Economic Policy Uncertainty and Sectoral Trading Volume in the U.S. Stock Market: Evidence from the COVID-19 Crisis

Pak, Dohyun; Choi, Sun-Yong

doi:https://doi.org/10.1155/2022/2248731

Complexity

On this page

Abstract Introduction Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Complexity Arising in Financial Modelling and its Applications

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 2248731 | https://doi.org/10.1155/2022/2248731

Economic Policy Uncertainty and Sectoral Trading Volume in the U.S. Stock Market: Evidence from the COVID-19 Crisis

Dohyun Pak¹and Sun-Yong Choi¹

Academic Editor: Sameh S. Askar

Received26 May 2021

Accepted22 Mar 2022

Published25 Apr 2022

Abstract

We empirically analyze the impact of economic uncertainty due to the COVID-19 pandemic on the trading volume of each sector in the S&P 500 index. Wavelet coherence analysis is carried out using economic policy uncertainty data and the trading volume of each sector in the S&P 500 index from July 2004 to September 2020. Furthermore, we apply multifractal detrended fluctuation (MF-DFA) analysis to the trading volume series of all sectors. The wavelet coherence analysis shows that the COVID-19 pandemic has substantially influenced trading volume in all sectors. However, the impact of the pandemic is different from that during the global financial crisis in some sectors, such as information technology, consumer discretionary, and communication services. Because of the lockdown taken to suppress COVID-19, increased remote working and remote learning are the main reasons for these results. Additionally, according to the MF-DFA analysis, the trading volume of all the sectors has clear multifractal characteristics, and they are all nonpersistent. Specifically, trading volumes of the real estate and materials sector are highly correlated, whereas the trading volumes of industry and information technology sectors are comparatively less correlated.

1. Introduction

Trading volume has long been a major concern in finance. For example, many studies have reported that trading volume has a relationship with returns and the absolute value of returns (Crouch [1]; Copeland [2]; Karpoff [3]; Jones et al. [4]; Foster [5]; Kramer [6]; Wang and Yau [7]; Chen et al. [8]; Gagnon and Karolyi [9]; Lin [10]; Wang et al. [11]). According to these studies, there is a positive relationship between trading volume and stock returns. Similarly, the trading volume has also been investigated in terms of volatility (Karpoff [3]; Foster [5]; Lee and Rui [12]; Güner and Önder [13]; Li and Wu [14]; Wen and Yang [15]; Rossi and De Magistris [16]; Darolles et al. [17]; Clements and Neda [18]; Ftiti et al. [19]; Kao et al. [20]; Khuntia and Pattanayak [21]). One of the important motivations of these studies is that the volume of transactions represents the scale and rate of information flow to the stock market (Wang and Yau [7]; Clements and Neda [18]; Ftiti et al. [19]). That is, the trading volume captures the most important information about market participants’ trading activities. Recently, several studies have investigated the relationship between changes in trading volume and uncertainty (Choi [22]; Rehse et al. [23]; Nagar et al. [24]; Chen et al. [25]; Chiah and Zhong [26]). In particular, Chiah and Zhong [26] and Chen et al. [25] examined how changes in the financial markets caused by the coronavirus disease 2019 (COVID-19) pandemic affect the trading volume. Meanwhile, numerous mathematical models are also used to investigate and control the COVID-19 pandemic. First, many studies describe the main features of the COVID-19 pandemic using the susceptible-infected-removed (SIR) models (Colombo et al. [27]; Tian et al. [28]; Alshomrani et al. [29]; Leung et al. [30]; Read et al. [31]; Wu et al. [32]; Yang et al. [33]). In these studies, the SIR-type models are fitted to the actual data, and the reproductive number was estimated (Read et al. [31]). Furthermore, the COVID-19 pandemic peaks and sizes are predicted based on the SIR-type models (Yang et al. [33]). Second, agent-based models have been used to capture the interaction structure of the underlying populations for the COVID-19 pandemic (Adiga et al. [34]). For example, Agrawal et al. [35] build an agent-based simulator to study the impact of various nonpharmaceutical interventions in the COVID-19 pandemic and demonstrate the ability of simulators through several case studies. Gharakhanlou and Hooshangi [36] develop an agent-based model that simulates the spatio-temporal outbreak of COVID-19. Additionally, they simulate the transmission of COVID-19 between human agents based on one of the SIR-type models. Third, there are studies on developing new mathematical models for COVID-19. For example, Matouk [37] suggests a susceptible-infected model with a multi-drug resistance, called SIMDR. They also investigated the dynamic behavior of the SIMDR model for the COVID-19 pandemic. Mohammed et al. [38] examine the dynamic behavior of COVID-19 using Lotka–Volterra-based models. Particularly, their proposed models contain fractional derivatives, which present a more sufficient and realistic description of the COVID-19 phenomena. In this study, we examine the impact of economic uncertainty on the trading volume of the U.S. stock market. We employ the U.S. daily news-based economic policy uncertainty (EPU) index to measure economic uncertainty. To do that, we calculate industry-specific trading volume and investigate the relationship between the trading volume of each industry and EPU. Furthermore, we investigate the multifractal nature of the industry-specific trading volume. Based on this investigation, we analyze the fluctuations of trading volumes. Industry-specific trading volume is defined based on the trading volume of 11 S&P 500 index sectors. We apply wavelet coherence analysis to estimate the interdependence and causality between EPU and each sector’s trading volume from January 2008 to September 2020. Furthermore, we examine the relationship between them in terms of several events during the sample period such as the global financial crisis (GFC) and COVID-19 pandemic. Recently, many studies have investigated the relationship between EPU and volatility of various financial assets, such as the stock market (Ko and Lee [39]; Liu and Zhang [40]; Li et al. [41]; Choi [42], oil Mei et al. [43]; Ma et al. [44]; Wen et al. [45], foreign exchange Juhro and Phan [46]; Bartsch [47]; Chen et al. [48], and cryptocurrency Demir et al. [49]; Wang et al. [50]; Cheng and Yen [51]). Unlike the previous literature, studies of the relationship between the trading volume and EPU are relatively scarce. To the best of our knowledge, this is the first report on the relationship between EPU and trading volume. Furthermore, we employ the multifractal detrended fluctuation analysis (MF-DFA) approach introduced by Kantelhardt et al. [52] to investigate long-range autocorrelations and describe the multifractal properties of the trading volume. Several studies show that stock markets are multifractal (Bacry et al. [53]; Kwapień et al. [54]; Zunino et al. [55]; Wang et al. [56]; Machado [57]; Choi [58]). The contributions of this study are threefold: first, it adds to the flourishing strand of the literature on the impact of COVID-19 on the U.S. stock market (Mazur et al. [59]; Sharif et al. [60]; Hanke et al. [61]; Smales [62]; Baker et al. [63]). Second, our study extends the literature by examining the change in trading volume at the industry level following extreme events. In particular, while some studies have examined the relationship between the effect of the COVID-19 pandemic and trading volume of individual stocks or the stock market in each country (Ortmann et al. [64]; Chiah and Zhong [26]), no studies have addressed the trading volume of each sector. Third, we inspect whether the trading volumes for all sectors have multifractal characters. The investigation of the multifunctional nature of the trading volume at the industrial level is also not adequately explored in the existing literature. The remainder of this study is organized as follows: Section 2 describes the data and reviews the wavelet coherence analysis and MF-DFA approaches. Section 3 presents the main findings. Finally, concluding remarks are provided in Section 4.

