as: tourism and hotel industry (Trandafir 2015; Geng et al. 2021), construction (Horak et al. 2020) oil industry (Wang and Yang 2014), steel industry (Ion and Man 2019), banking industry (Owusu-Antwi et al. 2015), IT sector (Radneantu et al. 2010), and the sale of spare parts (Jankalová and Kurotová 2020). The conclusions of this research are very diverse. Some authors have reported that the use of EVA requires an adaptation of strategies according to the specifics of companies (Geng et al. 2021). At the same time, it has been reported that EVA is more comprehensive than other performance indicators (Panigrahi et al. 2014), but it does not fully capitalize on the non-financial factors of performance (Wang and Yang 2014).

The literature search provided few evidences on the use of EVA to assess the performance of companies in the automotive sector. The only study identified (Pavelková et al. 2018) showed that the automotive industry is highly sensitive to business cycles. While separately analyzing the behaviors of manufacturers and suppliers in the automotive sector (2005–2012), the authors showed that added value was a key factor with the greatest positive impact on performance (as assessed by EVA) in all investigated periods—pre- and post-crisis).

Analyzing its limits, Bhusan Sahoo and Pramanik (2016) report that EVA analysis: (a) does not include such important determinants of performance as brand capital and human resources, etc.; (b) does not provide information about financial performance of companies affected by variations of business cycles; and, (c) does not stimulate growth of company wealth (as it is believed that the acquisition of fixed assets has a negative impact on performance measured by EVA).

Without neglecting the mentioned limits, we considered measuring the performance through EVA, because managers (concerned about efficient use of capital and growth of company value) can perform four types of interventions (Kijewska 2016): (a) the growth of net profit margin that would generate improvement of operations and efficiency; in this sense, production costs reduction and improvement of processes are most important; (b) growth of sales by identifying the market trends and quick response to needs/expectations/desires of clients); (c) a decrease of invested capital when it is not fully used (whether by selling assets, or reduction of administrative costs); (d) optimization of capital structure, i.e., the calculation of the combination of own-borrowed capital that minimizes the costs of resource purchase without affecting company's financial autonomy and flexibility; and, (e) the latter intervention reduction of costs that are associated with tax

burden (including tax burden related to various methods of financing) and allocation of capital to profitable investments allowing value creation.

Moreover, we justify this choice by the fact that the experts have admitted that measuring performance through EVA intensifies interest in corporate sustainability. They showed that 'the link between the sustainable value and EVA provides a huge potential for synergy' (Jankalová and Kurotová 2020) and EVA translates the indicator of financial performance into today's corporate language (Bhusan Sahoo and Pramanik 2016).

The empirical research was conducted on the example of Romanian companies that usually use the traditional performance measurement indicators (indicators integrated in the annual financial statements). Unlike traditional indicators of measuring financial performance (which allow for the direct processing of the information that is available in annual reports), the methodology for determining EVA requires adjustments to eliminate the influence of various national accounting practices (especially those on creative accounting). Therefore, we synthesized the results of the main studies that were based on the use of EVA to assess the performance of Romanian companies in order to facilitate the proximity between theory and practice.

Brad and Munteanu (2012) looked for a link between the process of value creation in financial and non-financial companies; they started from the premise that the performance of companies is influenced by performance of financial institutions (a key role being attributed to financial leverage); although the authors could not validate their hypothesis, they showed that the macroeconomic environment has a significant influence on the results of their research.

Some authors (Radneantu et al. 2010) conducted a deep diagnosis of a company (the IT sector) during economic crisis, showing that the use of EVA improves a company's capacity to manage the financial and non-financial factors that facilitates the development of growth strategies and reduces risks. Trandafir (2015) provides an example of added economic value analysis for companies in the hotel industry; the results of analysis (negative values for EVA) are explained by the specificity of operations, being highly marked by seasonality; although high profits were generated at the end of financial year, turnover had not been high enough to cause the creation of value for shareholders. Ion and Man (2019) researched the relevance of the economic added value (EVA) for stakeholders. Analyzing one Romanian steel company, they showed that EVA (calculated while considering the overall result) provides a more accurate image of company's overall performance (when compared to situation when EVA is calculated based on net profit from operation and total net profit). When comparing EVA with MVA (market value added, determined as difference between the market value per share, and the book value per share), other authors (Sichigea and Vasilescu 2015) showed that the best way to grow MVA is to maximize EVA, which is only possible if EVA is treated as a target of internal and external decisions.

#### *2.3. Research Methodology*

We opted for the analysis of added economic value for only one company (case study), respectively, for a restricted sample of companies with the same object of activity (econometric analysis), as some authors argued that the results of studies on EVA differ significantly by country, by sector, or even by company (Kijewska 2016). Because previous studies have shown that large and performing companies have a higher sensitivity to the business cycle (Pavelková et al. 2018), we have reduced the analysis period to five years (2014–2018). Moreover, taking the importance of this industry into account, but also the fact that Romanian literature does not provide an example for this field, we decided to analyze the economic added value for a company in the field of the automotive industry.