2. Data Description and Methodology

2.1. Data Description

The time series of EPU is obtained from https://www.policyuncertainty.com. This website presents data on the news-based EPU index proposed by Baker et al. [65]. The sample period runs from July 2004 to September 2020. The index measures EPU using information from keyword searches in 10 large newspapers and is normalized to the volume of news articles discussing EPU.

Figure 1 shows the monthly time series of EPU and total trading volume (the sum of the trading volume of all the shares included in the S&P 500 index) during the sample period and several events that shocked the market such as the Lehman bankruptcy, debt-ceiling crisis, trading tensions between the United States and China, and the COVID-19 pandemic. As can be seen, the EPU index during the pandemic is significantly higher than in other events. In addition, changes in total trading volume tend to be similar to changes in EPU. About 500 companies in the U.S. stock market are used to define the S&P 500 index, which has 11 sectors in total (we use the global industry classification standard). The market cap of the S&P 500 is 70–80% of total U.S. stock market capitalization. Consequently, the sectors of the index naturally become a classification criterion for the U.S. economy. To calculate the trading volume of each sector, we first define the daily average sectoral trading volume of the -th sector at time as follows:where is the total number of stocks (the total number of shares (N) changes as the incorporated stock in the i-th sector changes) in the sector at time and is the trading volume of the -th stock in the -th sector at time . Because the EPU is calculated monthly, we define the monthly average trading volume (MATV) for ,\{ July 2004, August 2004, September 2020\} as the sum of average daily trading volume in each month. Table 1 presents the summary statistics of MATV. In addition, the MATV in each sector is shown in Figure 2. According to Table 1andFigure 2, the MATV of the IT and financial industries is large and the fluctuation of MATV is also large. On the contrary, the MATV of the utilities and real estate sectors is smaller than those of the other industries and their changes are also small. Furthermore, during both the GFC and the COVID-19 pandemic, there is a considerable change in trading volume in all sectors. According to the World Health Organization (WHO) (“Tracking SARS-CoV-2 variants” https://www.who.int/en/activities/tracking-SARS-CoV-2-variants), several COVID-19 variants have been observed, namely, alpha, beta, gamma, and delta. As they were all officially designated after December 2020, our sample data do not include the impact of the new COVID-19 variants on the EPU and the MATV. Therefore, we provide the extended EPU and MATV, that is, from January 2020 to June 2021, in Figures 3 and 4. In Figure 3, the designation date of COVID-19 variances is indicated. When looking at the plots, the EPU and the volume do not seem to have been significantly affected by the occurrence of COVID-19 variances. Furthermore, MATVs in all sectors do not appear to be significantly related to the COVID-19 variants. However, it is noteworthy that the MATV of the energy sector from January 2020, except for a few periods, is the largest among the MATVs of all sectors. This is largely different from the MATV results before 2020 in Figure 2. We use monthly EPU and the monthly sample data during the COVID-19 pandemic are not enough to apply to the wavelet coherency analysis. To solve this problem, a short-term sample data set is needed, such as weekly or daily data. Additionally, a longer study period may capture the impact of the new COVID-19 variants. These are opportunities for future studies.

2.2. Methodology

2.2.1. Wavelet Coherence Analysis

Using wavelet coherence analysis with the Morlet specification, we investigate the causality and interdependence between the EPU and trading volume. Based on this analysis, we make inferences in a time-frequency frame. From several studies (Ko and Lee [39]; Kristoufek [66]; Pal and Mitra [67]; Sharif et al. [60]), this can be briefly explained as follows: For time series , the continuous wavelet transform is given by the following equation:where is the scaling factor adjusting the length of the wavelet and is the translation parameter adjusting the wavelet location in time. is the complex conjugate function of . In addition, is found by scaling and shifting the mother wavelet . According to Soares et al. [68], we choose the Morlet wavelet suggested by Goupillaud et al. [69] as the mother wavelet :

For the and time series, the cross-wavelet transform is defined as follows:

From the cross-wavelet transform, the wavelet coherence between two series, and , is given by Torrence and Webster [70]:where is the smooth operator in time and scale. is a squared correlation localized in time frequency and . Based on Bloomfield et al. [71], the phase difference from the phase angle obtained by the cross-wavelet transform is as follows:where and are the real and imaginary parts of the smooth cross-wavelet transform, respectively. can explain the interdependence and causality between the two time series, and , while the squared wavelet coherence does not know the direction of the relationship. Based on several studies (Flor and Klarl [72]; Cai et al. [73]; Funashima [74]), we can determine the connection between the two time series, and , by understanding the scale of the phase difference, . If , and have positive relations, and leads . If , lags . For , and have negative relations, but lags . If , the two time series also have negative relations, with leading .