The choice of the company for the case study was random. The selected company (in the field of automotive manufacturing) has been listed on the Bucharest Stock Exchange until 2016, and it was delisted in 2017, as it could not meet the criteria that were related to the number of publicly distributed shares. Even so, the company continued to disclose its financial statements and annual reports, which allowed for the collection of data for analysis. In building the sample for econometric analysis, we had, as a benchmark, the adjustments made to the variables based on which EVA was determined (in the case study). In order to have access to information, such as current depreciation, advertising costs, research and development costs (R&D costs), advance expenses, shares held, and loans granted, our attention was directed to listed companies, which publish not only financial statements, but also explanatory notes and audited reports, and that detail the indicators mentioned. Out of the total number of companies listed on the regulated market, we only identified three companies that are producing spare parts for cars (CANE code 2392—Manufacture of parts and accessories for cars and car engines). Taking the recommendations of our research predecessors (indicated at the start of this section) into account, we decided to perform the econometric analysis on a pilot sample. Therefore, the final sample was represented by four companies: one unlisted company (on the example of which the case study was also performed) and three listed companies. Being a mixed sample (with listed and unlisted companies), the analysis was based on the capitalization of accounting information, which was collected from the financial statements and annual reports (available on the stock exchange website or on the website of each company).

Indicators used to analyze the dynamics of economic added value were:


Although the calculation of EVA seems to be relatively simple, it is, in practice, more complex, as several adjustments are needed to eliminate the influence of different accounting practices. Studies show that, out of 160 likely adjustments (Stewart 1999; Francisco de Almeida et al. 2016), only 10 have a more significant influence on NOPat and WACC (Brad and Munteanu 2012). These adjustments refer to: depreciation; research, development, and training expenses; promotional costs (advertising costs); deferred taxes; intangible assets (such as goodwill); non-interest-bearing debts (such as advances received from customers, salaries, and their related expenses); etc. In this context, we support the opinion, according to which the EVA method is usefulness due to its robustness and its immunity from creative accounting (Bhasin 2013).

In this study, NOPat was adjusted taking the following elements into account: current depreciation, research and development, and advertising expenses (Table 1). In order to calculate the invested capital (Ic), we used the elements of liabilities and assets from the balance sheet. Out of total external financing sources, we eliminated the non-cost debts (such as advances paid by clients, commercial debts and salaries, and their related payments). Next, net asset value purchased using financial leasing was seen as a longterm debt, and the related costs (annual expenses for lease payments) were treated as the elements of capital costs. The research and development and advertising expenses were also seen as belonging to invested capital, as they have effects over several years and contribute to business development. Additionally, we have deducted (of total invested capital) the assets not related to the fiscal year (such as prepaid expenses). Regarding the non-operating assets, as a novelty element in our research, we have extended the adjustment of the invested capital by deducting not only the owned securities, but also the granted loans. This adjustment is justified due to the fact that both investments in securities, as well as granted loans, are cash outflows reducing the company's possibilities to finance its current operations. In terms of financial management, these operations are associated with higher yield investments. As for operational management, these are viewed as a reduction of resources that are allocated for current operations.


**Table 1.** Indicators used in analysis.

Source: Authors own elaboration according to Pavelková et al. (2018); Jankalová and Kurotová (2020).

Concerning WACC, the calculation method took the features of different sources of funding into account. For higher accuracy of results, we opted for separate calculation of costs, as there were significant differences in the cost of own capitals and cost of borrowed capital for the analyzed company.

We determined the compound annual growth rate (CAGR) to identify the trend and significance of the data used in the analysis. Subsequently, we drew two performance profiles, one at the end of the five years analyzed and one at the end of the five years of forecast. The model of analysis that we proposed was intended to be a simple one to allow its practical operationalization. This was also the reason why the data processing was done using the analysis toolkit that was provided by Excel. Thus, based on information from the five years of analysis, the annual compound growth rate was determined using the "XIRR" function and the trends were identified using the "Trendline options" function.

Based on the obtained data, we reconstructed the integrated framework of decisionmaking that enabled economic value creation during the five years of analysis. When compared to other performance measurement tools, we showed that EVA has the advantage that it includes, in the analysis, all costs that are related to the business, regardless of whether they appear in the income statement, balance sheet, or in the financial statements (Annamalah et al. 2018), allows for an analysis of results and highlights how these results are obtained and improves the quality of management decision-making.

The econometric analysis was performed on a sample of four companies in the same field of activity (automotive), having adjustments that were comparable to the company on which the case study was performed (also included in the sample), in order to ensure the representativeness of the data. Data were collected from secondary sources (for the same period for which the case study was conducted, 2014–2018). Eviews 9 software was used to perform statistical analyses with econometric models that estimate the impact of a set of variables on EVA. The analyses are based on the panel data method, which is a specific method of generating equations for data containing both time series and cross sections.

#### **3. Results**

Recent studies report that the adoption of EVA method by increasing companies worldwide is a proof of the fact that it provides an integrated decision-making framework for creating sustainable value for companies, clients, employees, shareholders, and management (Bhasin 2013; Bhusan Sahoo and Pramanik 2016).

In order to support companies wishing to adopt EVA as a performance measure, we will be presenting the features of this method (Table 2). The first step in measuring the added economic value includes the calculation of operational profit after tax (NOPat\_a). The analysis shows that, in just five years, the company managed to double its gross operational profit. Yearly current depreciation was around half of NOPat\_a due to the

high value of fixed assets. For the five analyzed years, the total value of net fixed assets grew by 22.4%; mainly technical installations, machinery, and working equipment had been purchased; therefore, as reported by earlier studies (Bhusan Sahoo and Pramanik 2016), growth in the fixed assets purchase could have a negative impact on performance that was measured by EVA. An increase in sales volume is attributed to the increase in advertising costs by 51% in 2018 as compared to 2014, and an increase in research and development costs by 40.6%. Therefore, by accepting that these costs contributed to business development, it is expected that NOPat\_a will grow.


**Table 2.** Net operation profit after tax, invested capital, and weighted average cost of capital.

Source: Authors own calculation.

The second step in measuring the performance based on economic added value is the calculation of invested capitals and their adjustment in accordance with method's rigor. Table 2 shows that the company was mainly financed by own capitals. The external capital includes financial debts (mainly agreements within the group) and a financial leasing for a logistic deposit for a period of 15 years. In order to adjust the invested capital, we added the advertising costs and the R&D costs (seen as capital allocations for long-term business development) and we subtracted the value of shares held and loans granted as well as the expenses in advance. The increase in the invested capital may indicate a decrease of EVA (Ic that is multiplied by WACC is subtracted from NOPat\_a, decreasing the prospects of growth for EVA).

The strategic financial structure was used as a reference point to calculate the weighted average cost of capital. As it also results from Table 2, the company was mainly financed by its own funds (the average share—along all years—of these funding amounts to 84.4%). The cost of own capitals was calculated by relating the net dividends to own capitals. The cost of indebtedness (corresponding to external capitals) was calculated considering the deductibility of interest expenses. During 2014–2018, the interest expenses were fully deductible (because the interest expenses were lower than the income from interest).

The increase of weighted cost of capital (from 9.0% in 2014 to 13.2% in 2018), correlated with the increase of invested capital, may be linked to the negative impact on EVA (if the rate of growth of NOPat\_a is lower than the rate of growth of invested capital opportunity cost). Economic Value Added asserts that businesses should create returns at a rate that is above their cost of capital (EVA 2019).

Table 3 presents the summary of results. In all five years of the analyzed period (2014–2018), the company had a positive EVA, which means that the rate of growth of income was higher than the rate of growth of capital that was allocated to production processes. By analyzing the rate of annual variation of added economic value, we have found an alternation between the annual decreases and increases.


**Table 3.** Economic value added (thousand euro).

Source: Authors own calculation.

Strategically, for the five analyzed years (from the perspective of the initial and final situation), we observe two important aspects: (a) each year the company increased the invested capital; and, (b) EVA has been positive and has increased.

We performed an econometric analysis, according to the coordinates established in the *Research methodology* section because the case study showed that a company can increase EVA, even if the invested capital increase. The dependent variable of econometric analysis is EVA. The independent variables are: adjusted invested capital (Ic\_a); return on assets (ROA) determined as the ratio between gross profit and total assets; fixed assets volume (FA); return on fixed assets (Raf), determined as the ratio between gross profit and total fixed assets); and, level of indebtedness (LI), determined as the ratio between debt and total financing.

The indicators determined on the basis of fixed assets (FA and Raf) were included in the analysis precisely to identify the link between their dynamics and EVA dynamics. Subsequently, because the case study indicated that part of the investments was financed on the basis of external capital (financial debts), the influence of this variable was also analyzed.

Based on the identified interdependencies, we proposed testing the following hypothesis:

#### **Hypothesis 1 (H1).** *Adjusted invested capital (Ic\_a) is positively correlated with EVA.*

This first hypothesis was based on the results of the case study indicating that EVA increased due to the increase in investments in fixed assets. The confirmation of this hypothesis may bring an element of novelty in scientific research, as previous studies have reported opposite results (Bhusan Sahoo and Pramanik 2016).

The ROA variable was introduced, because, at the sample level, it was observed that a company registers a positive EVA while the gross result is negative. This situation justified the testing of the interdependencies between the return on assets (ROA) and EVA, for which the following hypothesis was formulated:

#### **Hypothesis 2 (H2).** *There is a direct determination relationship between EVA and the rate-based performance indicator (ROA).*

The confirmation of this hypothesis may be further evidence to support the superiority of EVA over traditional performance measures (accused of not reflecting economic reality) (Novyarni and Ningsih 2020).