2.2.2. Multifractal Detrended Fluctuation Analysis

The MF-DFA method represents the multifractal properties of a financial time series. According to Kantelhardt et al. [52], the MF-DFA procedure consists of the following five steps (Wang et al. [75]). Let be a time series, where is the length of the series:(i)Step 1. Determine the profile where(ii)Step 2. Divide the profile into nonoverlapping segments of equal length . To cover the whole sample, repeat the same procedure from the end of the sample. In this way, segments are obtained altogether:(iii)Step 3. Calculate the local trend for each of the segments. For every segment, the local trend is estimated by a least-square fitting polynomial. Consequently, the variance is determined as follows: Here, is the fitting polynomial with order in segment . In this study, we adopt a linear polynomial to prevent overfitting and facilitate the calculation (Lashermes et al. [76]; Ning et al. [77]).(iv)Step 4. Average over all the segments. Then, we obtain the -th order fluctuation function:(v)Step 5. Determine the scaling behavior of the fluctuation functions. Compare the log-log plots with for each value of . If the series are long-range power-law correlated, increases for high values of . The power law is expressed as follows: where represents the generalized Hurst exponent. Equation (12) can be written as . After taking the logarithms of both sides, where is a constant.

The exponent depends on . The time series is monofractal when does not depend on ; otherwise, it is multifractal. For is identical to the Hurst exponent Calvet and Fisher [78]. Thus, the function is called a generalized Hurst exponent. If , the time series are not correlated, and it follows a random-walk process. When , the time series is long-range dependent, and an increase (decrease) is more likely to be followed by another increase (decrease). means a nonpersistent series; that is, an increase (decrease) is more likely to be followed by a decrease (increase). According to Kantelhardt et al. [52], relates to the multifractal scaling exponents as follows:

To estimate multifractality, we transform and to and using a Legendre transform with the following equations:where is the multifractal spectrum or singularity spectrum, and is the singularity strength. Furthermore, we define the degree of multifractality as follows (Yuan et al. [79]; Antônio et al. [80]; Ruan et al. [81]):

In addition, we define the width of the multifractal spectrum as follows (Wang et al. [82]; Antônio et al. [80]; Ruan et al. [81]):

A larger value indicates a stronger degree of multifractality and a wider multifractal spectrum, implying a stronger degree of multifractality. As another important feature of the multifractal spectrum (Drożdż and Oświcimka [83]; Maiorino et al. [84]; Drożdż et al. [85]; Wa̧torek et al. [86]), we define the asymmetric parameter as follows:where . Here, is the value at the maximum of . The asymmetric parameter estimates the asymmetry of the spectrum and determines the dominance of small and large fluctuations for the multifractal spectrum. When the asymmetric parameter , both large and small fluctuations lead fairly to multifractality. In addition, exhibits left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum. Conversely, exhibits right-sided asymmetry in the spectrum, thus indicating that smaller fluctuations constitute a dominant multifractality source.

3. Empirical Analysis

3.1. Wavelet Analysis

In this subsection, we provide the wavelet coherence between EPU and MATV for each sector to investigate the interdependence between them. Figures 5 and 6 present the estimated wavelet coherence and relative phasing of the two series represented by arrows. An explanation for wavelet coherence analysis is provided in previous studies (Torrence and Webster [70]; Tiwari [87]; Lu et al. [88]; Pal and Mitra [67]). Based on the wavelet coherence analysis results, our main findings are summarized as follows: first, in the figures, the red areas are mainly observed in the GFC and COVID-19 pandemic periods, which indicates strong interdependence between EPU and MATV. In other words, during the GFC and COVID-19 pandemic periods, the EPU and sectoral trading volume have noteworthy interdependence in most sectors. In times other than these two events, while several sectors display a common strong interconnection, the heavy linkage is short. Second, during the pandemic, in most sectors, the MATV has a different relationship with EPU than during the GFC. In particular, the red area in the consumer discretionary, energy, and utility industries is larger during the pandemic than in the GFC. Therefore, the pandemic has a greater influence on the MATV of industries than the GFC. Third, on the contrary, in the communication services, consumer staples, information technology, and materials sectors, the impact of the GFC on trading volume is greater. One of the reasons may be that some sectors such as technology and e-commerce are more profitable than before because of the pandemic (“Winners from the pandemic Big tech’s covid-19 opportunity,” The Economist, https://www.economist.com/leaders/2020/04/04/big-techscovid-19-opportunity).

(a)

(b)

(c)

(d)

(e)

(f)

(a)

(b)

(c)

(d)

(e)

3.2. Multifractal Analysis

In this subsection, we apply MF-DFA to the MATV series to investigate the fractal nature of the MATV series, based on the degree of multifractality and the width of the multifractal spectrum . First, we display the log-log plots of compared to for all the MATV series for , corresponding to the curve from the bottom to the top when the polynomial order in Figure 7. According to the plots, we obtain the presence of different scaling laws and exponents.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

Second, we further show the generalized Hurst exponents of the MATV series, as shown in Figure 8. As shown in Figure 8, the generalized Hurst exponent of the MATV series decreases as increases from to 5 in all sectors. This implies that the MATV of all sectors has obvious multifractal features. Additionally, all sectors’ Hurst exponents (= ) are smaller than 0.5. This indicates that the MATV series of all sectors are nonpersistent.