The analysis at the level of descriptive statistics (Table 4) indicated that the indebtedness level is the variable with the highest variation. Thus, the indebtedness level of the companies from our sample varied between a minimum of 29% and a maximum of almost 55%. The return on fixed assets is another variable that also varied significantly, which took values between a minimum of −3.69% and a maximum of 10.4%. ROA also recorded significant variations (between 9.4 and −2.1). We calculated the natural logarithm for these indicators (Logarithm of EVA, Logarithm of Ic\_a, and Logarithm of FA) due to the fact that EVA, Ic\_a, and FA are expressed in absolute dimensions (thousand euros), in order to obtain correct results in future analyzes. However, the number of observations obtained for Logarithm of EVA is 18 due to the fact that EVA also has two negative values (see Table 4). The econometric practice shows that, for obtaining the logarithm of the variables that also have negative values, either a constant value is added to the data prior to the log transformations. Additionally, the transformation becomes log (Y + a), where a is the constant. However, this is not always a good idea, because it might change the way that we interpret the results. Sometimes, a better way to handle negative values is to use the missing values for the logarithm of a non-positive number.

The correlation matrix shows that, from the independent variables chosen, some are highly correlated: fixed assets with invested capital adjusted, and ROA with a return on fixed assets (marked with bold in Table 5). Therefore, in the following analysis, we alternatively excluded the correlated variables.

The general equations of the regression model applied are as follows: Model 1:

Logarithm of EVA*it* = Logarithm of Ic\_a *it* β1 + ROA*it* β2 + LI*it* β3+U*it*, (1)

where: *i* represents the companies included in the analysis, *t* is time (2014, ... , 2018); Logarithm of EVA*it* is the dependent variable and Logarithm of Ic\_a *it*, ROA*it,* and LI*it* are the independent variables; β1, β2, β3, represent the coefficients; and, U*it* is the error term. Model 2:

Logarithm of EVA*it* = Logarithm of FA*it* β1 + Rfa*it* β2 + LI*it* β3+U*it*, (2)

where: *i* represents the companies included in the analysis, *t* is time (2014, ... , 2018); Logarithm of EVA*it* is the dependent variables and Logarithm of FA*it*, Rfa*it*, and LI*it* are the independent variables; β1, β2, β3, represent the coefficients; and, U*it* is the error term.


**Table 4.** Descriptive statistics of the variables included in the analysis.

Note: <sup>1</sup> Values in millions of RON (national currency of Romania); <sup>2</sup> Values in millions of EURO. Source: Processed by the authors.

#### **Table 5.** Correlation matrix.


Note: probability in parenthesis. Source: Processed by the authors.

Table 6 centralizes the results of the regression analysis. The two regression models applied resulted in being statistically significant; the probability associated to F-statistic is higher than 0.01, showing that the predictors are related significantly with the dependent variable.

The coefficients that were obtained for Model 1 show that the invested capital adjusted and ROA are positively and statistically significantly related to EVA. For Model 2, only one variable has a positive and statistically significant coefficient: fixed assets.


**Table 6.** Regression analysis.

Note 1: Standard error in parenthesis; Note 2: \* and \*\*\* represents significant values at 1% and 10%. Source: Processed by the authors.

#### **4. Discussion**

#### *4.1. Discussions on the Case Study*

The recorded performance (appreciated by EVA) is the result of the strategies adopted by the company, such as: growth of operational efficiency; growth of income from sales; and, adaptation of financial structure to support the two previously formulated strategies.

	- Improvement of processes. For the analyzed period, the degree of automation of production lines grew. According to the management report, the company assumes the fact that a high degree of automation is a pre-requisite for shortening the production time and providing the highest level of quality.
	- Increasing the efficiency in the use of raw material. The products with the highest attractiveness on the market were targeted. In 2014, only one car model car amounted to 50% in total production. At the end of 2018, the share of this car model reached 79% (the highest demand came from the EU).
	- Increasing the efficiency in the use of human resources. The average sold production per employee grew from 12 to 15 million euro over five years. It could be explained by the changes in the structure of production; the company reducing the number of cars with a lower added value (and lower production cost) in favor of cars with higher added value (despite their higher cost). Evidence of the degree of accountability and involvement of human resource in the growth of performance lies in the fact that the average growth rate of human resource expense (6.9%) was below the average growth rate of production sold per employee (7.5%).

strategy, the company used the opportunity that was provided by the market and undertook the goal to increase the volume of its production. When the sales strategy was formulated, it has been taken into account that 92% of sales in 2014 were on foreign markets. The company succeeded in only five years to reduce its dependence on foreign markets (the volume of sales from export reaching 85% in 2018) and focus on closer markets. In 2018, 66% of exports oriented towards Europe, 15% towards Asia, 12% towards Africa, and 7% towards America. This redefining in the share of markets is justified by high expenses that are related to car deliveries on different continents.



**Table 7.** Compound Annual Growth Rate.

Source: Authors own calculation.

**Figure 1.** The impact of the variation of economic added value (EVA) determinants (compound annual growth rate (CAGR) [%]). Source: Authors own representation.

During the five years of the analysis, the adjusted NOPat had a compound annual growth rate of 7.43%. During the same period, adjusted invested capital (6.61%), the weighted average cost of capital (7.96%), and fixed assets (3.46%) have increased. The cumulative effect of these changes materialized in an annual compound growth rate of EVA of 2.48%. These results empirically prove that EVA can grow, even in the face of increased investment in fixed assets.

We determined the trends for the analyzed indicators (for a forecast horizon of five years) in order to obtain significant conclusions regarding the efficiency of management and the trends of the future evolution, based on the information in Table 2. Based on the least squares method, we looked for a suitable trend line. We tested the types of trends that best fit the data set analyzed. Figure 2 indicates that the polynomial model fits the data best, because R-square has the best values (closer to 1).

A polynomial trend line indicates that the data vary (as confirmed by previous annual analyses), being recommended by the Excel package to describe the relationship between two variables with different trends up to a certain point, beyond which their trends synchronize. Therefore, we can admit that, for the forecast period, the increase of invested capital (adjusted) and net operating profit (adjusted) will generate a higher economic value. These increases will amplify over time, due to operating expenses (such as research and development or promotion) that generate benefits in future financial years.

#### *4.2. Discussions on the Econometric Analysis*

The invested capital adjusted and ROA are positively and statistically significant related to EVA, according to Model 1. Therefore, an increase of investments in total assets will lead to an increase in EVA. Additionally, higher rates of ROA will determine an increase of EVA. These results are in contradiction with previously research results which shown that EVA does not provide information regarding the financial performance of companies affected by variations of business cycles and does not stimulate growth of company wealth (Bhusan Sahoo and Pramanik 2016). The lack of congruence of the research results is further evidence that this area of research is far from exhausted; therefore, this study fills the research gap that is generated by the differences between theory and practice.

The second hypothesis was assumed to test the extent to which different performance indicators are correlated, as we mentioned in the previous section. Because the increase in ROA (as an independent variable) contributes to the increase in EVA (as a dependent variable), the superiority of EVA over traditional performance measures is confirmed (Novyarni and Ningsih 2020). Subsequently, when considering the situation identified at

the level of the primary analysis data (for one of the companies in the sample the ROA was negative during the analyzed period while EVA was positive) it is confirmed that EVA reflects the true economic profit of a business (Orazalin et al. 2019).

Only one variable has a positive and statistically significant coefficient: fixed assets, according to Model 2. This result shows that increasing the level of fixed assets will determine an increase of EVA. Therefore, the acquisition of fixed assets (in the business growth phase) has a positive impact on performance that is measured by EVA. In other words, a company can improve its performance, even in periods of growth, which are characterized by consistent investments.

The other two variables considered (return on fixed assets and level of indebtedness) did not result in being statistically significant with EVA for the case of the companies considered. The value obtained for R-square adjusted indicates that over 95% of the variation of EVA is explained by the variation of the independent variables.

#### *4.3. Practical Implication*

The results of this study are of interest to both investors and managers, because they improve on the understanding of the variables that influence EVA. A positive and growing EVA (in the conditions of growth the invested capital) provides evidence to investors regarding the business's ability to generate superior performance in the future. Subsequently, the clarification of the methodology for determining the EVA, adapted to the Romanian companies in the automotive industry, facilitates the decision-making process of the managers, preventing any possible distortions in the performance evaluation. Thus, managers gain additional information regarding the performance and position of the company they manage. Following the EVA methodology, managers can know whether or not the investments made create value, if the weighted average cost of capital is lower than the internal rate of return of the business (respectively, if the absorption of financing is positive), whether the growth rate of sales is lower than the growth rate of operational expenses, etc. A skilled manager can turn this information into real competitive advantages.

#### **5. Conclusions**

EVA, as a measure of performance, provides managers the opportunity and motivation to take decisions growing the value of business in both the interests of shareholders and other stakeholders. Although the center of decision-making, which is responsible for monitoring and measuring performance, is placed in the area of financial management, it does not neglect the non-financial issues. The method's accuracy makes it easy to be understood by managers at all levels (including the non-financial managers) and makes it possible to measure the performance of the entire business.

Our literature reviews have found a growing interest in using EVA as a performance measure. Although empirical research focused on various areas, few evidences were identified on the use of EVA to assess the performance of companies in the automotive sector. For example, Pavelková et al. (2018) showed that the car industry is very sensitive to economic cycles, and value added is a factor with a major positive impact on performance in both the pre- and post-crisis period. This paper summarizes the advantages and disadvantages of using EVA as a tool for measuring performance, presents the peculiarities of its determination, analyzes the dynamics (relative to other variables), and points out its usefulness for shareholders, investors, and managers.