Third, Figure 9 and Table 2 illustrate the multifractal spectra and the degree of multifractality and width of the multifractal spectra of all the MATV series, respectively. Regarding the degrees of multifractality ( and ) given in Table 2, the real estate and materials sectors have the first and second-largest degrees of multifractality, respectively. Meanwhile, the industry and information technology sectors have the first and second smallest degree of multifractality, respectively. This implies that the MATV of the real estate and materials sectors is more highly correlated, whereas the MATV of the industry and information technology sectors is less correlated. Finally, all MATV series have negative asymmetric parameters . In other words, the small fluctuations in MATV are more leading multifractality sources than the large fluctuations in the MATV series of all sectors.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

4. Concluding remarks

We present empirical evidence on the relationship between economic uncertainty about the COVID-19 pandemic and trading volume at the sector level. Furthermore, we compare the effect of this pandemic with the impact of the GFC in the United States. We employ the wavelet coherence analysis to measure the interrelation and causality between EPU and trading volume of each sector. According to the MF-DFA analysis, we examine the multifractality of the trading volume for all sectors. The empirical results provide a number of interesting conclusions. First, we find a strong positive correlation between EPU and MATV in all sectors in the middle term during the pandemic. In addition, the phase patterns indicate that EPU leads MATV in all sectors. Second, in terms of the impact of the market shock, some industries show different characteristics during the pandemic compared with the GFC. For example, in industries based on Internet technology such as the IT and communication services sectors, the impact of EPU is relatively small. Third, the impact of COVID-19 on the trading volume of the consumer discretionary and material sectors is longer and shorter than that during the GFC, respectively. According to an article (“Consumer discretionary and IT stocks are “egregiously expensive,” strategist says,” CNBC, https://www.cnbc.com/2020/12/04/avoid-expensive-consumer-discretionary-and-it-stocks-strategist-says.html), IT and consumer discretionary stocks have performed strongly since the outbreak of the COVID-19 pandemic, with more people working remotely and spending time at home due to lockdown restrictions. In particular, the MSCI World Consumer Discretionary Price Index has rocketed by 85% since mid-March 2020, while the MSCI World Information Technology Price Index has soared by over 75%. On the contrary, unlike during the GFC, there has been no sharp drop in housing prices during the pandemic; rather, housing prices have risen because of the Federal Reserve’s unprecedented monetary easing (Zhao [89]). Moreover, the materials sector is generally affected by the housing market. Therefore, these factors seem to have caused the difference in the materials sector. Finally, based on the MF-DFA results, the MATV of all the sectors has obvious multifractal features, and the small fluctuations in the MATV are a more dominant multifractality source. In addition, the MATV of the real estate and materials sectors is more highly correlated; meanwhile, the MATV of the industry and information technology sectors is less correlated. Our study contributes insights into the influence of the COVID-19 pandemic on the trading volume of the sectors in the U.S. stock market. The findings demonstrate that overall COVID-19 has affected trading volume considerably. However, some industries are not affected to the same degree as during the GFC. The reason for this difference could be the lockdown taken to prevent the spread of COVID-19 as well as the implementation of a 0% interest rate and unlimited quantitative easing (Donthu and Gustafsson [90]; Zhang et al. [91]). Moreover, our findings show that the trading volume series for all sectors has a multifractal nature. Compared to the existing literature that mainly conducted multifractal analysis on the trading volume of the financial market (Bolgorian and Raei [92]; Stosic et al. [93]; Zhang et al. [94]), this study has another contribution to examining the multifractal nature of the trading volume at the industry level. Finally, we mention a few directions for future research. First, as the COVID-19 pandemic has not been officially terminated, the data used in this study cannot reflect all the effects of the COVID-19 pandemic on the trading volume. Therefore, with the official closure of the COVID-19 pandemic, it is necessary to conduct a study on the entire COVID-19 pandemic period. If so, we can inspect the effect of the new variants of COVID-19 on the trading volume. Second, here, the multiplicity properties of the trading volume of sectors were investigated. According to the previous literature (Ané and Ureche-Rangau [95]; Cheng et al. [96]; Boudt and Petitjean [97]; Ong [98]; Ma et al. [99]; Ftiti et al. [100]), the trading volume is known to be highly related to the price and volatility of stocks. Future studies should examine the fractal relationship between trading volume and stock price or volatility based on an industrial level. Finally, as another measure for complexity, the entropy measure might be applied to the trading volume. The entropy measure properly describes the chaotic structure of the time series and it has been broadly used for financial data (Maasoumi and Racine [101]; Bentes and Menezes [102]; Stosic et al. [103]; Ahn et al. [104]; Machado [57]). Therefore, a study on the entropy measure for the trading volume can enhance our findings.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The work of S.-Y. Choi was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2019R1G1A1010278).

References

R. L. Crouch, “The volume of transactions and price changes on the New York stock exchange,” Financial Analysts Journal, vol. 26, no. 4, pp. 104–109, 1970.
View at: Publisher Site | Google Scholar
T. E. Copeland, “A model of asset trading under the assumption of sequential information arrival,” The Journal of Finance, vol. 31, no. 4, pp. 1149–1168, 1976.
View at: Publisher Site | Google Scholar
J. M. Karpoff, “The relation between price changes and trading volume: a survey,” Journal of Financial and Quantitative Analysis, vol. 22, pp. 109–126, 1987.
View at: Publisher Site | Google Scholar
C. M. Jones, G. Kaul, and M. L. Lipson, “Transactions, volume, and volatility,” Review of Financial Studies, vol. 7, no. 4, pp. 631–651, 1994.
View at: Publisher Site | Google Scholar
A. J. Foster, “Volumevolatility relationships for crude oil futures markets,” Journal of Futures Markets, vol. 15, no. 18, p. 929, 1995.
View at: Publisher Site | Google Scholar
C. Kramer, “Noise trading, transaction costs, and the relationship of stock returns and trading volume,” International Review of Economics & Finance, vol. 8, no. 4, pp. 343–362, 1999.
View at: Publisher Site | Google Scholar
G. H. Wang and J. Yau, “Trading volume, bid–ask spread, and price volatility in futures markets,” Journal of Futures Markets: Futures, Options, and Other Derivative Products, vol. 20, no. 10, pp. 943–970, 2000.
View at: Publisher Site | Google Scholar
G.-m. Chen, M. Firth, and O. M. Rui, “The dynamic relation between stock returns, trading volume, and volatility,” The Financial Review, vol. 36, no. 3, pp. 153–174, 2001.
View at: Publisher Site | Google Scholar
L. Gagnon and G. A. Karolyi, “Information, trading volume, and international stock return comovements: evidence from cross-listed stocks,” Journal of Financial and Quantitative Analysis, vol. 44, pp. 953–986, 2009.
View at: Publisher Site | Google Scholar
H.-Y. Lin, “Dynamic stock return–volume relation: evidence from emerging asian markets,” Bulletin of Economic Research, vol. 65, no. 2, pp. 178–193, 2013.
View at: Publisher Site | Google Scholar
Z. Wang, Y. Qian, and S. Wang, “Dynamic trading volume and stock return relation: does it hold out of sample?” International Review of Financial Analysis, vol. 58, pp. 195–210, 2018.
View at: Publisher Site | Google Scholar
C. F. Lee and O. M. Rui, “Does trading volume contain information to predict stock returns? evidence from China’s stock markets,” Review of Quantitative Finance and Accounting, vol. 14, no. 4, pp. 341–360, 2000.
View at: Publisher Site | Google Scholar
N. Güner and Z. Önder, “Information and volatility: evidence from an emerging market,” Emerging Markets Finance and Trade, vol. 38, 2002.
View at: Google Scholar
J. Li and C. Wu, “Daily return volatility, bid-ask spreads, and information flow: analyzing the information content of volume,” Journal of Business, vol. 79, no. 5, pp. 2697–2739, 2006.
View at: Publisher Site | Google Scholar
F. Wen and X. Yang, “Empirical study on relationship between persistence-free trading volume and stock return volatility,” Global Finance Journal, vol. 20, no. 2, pp. 119–127, 2009.
View at: Google Scholar
E. Rossi and P. S. De Magistris, “Long memory and tail dependence in trading volume and volatility,” Journal of Empirical Finance, vol. 22, pp. 94–112, 2013.
View at: Publisher Site | Google Scholar
S. Darolles, G. Le Fol, and G. Mero, “Measuring the liquidity part of volume,” Journal of Banking & Finance, vol. 50, pp. 92–105, 2015.
View at: Publisher Site | Google Scholar
A. E. Clements and T. Neda, “Information flow, trading activity and commodity futures volatility,” Journal of Futures Markets, vol. 36, no. 1, pp. 88–104, 2016.
View at: Publisher Site | Google Scholar
Z. Ftiti, F. Jawadi, and W. Louhichi, “Modelling the relationship between future energy intraday volatility and trading volume with wavelet,” Applied Economics, vol. 49, no. 20, pp. 1981–1993, 2017.
View at: Publisher Site | Google Scholar
Y.-S. Kao, H.-L. Chuang, and Yu-C. Ku, “The empirical linkages among market returns, return volatility, and trading volume: evidence from the s&p 500 vix futures,” The North American Journal of Economics and Finance, vol. 54, Article ID 100871, 2019.
View at: Google Scholar
S. Khuntia and J. K. Pattanayak, “Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume,” Finance Research Letters, vol. 32, Article ID 101077, 2020.
View at: Publisher Site | Google Scholar
H. M. Choi, “Market uncertainty and trading volume around earnings announcements,” Finance Research Letters, vol. 30, pp. 14–22, 2019.
View at: Publisher Site | Google Scholar
D. Rehse, R. Ryan, N. Rottke, and J. Zietz, “The effects of uncertainty on market liquidity: evidence from hurricane sandy,” Journal of Financial Economics, vol. 134, no. 2, pp. 318–332, 2019.
View at: Publisher Site | Google Scholar
V. Nagar, S. Jordan, and L. Wellman, “The effect of economic policy uncertainty on investor information asymmetry and management disclosures,” Journal of Accounting and Economics, vol. 67, no. 1, pp. 36–57, 2019.
View at: Publisher Site | Google Scholar
C. Chen, L. Liu, and N. Zhao, “Fear sentiment, uncertainty, and bitcoin price dynamics: the case of covid-19,” Emerging Markets Finance and Trade, vol. 56, no. 10, pp. 2298–2309, 2020a.
View at: Publisher Site | Google Scholar
M. Chiah and A. Zhong, “Trading from home: the impact of covid-19 on trading volume around the world,” Finance Research Letters, vol. 37, Article ID 101784, 2020.
View at: Publisher Site | Google Scholar
R. M. Colombo, M. Garavello, F. Marcellini, and E. Rossi, “An age and space structured sir model describing the covid-19 pandemic,” Journal of mathematics in industry, vol. 10, no. 1, pp. 1–20, 2020.
View at: Publisher Site | Google Scholar