The case study provides an original methodological framework for applying this method of performance measurement, which is adapted to companies in the automotive industry. In an original manner, we have considered not only the calculation of indicators specific to EVA (based on past events), but also the presentation of arguments that lied at the basis of business decisions that led to the growth of performance. Thus, we have shown that EVA-based performance management depends not only on accounting information, but also on the way that the information from outside the company is used. Moreover, the study confirms that EVA has the ability to provide investors and corporate managers

the information regarding to the company's prospects for higher earnings in the future (Bhasin 2013).

Additionally, the empirical research provides evidence fighting one of the criticisms brought to this method, namely that the EVA methodology does not stimulate the development of economic capital of a business due to the fact that the purchase of fixed assets has a negative impact on performance (Bhusan Sahoo and Pramanik 2016). Our study has shown that a company can increase EVA, even under conditions of growth of invested capital, respectively, fixed assets if: (a) the rate of remuneration growth that is required by funders is lower than the rate of net operational profit growth; and, (b) there is a consensus among funders on the reduction of current remuneration over higher future financial remuneration. At the same time, the results confirmed that EVA is superior to traditional performance indicators (Novyarni and Ningsih 2020), and that it reflects the true economic profit of a business (Orazalin et al. 2019).

The increase in economic value added was possible due to the division of the company mission into synchronized strategies. This way, the strategy of growing the efficiency of operational activities allowed for the improvement of processes (by automation of production lines and improving quality), the growth of efficiency in the use of material resources (by their allocation to products with the highest attractiveness on the market), and growth of efficiency in the use of human resources (by increasing work productivity and the degree of accountability and involvement of human resources in growth of performance). Subsequently, the strategy of growth of income from sales focused on identifying and favorable exploitation of opportunities (the average age of car fleet in Romania and the EU, restrictions on imports and on polluting vehicles, the degree of competitive pressure) and of the needs of car users. To create value for clients, the company adapted its production lines in such a way as to them to produce the most solid model. The two strategies (growth of operations efficiency and sales growth) were supported by financial strategy centered mainly on the consolidation of own capital. From the perspective of shareholders, financial autonomy was more important than the opportunities for integral use of fiscal benefits that are related to indebtedness.

The study has some limitations. The adjustment of NOPat and Ic was made according to the most relevant elements that were identified in the case study, but they are not generally valid for all companies. In econometric analysis, only accounting financial data were used (not all companies in the sample are listed, which made it difficult to determine the real cost of equity from the perspective of shareholder expectations). The restricted sample on which the situations identified in the case study were tested allows for a limited generalization of the results. In order to ensure a greater representativeness of the results, we are considering an extensive research development (by enlarging the sample, so that the results are representative at the industry level, but without losing sight of the specificity of EVA determination) and an intensive development (by analyzing the performance in the different stages of economic growth/decline). We strongly believe that theory and practice from the performance management field (including performance measurement methods such as EVA) may be continuously improved, according to changes in the business environment. By providing support for putting the methods of performance measurement into practice, we aim to adapt the scientific methods of performance measurement to practical specificity of company performance measurement and validate these methods in practice.

**Author Contributions:** All authors contributed equally to the writing of the paper. Conceptualization, M.B.T., V.D.R. and S.A. Formal analysis, M.B.T., V.D.R. and S.A. Methodology, M.B.T., V.D.R. and S.A. Writing—review & editing, M.B.T., V.D.R. and S.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**S, tefan Cristian Gherghina \*, Daniel S, tefan Armeanu and Camelia Cătălina Joldes,**

Department of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania; darmeanu@yahoo.com (D.S, .A.); joldes.catalina@yahoo.com (C.C.J.) **\*** Correspondence: stefan.gherghina@fin.ase.ro; 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

**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/).

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 COVID-19 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

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 2 reviews the related literature. Section 3 describes the dataset and quantitative techniques. Section 4 presents and discusses the empirical outcomes. Section 5 concludes 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

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.

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.



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.

#### **Table 2.** Variables' descriptions.


Source: Authors' own work.