H. Tian, Y. Liu, Y. Li et al., “An investigation of transmission control measures during the first 50 days of the covid-19 epidemic in China,” Science, vol. 368, no. 6491, pp. 638–642, 2020.
View at: Publisher Site | Google Scholar
A. S. Alshomrani, M. Z. Ullah, and B. Dumitru, “Caputo sir model for covid-19 under optimized fractional order,” Advances in Difference Equations, vol. 2021, no. 1, pp. 1–17, 2021.
View at: Publisher Site | Google Scholar
K. Leung, J. T. Wu, Di Liu, and G. M. Leung, “First-wave covid-19 transmissibility and severity in China outside hubei after control measures, and second-wave scenario planning: a modelling impact assessment,” The Lancet, vol. 395, no. 10233, pp. 1382–1393, 2020.
View at: Publisher Site | Google Scholar
J. M. Read, J. R. Bridgen, D. A. Cummings, A. Ho, and C. P. Jewell, “Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates B,” Philosophical Transactions of the Royal Society, vol. 376, p. 1829, 2020.
View at: Google Scholar
J. T. Wu, K. Leung, and G. M. Leung, “Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in wuhan, China: a modelling study,” The Lancet, vol. 395, no. 10225, pp. 689–697, 2020.
View at: Publisher Site | Google Scholar
Z. Yang, Z. Zeng, Ke Wang et al. et al., “Modified seir and ai prediction of the epidemics trend of covid-19 in China under public health interventions,” Journal of Thoracic Disease, vol. 12, no. 3, p. 165, 2020.
View at: Publisher Site | Google Scholar
A. Adiga, D. Dubhashi, B. Lewis, M. Marathe, V. Srinivasan, and A. Vullikanti, “Mathematical models for covid-19 pandemic: a comparative analysis,” Journal of the Indian Institute of Science, vol. 1–15, 2020.
View at: Publisher Site | Google Scholar
S. Agrawal, S. Bhandari, A. Bhattacharjee et al., “City-scale agent-based simulators for the study of non-pharmaceutical interventions in the context of the covid-19 epidemic,” Journal of the Indian Institute of Science, vol. 1–39, 2020.
View at: Publisher Site | Google Scholar
N. M. Gharakhanlou and N. Hooshangi, “Spatio-temporal simulation of the novel coronavirus (covid-19) outbreak using the agent-based modeling approach (case study: urmia, Iran),” Informatics in Medicine Unlocked, vol. 20, Article ID 100403, 2020.
View at: Publisher Site | Google Scholar
A. E. Matouk, “Complex dynamics in susceptible-infected models for covid-19 with multi-drug resistance,” Chaos, Solitons & Fractals, vol. 140, Article ID 110257, 2020.
View at: Publisher Site | Google Scholar
W. W. Mohammed, E. S. Aly, A. E. Matouk, S. Albosaily, and E. M. Elabbasy, “An analytical study of the dynamic behavior of lotka-volterra based models of covid-19,” Results in Physics, vol. 26, Article ID 104432, 2021.
View at: Publisher Site | Google Scholar
J.-H. Ko and C.-M. Lee, “International economic policy uncertainty and stock prices: wavelet approach,” Economics Letters, vol. 134, pp. 118–122, 2015.
View at: Publisher Site | Google Scholar
Li Liu and T. Zhang, “Economic policy uncertainty and stock market volatility,” Finance Research Letters, vol. 15, pp. 99–105, 2015.
View at: Publisher Site | Google Scholar
T. Li, F. Ma, X. Zhang, and Y. Zhang, “Economic policy uncertainty and the Chinese stock market volatility: novel evidence,” Economic Modelling, vol. 87, pp. 24–33, 2020.
View at: Publisher Site | Google Scholar
S.-Y. Choi, “Industry volatility and economic uncertainty due to the covid-19 pandemic: evidence from wavelet coherence analysis,” Finance Research Letters, vol. 37, p. 101783, 2020.
View at: Publisher Site | Google Scholar
D. Mei, Q. Zeng, X. Cao, and X. Diao, “Uncertainty and oil volatility: new evidence,” Physica A: Statistical Mechanics and Its Applications, vol. 525, pp. 155–163, 2019.
View at: Publisher Site | Google Scholar
R. Ma, C. Zhou, H. Cai, and C. Deng, “The forecasting power of epu for crude oil return volatility,” Energy Reports, vol. 5, pp. 866–873, 2019.
View at: Publisher Site | Google Scholar
F. Wen, Y. Zhao, M. Zhang, and C. Hu, “Forecasting realized volatility of crude oil futures with equity market uncertainty,” Applied Economics, vol. 51, no. 59, pp. 6411–6427, 2019.
View at: Publisher Site | Google Scholar
S. M. Juhro and D. H. B. Phan, “Can economic policy uncertainty predict exchange rate and its volatility? evidence from asean countries,” Buletin Ekonomi Moneter dan Perbankan, vol. 21, no. 2, pp. 251–268, 2018.
View at: Google Scholar
Z. Bartsch, “Economic policy uncertainty and dollar-pound exchange rate return volatility,” Journal of International Money and Finance, vol. 98, Article ID 102067, 2019.
View at: Publisher Site | Google Scholar
L. Chen, Z. Du, and Z. Hu, “Impact of economic policy uncertainty on exchange rate volatility of China,” Finance Research Letters, vol. 32, Article ID 101266, 2020b.
View at: Publisher Site | Google Scholar
E. Demir, G. Gozgor, C. K. M. Lau, and S. A. Vigne, “Does economic policy uncertainty predict the bitcoin returns? an empirical investigation,” Finance Research Letters, vol. 26, pp. 145–149, 2018.
View at: Publisher Site | Google Scholar
G.-J. Wang, C. Xie, D. Wen, and L. Zhao, “When bitcoin meets economic policy uncertainty (epu): measuring risk spillover effect from epu to bitcoin,” Finance Research Letters, vol. 31, 2019b.
View at: Publisher Site | Google Scholar
H.-P. Cheng and K.-C. Yen, “The relationship between the economic policy uncertainty and the cryptocurrency market,” Finance Research Letters, vol. 35, Article ID 101308, 2020.
View at: Publisher Site | Google Scholar
J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, “Multifractal detrended fluctuation analysis of nonstationary time series,” Physica A: Statistical Mechanics and Its Applications, vol. 316, no. 1-4, pp. 87–114, 2002.
View at: Publisher Site | Google Scholar
E. Bacry, D. Jean, and M. Jean-François, “Modelling financial time series using multifractal random walks,” Physica A: Statistical Mechanics and Its Applications, vol. 299, no. 1-2, pp. 84–92, 2001.