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):

$$R\_{i,l} = \ln \left( \frac{P\_{i,l}}{P\_{i,l-1}} \right) \tag{1}$$

where *Ri*,*<sup>l</sup>* is the yield of the index/asset *i* in period *l*, *Pi*,*<sup>l</sup>* is the asset price/stock market index value *i* in period *l* and *Pi*,*l*−<sup>1</sup> 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:

$$y\_t = \mu + \varepsilon\_t \sim N\left(0, \sigma^2\_{\ \prime}\right) \tag{2}$$

$$
\sigma^2 \sigma^2 = \omega + \alpha\_1 \varepsilon^2 \varepsilon\_{t-1} + \dots + \alpha\_q \varepsilon^2 \varepsilon\_{t-q} + \beta\_1 \sigma^2 \varepsilon\_{t-1} + \dots + \beta\_p \sigma^2 \varepsilon\_{t-p} \tag{3}
$$

where *ω* > 0 and *α<sup>i</sup>* ≥ 0, *β<sup>i</sup>* ≥ 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 *σ*<sup>2</sup> *<sup>t</sup>*−*<sup>p</sup>* represent persistence of volatility, whereas the coefficients of *ε*<sup>2</sup> *<sup>t</sup>*−*<sup>q</sup>* signify the rate of reaction of volatility to shocks in the financial market. Parameter *p* is the order of the terms GARCH and *q* is 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:

$$BET\_l = \delta\_1 + \sum\_{j=1}^{k} \beta\_j BET\_{t-j} + \sum\_{j=1}^{k} \gamma\_j \text{COVID}\_{t-j} + \mu\_{1t} \tag{4}$$

$$COVID\_t = \delta\_2 + \sum\_{j=1}^{k} \psi\_j COVID\_{t-j} + \sum\_{j=1}^{k} \varphi\_j BET\_{t-j} + u\_{2t} \tag{5}$$

where *δ*<sup>1</sup> and *δ*<sup>2</sup> are the intercepts, *β*, *γ*, *ψ*, and *ϕ* are the endogenous variables coefficients, whereas *u* are the residual terms.

Further, for each estimated VAR model, we will employ the Granger causality test, as in earlier literature (Bourghelle et al. 2021; Chen et al. 2021; Liu et al. 2021b). So as to perform the causality test, the data series must be stationary and zero average (Granger 1969). The null hypothesis is that *b* does not cause Granger on *c* and that *c* does not cause Granger on *b*. The following bivariate regressions are given:

$$\mathfrak{a}\_{t} = \mathfrak{a}\_{0} + \mathfrak{a}\_{1}\mathfrak{c}\_{t-1} + \dots + \mathfrak{a}\_{p}\mathfrak{c}\_{t-p} + \beta\_{1}\mathfrak{b}\_{t-1} + \dots + \beta\_{p}\mathfrak{b}\_{-p} + \mathfrak{e}\_{t} \tag{6}$$

$$b\_{t} = a\_{0} + a\_{1}b\_{t-1} + \dots + a\_{p}b\_{t-p} + \beta\_{1}c\_{t-1} + \dots + \beta\_{p}c\_{-p} + u\_{t} \tag{7}$$

Thus, a first step in the present study is the examination of the stationarity of the selected variables, which is an important stage in any econometric study. This will be verified by the ADF test, much like Bai et al. (2021) and Chen et al. (2021). Next, we intend to model BSE volatility through the GARCH model and identify the types of causality that are established between BSE and COVID-19 variables through the Granger causality test after VAR estimation.

The research hypotheses are formulated as follows:

**Hypothesis 1 (H1).** *The COVID-19 pandemic negatively influences the return of the Bucharest Exchange Trading Index.*

**Hypothesis 2 (H2).** *The COVID-19 pandemic adversely impacts the return of the companies traded on the Bucharest Stock Exchange.*

#### **4. Empirical Results**

#### *4.1. Preliminary Statistics*

Table 3 shows descriptive statistics for the daily logarithmic returns of the shares traded on BSE, as well as for the BET stock market index, whereas Figure 1 reveals the density graphs. The selected shares have a negative skewness (except for the TRP share) in line with Agarwalla et al. (2021), Banerjee (2021), Malik et al. (2021),Yousaf (2021), and Zhang and Hamori (2021). As a common condition of financial markets, skewness is negative, suggesting an asymmetry to the left.

**Table 3.** Descriptive statistics for daily logarithmic returns.


Source: Authors' calculations. Notes: Variables' descriptions are provided in Table 2.

**Figure 1.** Density plots for daily logarithmic returns. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

The Kurtosis indicator measures the magnitude of the extreme values. Accordingly, in the current investigation, all the explored variables register a value of kurtosis greater than three. This fact indicates that the return series has fatter tails than the normal distribution, similar to Banerjee (2021), Bourghelle et al. (2021), Fakhfekh et al. (2021), Ftiti et al. (2021), Malik et al. (2021), Yu et al. (2021), and Zhang and Hamori (2021). This feature is referred to as leptokurtosis, which could be caused by volatility clustering.

Additionally, through the Jarque–Bera test, we can decide the distribution of variables. Consistent with the empirical results presented in Table 2, the probability accompanying the test is 0%. Hence, the test values are quite different from those of the normal distribution, proving that the series are not normally distributed, much like Curto and Serrasqueiro (2021), Liu et al. (2021a), Malik et al. (2021), Yousfi et al. (2021), and Zhang and Hamori (2021).