View at: Publisher Site | Google Scholar
J. Kwapień, P. Oświcimka, and S. Drożdż, “Components of multifractality in high-frequency stock returns,” Physica A: Statistical Mechanics and Its Applications, vol. 350, no. 2-4, pp. 466–474.
View at: Google Scholar
L. Zunino, A. Figliola, B. M. Tabak, D. G. Pérez, M. Garavaglia, and O. A. Rosso, “Multifractal structure in Latin-american market indices,” Chaos, Solitons & Fractals, vol. 41, no. 5, pp. 2331–2340, 2009.
View at: Publisher Site | Google Scholar
Y. Wang, L. Liu, and R. Gu, “Analysis of efficiency for shenzhen stock market based on multifractal detrended fluctuation analysis,” International Review of Financial Analysis, vol. 18, no. 5, pp. 271–276, 2009.
View at: Publisher Site | Google Scholar
J. A. T. Machado, “Fractal and entropy analysis of the dow jones index using multidimensional scaling,” Entropy, vol. 22, no. 10, p. 1138, 2020.
View at: Publisher Site | Google Scholar
S.-Y. Choi, “Analysis of stock market efficiency during crisis periods in the us stock market: differences between the global financial crisis and covid-19 pandemic,” Physica A: Statistical Mechanics and Its Applications, vol. 574, Article ID 125988, 2021.
View at: Publisher Site | Google Scholar
M. Mazur, M. Dang, and M. Vega, “Covid-19 and the march 2020 stock market crash. evidence from s&p1500,” Finance Research Letters, Article ID 101690, 2020.
View at: Google Scholar
A. Sharif, C. Aloui, and L. Yarovaya, “Covid-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the us economy: fresh evidence from the wavelet-based approach,” International Review of Financial Analysis, vol. 70, Article ID 101496, 2020.
View at: Publisher Site | Google Scholar
M. Hanke, M. Kosolapova, and A. Weissensteiner, “Covid-19 and market expectations: evidence from option-implied densities,” Economics Letters, vol. 195, Article ID 109441, 2020.
View at: Publisher Site | Google Scholar
L. A. Smales, “Investor attention and the response of us stock market sectors to the covid-19 crisis,” Review of Behavioral Finance, 2020.
View at: Publisher Site | Google Scholar
B. S. R. Baker, N. Bloom, S. J. Davis, K. Kost, M. Sammon, and T. Viratyosin, “The unprecedented stock market reaction to covid-19,” The Review of Asset Pricing Studies, vol. 10, no. 4, pp. 742–758, 2020.
View at: Publisher Site | Google Scholar
R. Ortmann, M. Pelster, and S. Tobias Wengerek, “Covid-19 and investor behavior,” Finance Research Letters, vol. 37, Article ID 101717, 2020.
View at: Publisher Site | Google Scholar
B. S. R. Baker, N. Bloom, and S. J. Davis, “Measuring economic policy uncertainty,” Quarterly Journal of Economics, vol. 131, no. 4, pp. 1593–1636, 2016.
View at: Publisher Site | Google Scholar
L. Kristoufek, “What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis,” PLoS One, vol. 10, no. 4, Article ID e0123923, 2015.
View at: Publisher Site | Google Scholar
D. Pal and S. K. Mitra, “Oil price and automobile stock return co-movement: a wavelet coherence analysis,” Economic Modelling, vol. 76, pp. 172–181, 2019.
View at: Publisher Site | Google Scholar
M. J. Soares and M. J. Soares, “Business cycle synchronization and the euro: a wavelet analysis,” Journal of Macroeconomics, vol. 33, no. 3, pp. 477–489, 2011.
View at: Publisher Site | Google Scholar
P. Goupillaud, A. Grossmann, and J. Morlet, “Cycle-octave and related transforms in seismic signal analysis,” Geoexploration, vol. 23, no. 1, pp. 85–102, 1984.
View at: Publisher Site | Google Scholar
C. Torrence and P. J. Webster, “Interdecadal changes in the enso–monsoon system,” Journal of Climate, vol. 12, no. 8, pp. 2679–2690, 1999.
View at: Publisher Site | Google Scholar
D. S. Bloomfield, R. T. J. McAteer, B. W. Lites, P. G. Judge, M. Mathioudakis, and F. P. Keenan, “Wavelet phase coherence analysis: application to a quiet‐sun magnetic element,” The Astrophysical Journal, vol. 617, no. 1, pp. 623–632, 2004.
View at: Publisher Site | Google Scholar
M. A. Flor and T. Klarl, “On the cyclicity of regional house prices: new evidence for us metropolitan statistical areas,” Journal of Economic Dynamics and Control, vol. 77, pp. 134–156, 2017.
View at: Publisher Site | Google Scholar
X. J. Cai, S. Tian, N. Yuan, and S. Hamori, “Interdependence between oil and east asian stock markets: evidence from wavelet coherence analysis,” Journal of International Financial Markets, Institutions and Money, vol. 48, pp. 206–223, 2017.
View at: Publisher Site | Google Scholar
Y. Funashima, “Time-varying leads and lags across frequencies using a continuous wavelet transform approach,” Economic Modelling, vol. 60, pp. 24–28, 2017.
View at: Publisher Site | Google Scholar
F. Wang, X. Ye, and C. Wu, “Multifractal characteristics analysis of crude oil futures prices fluctuation in China,” Physica A: Statistical Mechanics and Its Applications, vol. 533, Article ID 122021, 2019a.
View at: Publisher Site | Google Scholar
B. Lashermes, P. Abry, and P. Chainais, “New insights into the estimation of scaling exponents,” International Journal of Wavelets, Multiresolution and Information Processing, vol. 2, no. 04, pp. 497–523, 2004.
View at: Publisher Site | Google Scholar
Y. Ning, Y. Wang, and C.-w. Su, “How did China’s foreign exchange reform affect the efficiency of foreign exchange market?” Physica A: Statistical Mechanics and Its Applications, vol. 483, pp. 219–226, 2017.
View at: Publisher Site | Google Scholar
L. Calvet and A. Fisher, “Multifractality in asset returns: theory and evidence,” The Review of Economics and Statistics, vol. 84, no. 3, pp. 381–406, 2002.
View at: Publisher Site | Google Scholar
Y. Yuan, X.-t. Zhuang, and X. Jin, “Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis,” Physica A: Statistical Mechanics and Its Applications, vol. 388, no. 11, pp. 2189–2197, 2009.
View at: Publisher Site | Google Scholar
C. d. S. F. Antônio, D. M. Natália, and E. Fonseca de Almeida, “Multifractal analysis of bitcoin market,” Physica A: Statistical Mechanics and Its Applications, vol. 512, pp. 954–967, 2018.
View at: Google Scholar
Q. Ruan, S. Zhang, D. Lv, and X. Lu, “Financial liberalization and stock market cross-correlation: mf-dcca analysis based on shanghai-Hong Kong stock connect,” Physica A: Statistical Mechanics and Its Applications, vol. 491, pp. 779–791, 2018.
View at: Publisher Site | Google Scholar
Y. Wang, C. Wu, and Z. Pan, “Multifractal detrending moving average analysis on the us dollar exchange rates,” Physica A: Statistical Mechanics and Its Applications, vol. 390, no. 20, pp. 3512–3523, 2011.
View at: Publisher Site | Google Scholar
S. Drożdż and P. Oświcimka, “Detecting and interpreting distortions in hierarchical organization of complex time series,” Physical Review, vol. 91, no. 3, Article ID 030902, 2015.
View at: Publisher Site | Google Scholar
E. Maiorino, F. Maria Bianchi, L. Livi, A. Rizzi, and A. Sadeghian, “Data-driven detrending of nonstationary fractal time series with echo state networks,” Information Sciences, vol. 382, pp. 359–373, 2017.
View at: Publisher Site | Google Scholar
S. Drożdż, R. Kowalski, P. Oświcimka, R. Rak, and R. Gbarowski, “Dynamical variety of shapes in financial multifractality,” Complexity, vol. 2018, Article ID 7015721, 13 pages, 2018.
View at: Publisher Site | Google Scholar
M. Wa̧torek, S. Drożdż, P. Oświcimka, and M. Stanuszek, “Multifractal cross-correlations between the world oil and other financial markets in 2012–2017,” Energy Economics, vol. 81, pp. 874–885, 2019.
View at: Google Scholar
A. K. Tiwari, “Oil prices and the macroeconomy reconsideration for Germany: using continuous wavelet,” Economic Modelling, vol. 30, pp. 636–642, 2013.
View at: Publisher Site | Google Scholar
Y. Lu, X. J. Cai, and S. Hamori, “Does the crude oil price influence the exchange rates of oil-importing and oil-exporting countries differently? a wavelet coherence analysis,” International Review of Economics & Finance, vol. 49, pp. 536–547, 2017.
View at: Google Scholar
Y. Zhao, “US housing market during COVID-19: aggregate and distributional evidence,” COVID Economics, vol. 50, pp. 113–154, 2020.
View at: Google Scholar
N. Donthu and A. Gustafsson, “Effects of covid-19 on business and research,” Journal of Business Research, vol. 117, p. 284, 2020.
View at: Publisher Site | Google Scholar
D. Zhang, M. Hu, and Q. Ji, “Financial markets under the global pandemic of covid-19,” Finance Research Letters, vol. 36, Article ID 101528, 2020.
View at: Publisher Site | Google Scholar
M. Bolgorian and R. Raei, “A multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in tehran stock market,” Physica A: Statistical Mechanics and Its Applications, vol. 390, no. 21-22, pp. 3815–3825, 2011.
View at: Publisher Site | Google Scholar
D. Stosic, D. Stosic, T. B. Ludermir, and T. Stosic, “Multifractal behavior of price and volume changes in the cryptocurrency market,” Physica A: Statistical Mechanics and Its Applications, vol. 520, pp. 54–61, 2019.
View at: Google Scholar
X. Zhang, L. Yang, and Y. Zhu, “Analysis of multifractal characterization of bitcoin market based on multifractal detrended fluctuation analysis,” Physica A: Statistical Mechanics and Its Applications, vol. 523, pp. 973–983, 2019.
View at: Publisher Site | Google Scholar
T. Ané and L. Ureche-Rangau, “Does trading volume really explain stock returns volatility?” Journal of International Financial Markets, Institutions and Money, vol. 18, no. 3, pp. 216–235, 2008.
View at: Publisher Site | Google Scholar
H. Cheng, J.-b. Huang, Y.-q. Guo, and X.-h. Zhu, “Long memory of price-volume correlation in metal futures market based on fractal features,” Transactions of Nonferrous Metals Society of China, vol. 23, no. 10, pp. 3145–3152, 2013.
View at: Publisher Site | Google Scholar
K. Boudt and M. Petitjean, “Intraday liquidity dynamics and news releases around price jumps: evidence from the djia stocks,” Journal of Financial Markets, vol. 17, pp. 121–149, 2014.
View at: Publisher Site | Google Scholar
M. A. Ong, “An information theoretic analysis of stock returns, volatility and trading volumes,” Applied Economics, vol. 47, no. 36, pp. 3891–3906, 2015.
View at: Publisher Site | Google Scholar
R. Ma, H. D. Anderson, and B. R. Marshall, “Stock market liquidity and trading activity: is China different?” International Review of Financial Analysis, vol. 56, pp. 32–51, 2018.
View at: Publisher Site | Google Scholar
Z. Ftiti, F. Jawadi, W. Louhichi, and M. A. Madani, “On the relationship between energy returns and trading volume: a multifractal analysis,” Applied Economics, vol. 51, no. 29, pp. 3122–3136, 2019.
View at: Publisher Site | Google Scholar
E. Maasoumi and J. Racine, “Entropy and predictability of stock market returns,” Journal of Econometrics, vol. 107, no. 1-2, pp. 291–312, 2002.
View at: Publisher Site | Google Scholar
S. R. Bentes and R. Menezes, “Entropy: a new measure of stock market volatility?” Journal of Physics: Conference Series, IOP Publishing, vol. 394, Article ID 012033, 2012.
View at: Publisher Site | Google Scholar
D. Stosic, D. Stosic, Teresa Ludermir, Wilson de Oliveira, and T. Stosic, “Foreign exchange rate entropy evolution during financial crises,” Physica A: Statistical Mechanics and Its Applications, vol. 449, pp. 233–239, 2016.
View at: Publisher Site | Google Scholar
K. Ahn, D. Lee, S. Sohn, and B. Yang, “Stock market uncertainty and economic fundamentals: an entropy-based approach,” Quantitative Finance, vol. 19, no. 7, pp. 1151–1163, 2019.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2022 Dohyun Pak and Sun-Yong Choi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

425

Downloads

625

Citations