Figure 2 shows the Q–Q (quantile–quantile) plots. The quantiles–quantiles graph is a straightforward method used to compare two distributions. Therewith, it signifies the graph of an empirical distribution versus a theoretical distribution (normal distribution). If the empirical distribution is normal, the subsequent Q–Q graph should be the first bisector. However, in current investigation, the distribution is very different from the normal one.

**Figure 2.** Q–Q plots for daily logarithmic returns. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

The density graph (see Figure 1) and Q–Q plot (see Figure 2) against the normal distribution show that the returns distribution also exhibits fat tails confirming the results in Table 2.

Further, we studied the stationarity of stocks and the stock market index using the ADF (Augmented Dickey–Fuller) test, much like Abuzayed et al. (2021), Atri et al. (2021), Banerjee (2021), Bora and Basistha (2021), Insaidoo et al. (2021), Li (2021), Yousaf (2021), Yousfi et al. (2021), and Zhang and Hamori (2021). ADF test is a very common method of assessing stationarity. The null hypothesis of the test is that the analyzed data series is not stationary and has a root unit. The outcomes of ADF test are revealed in Table 4.


**Table 4.** ADF test results for daily logarithmic returns.

Source: Authors' calculations. Notes: Intercept included in test equation. Lag length: Automatic selection based on Schwarz Info Criterion. Variables' descriptions are provided in Table 2.

According to the results presented by the ADF stationarity test in Table 4, the null hypothesis of a unit root can be rejected, indicating that the daily logarithmic returns are significant at the 1% level, hence stationary, similar to Bai et al. (2021) and Yu et al. (2021). Thus, taking into account the empirical results of the ADF stationarity test, the examined variables are stationary and have an integration order I (0). Likewise, the stationarity of the series can be seen in Figure 3, where the daily yields of the analyzed series are represented.

**Figure 3.** Daily values of the logarithmic returns. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

Figure 3 shows the evolution of the selected returns. Hence, there is acknowledged a phenomenon of "volatility clustering" and an alternation between periods of low volatility and those with high volatility, similar to Abuzayed et al. (2021), Insaidoo et al. (2021), Malik et al. (2021), and Yousfi et al. (2021). Moreover, "volatility clustering" implies a strong autocorrelation of returns.

Figures 4–6 reveal the evolution of the BET index against the new cases of COVID-19. The relationship between the evolution of the BET index and the number of new SARS-CoV-2 cases (USA, Italy, and Romania) is an indirect one. Thus, the increase in the number of infections (USA, Italy, and Romania) determined a decrease in the local stock market index and its return.

**Figure 4.** The evolution of the daily BET index quotes vs. no. of SUA new cases of SARS-COV-2. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

**Figure 5.** The evolution of the daily BET index quotes vs. no. of Italy new cases of SARS-COV-2. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

**Figure 6.** The evolution of the daily BET index quotes vs. no. of Romania new cases of SARS-COV-2. Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

#### *4.2. GARCH Outcomes*

Before applying GARCH models, it is advisable to perform preliminary tests to detect the effects of ARCH. Heteroskedasticity was investigated by determining autocorrelation (AC), partial autocorrelation (PAC), and Q test. The number of offsets used for all the time series was 20. The outcomes of AC, PAC and Q-Stat are revealed in Table 5.

**Table 5.** Estimated autocorrelation (AC), partial autocorrelation (PAC) and Q-statistics with 20 lags for daily squared returns.


Source: Authors' own work. Notes: Variables' descriptions are provided in Table 2.

According to the results of the Q test, in most of the cases, the existence of the serial correlation, heteroscedasticity (*p*-value less than 5%), is confirmed. However, in the case of TRP, the probability is greater than 5% and the null hypothesis of the absence of the serial correlation up to lag 20 cannot be rejected. Therefore, the data series shows heteroscedasticity that can be modelled by GARCH models (except TRP, because heteroskedasticity is a pre-condition for applying GARCH models for financial time series, where we may not be able to match GARCH models).

Table 6 shows 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.

**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 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.

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 COVID-19 pandemic.


 **6.** GARCH estimations.

**Table**



338. Coefficient

 covariance

 computed

 using outer product of gradients.

 Presample

 variance: backcast (parameter

 = 0.7).

**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 7 exhibits 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.

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 8 reveals the related lag order selection criteria. Hence, the Schwarz information criterion suggests five and seven lags.


**Table 8.** VAR lag order selection criteria.

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 *Y* based on its own past values and on the previous values of *X* are better than the predictions of *Y* based only on *Y*'s own past values. Table 9 shows 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.


**Table 9.** The results of the VAR Granger causality/block exogeneity Wald tests.

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 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.



**Table A1.** *Cont.*

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.


**Table A2.** Vector autoregression estimates for stock market index and the number of new cases of COVID-19 in Italy.

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.


**Table A3.** Vector autoregression estimates for stock market index and the number of new cases of COVID-19 in the US.

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.

#### **References**

