A network analysis of return connectedness in the financial stability: Insights about disease and economic policy uncertainties

Nội dung text: A network analysis of return connectedness in the financial stability: Insights about disease and economic policy uncertainties

1. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 65. 1Phan Thi Bich Nguyet* Huynh Ngoc Quang Anh* Huynh Luu Duc Toan* Abstract This paper studies how the return connectedness exhibits the potential linkages among 17 economies over the twenty-year period started in 2001. We found three main findings through employing the Dynamic Connectedness approach which is based on the Vector Auto-Regression (VAR) to calculate Generalized Forecast Error Decompositions. Firstly, although the financial crisis (2007-2008) experienced a high level of connectedness. This spillover index spiked in the beginning stage of the COVID-19 outbreak. Secondly, the group of nations including the United States, Australia, and European countries, is classified as the ‘return shocks sender’ while Vietnam is immune to the financial linkages. Thirdly, we found the predictive power of the US Economic Policy Uncertainty and Disease Fear with market volatility on the Vietnamese return connectedness. Thus, our study highlights a number of relevant policies to mitigate the spillover risks in the context of financial stability. Keywords: Stock market interconnectedness, financial stability. 1. Introduction Understanding how the markets have been interconnected is the forefront for not only investors but also for policymakers to make use of the sensible and appropriate strategies. In the recent past, the high level of equity market interdependence is required to have forthcoming market monitoring and supervision (Massacci, 2017). Concomitantly, the market expectations are likely to change after a vast variety of unprecedented events, such as the financial crisis 2007-2008. Accordingly, the global financial crisis (GFC) has intensified the public distrust in financial regulations. The collapse of the market began * University of Economics Ho Chi Minh city | Email: toanhld@ueh.edu.vn 975
2. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM in the United States without any policy resistance. While the shocks emanating initially from the largest economies were overlooked, it alerted the powerful transmission mechanism in all aspects of financial markets and economies. The concern still holds true in the COVID-19 pandemic (So et al., 2021). The recent study of Schell, Wang and Huynh (2020) concludes that ‘this time is indeed different’ since the majority of the equity markets persistently exhibit the negative abnormal returns in at least 30 trading days on the onset of pandemic period. These two events are the typical examples for the voluminous crises occurring in every market. Thus, our motivation is to systematically assess the financial linkages in the global scope from dynamic connectedness and network analysis. This study aims to shed a new light on the explanation of return spillover effects with three proposed questions: (i) How does the total dynamic connectedness of 17 economies vary over the twenty-year period? (ii) Which economies are the ‘shocks sender’ or ‘shock receiver’? How do they act in the network? and (iii) Is there any predictive power on the Vietnamese return connectedness? From these points, this study concentrates on providing the policy implications as well as the comprehensive assessment regarding the spillover risk. For example, the clear understandings of connected mechanism would benefit the investment strategies (portfolio allocation to hedge the sudden losses) or the timely intervention of the capital flows to avoid the overwhelming withdrawal (Arslanalp and Tsuda, 2014; Klingebiel, 2014). Why does this study focus on 17 economies? With a greater emphasis on intraregional financial integration initiatives in many areas (the American, the European, and the Asian economies), this study selected the representatives, including the United States, China, Japan, United Kingdom, South Korea, Australia, Germany, India, Italy, Russia, France, Singapore, Turkey, Malaysia, Philippines, Thailand, and Vietnam. In which, the growing role of China and other emerging Asian economies (Vietnam, Singapore, and Thailand) is attracting the attention due to the sources of conduits of financial shocks. At the same time, the political conflicts; for example, the US and China (Guo, Jiao & Xu, 2021; Burggraf, Fendel & Huynh, 2020) trade war, and BREXIT (Oehler, Horn & Wendt, 2017) are the main drivers of the global return connectedness. After using the dynamic connectedness approach based on Vector Auto-Regression (VAR) to calculate the Generalized Forecast Error Decompositions, our main results are as follows: ▪ The crises as well as market disruptions (such as the European debt crisis, the COVID-19 pandemic) are likely to associate with the global connectedness. 976
3. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM However, in which, the level of spillover risk has the highest value during the coronavirus period. This finding is consistent with the previous studies of Baker et al. (2020) and Schell et al. (2020) about the linkage between an unprecedented event and financial markets. ▪ The network analysis indicates that there is a strong connection between the United States and the European markets. Moreover, the Asian economies are likely to have an independent position with the return transmission. Our finding supports the theoretical framework of Diebold & Yilmaz (2015). Concomitantly, our study expands the set of countries in other continents such as Asia or Australia to provide further insights about the network connectedness. ▪ Using the robust regression, we found that the uncertainties, which have been proxied by two indicators (the disease index based on equity market volatility and the US economic policy uncertainties) have the predictive power on the Vietnamese connectedness. To be more precise, the higher level of uncertainties related to economic shocks, policy changes, and disease outbreak strengthen the ‘receiving position of Vietnam’. Accordingly, the Vietnamese equity market is likely to receive more shocks from the remaining markets when the uncertainties rise. Our results remain robust when controlling different rigorous macroeconomics variables. The remainder of this paper proceeds as follows. In section 2, the theoretical framework along with other empirical evidence on return connectedness are presented. The summary of data sources and data selection adopted in this paper are described in the first sub-section 3.1 while the remaining briefly explains our methodology and the model specifications. Section 4 exhibits main results on the connectedness network, time- varying analyses, and predictive regression. Finally, the paper is concluded in Section 5 with a selected number of policy implications. 2. Literature Review The connectedness (also known as spillover effects) is the centering position of the modern finance over the last decade, especially after the voluminous uncertainties such as financial crisis 2007-2008 (Gorton Metrick, 2012), European sovereign debt crisis (Lane, 2012), the political and geopolitical conflicts (Wagner et al., 2018), and the pandemic (Ding et al., 2021). In this section, we would like to emphasize the theoretical framework of connectedness in the finance and financial econometrics view. In addtion, we describe and summarize how the empirical evidence in the financial world explained the connectedness with different types of assets and markets. 977
4. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 2.1. Theoretical framework The definition of connectedness in financial market remains as an elusive concept. More significantly, Diebold and Yılmaz (2014) also criticized that calculation of financial connectedness has been incompletely defined and they offered challenging measurements. The current literature employs the econometrics with correlation as well as the distribution shape of asset’s returns to capture the connectedness in financial markets. In particular, the correlation-based method is one of the most widespread approaches with a focus on Gaussian distribution. Therefore, Nguyen and Bhatti (2012) developed the tail-dependence method to examine the relationship between two assets. In the same vein, Bhatti and Nguyen (2012) further developed the model with extreme values and stochastic models. The current theoretical framework leaves scholars with challenging research problems. Therefore, different studies chip away at this econometric approach in different ways. Overall, this paper agrees with the extant literature on tackling the connectedness measures by using the variance decompositions from approximating models, which has been admitted in the previous findings; for example, equi-correlation (Engle and Kelly, 2012), Co-Value- at-Risk (Adrian and Brunnermeier, 2011), marginal expected shortfall (MES) (Acharya, Engle & Richardson, 2012). To address the current issues in defining return connectedness, we refer to the dynamic predictive model, devised by White (1996). The central theory of this approach is dynamic predictive modeling under misspecification. To sum up, the theory background of connectedness is mainly based on VAR variance-decomposition theory and network topology theory from causal relationship (Dufour & Renault, 1998; Hansen and Lunde, 2014). From these starting points, Diebold and Yılmaz (2012, 2014) estimated the systematic risk and decomposed the transmitted effects from one node to another. Our detailed estimates and model specifications will be represented in section 3.2. 2.2. Empirical evidence about connectedness In the recent past, the studies of connectedness has been purposely developed to examine the financial markets’ characteristics. To offer a systematic review of connectedness, we choose the JODs (Journals of Distinction in finance) to consider how linkages exhibit among these markets. Accordingly, Elliott, Golub, and Jackson (2014) explain the market failure when the discontinuous changes appear. Simultaneously, the diversification should be focused on network structure dependence; thereby this is not only the solution but also sources of risks (Glasserman & Young, 2016). To support this point, Brownlees 978
5. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM & Engle (2017) and Benoit et al. (2017) indicated that the sources of data disclosure would be the systematic risk, which triggers the connectedness in the financial markets. The study of connectedness is not only designed for the market-level but also for the industry and firm-level. To be more precise, the European banking system could have risk exposures, measured by a decrease in assets’ prices, when one bank is severely facing the problem (Greenwood, Landier and Thesmar, 2015). Concomitantly, the US companies could experience the multitude of relevant risk spillover channels and firms’ shocks (Hautsch, Schaumburg and Schienle, 2015). In the broaden view, not only the nature of firms or industry but also the macroeconomics determinants could contribute to the risks in the financial connectedness such as the level of production and income, unemployment rate, working hours, personal consumption and housing, and sales, orders, and inventory (Giglio, Kelly, and Pruitt, 2016). By using quantile regression, Giglio, Kelly and Pruitt (2016) modeled the macroeconomic shocks as the predictive power on the systemic risk in the US and Europe. There are some specific industries, having more risk exposures, in financial contagion (for example, banking system (Demirer et al. 2018; Cai et al., 2018), sovereign bond market (Alter and Beyer, 2014)). Interestingly, the study of Engle, Jondeau and Rockinger (2015) shed a new light that synchronicity of time zones could be the potential channel of the dynamics of financial firms’ returns. In addition, the interconnectedness study starts booming in the commodity markets (Kang et al., 2017; Maghyereh et al., 2016). By using the DECO-GARCH, Kang et al. (2017) provided a novel channel of risk transmission among different types of assets (precious metals, the West Texas Intermediate crude oil, and the agricultural products). As a result, the financial turmoil reflects the risk spillover in the commodity network and the optimal portfolio strategies are conducted to hedge risks. In addition, the cross-country connectedness is also a crucial research point over the last decade. To be more specific, Diebold and Yilmaz (2015) demonstrated intriguing results with the directional risk transmission from the United States to European during the 2007-2008 financial crisis. More importantly, this study makes an alert to policymakers that the institutional investors have the disproportionate role in driving the connectedness in European crises. In same pillar, the study of Klửòner and Sekkel (2014) indicated that US and UK were the centering positions of spillovers shocks when using the economic policy uncertainty index. More noticeably, the foreign exchange market is not exception by having the study of connectedness by Barunớk et al. (2017) with the asymmetric effects. Finally, after reviewing the strand of literature, our study contributes an empirical evidence to the literature review by two main folds. First, this is the first study that 979
6. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM calculates a large-scale sample data in terms of return connectedness across twenty-year period starting from 2000. Second, our study employs the two main indicators of market uncertainties, proxied by disease market based on equity volatility and economic policy uncertainty to predict the net connectedness in the Vietnamese stock market. It is a worth- mentioning point that this study takes into account the negative effects of COVID-19 pandemic, which is an unprecedented event over the last decade. 3. Data and methodology 3.1. Data We retrieved the equity index data from Thomson Reuters to calculate natural-logarithm 푃푡 return (푅 = 푙푛 ; in which, 푃푡 is the current index and 푃푡−1 denotes the one previous day). 푃푡−1 In addition, all the variables are winsorized at the 1st and 99th percentile to alleviate the impact of outliers on our analysis. Table 1 summarizes the descriptive statistics of our main variables, which will be employed to analyze the spillover risk as well as network analysis. Table 1. Summary of descriptive statistics Std. Variables Mean Min Max Percent 1 Percent 99 Skewness Kurtosis Dev. United States 0.00009 0.00533 -0.05612 0.04796 -0.01527 0.0151 -0.44766 15.13797 China 0.00013 0.00720 -0.05573 0.06096 -0.02118 0.01834 -0.1345 9.431520 Japan 0.00003 0.00581 -0.04532 0.05673 -0.01592 0.01442 -0.3165 9.846680 United Kingdom 0.00000 0.00510 -0.04996 0.04024 -0.01515 0.01338 -0.34934 11.51888 South Korea 0.00017 0.00631 -0.05688 0.05091 -0.01795 0.01755 -0.29703 9.448620 Australia 0.00006 0.00456 -0.04522 0.03103 -0.01308 0.01161 -0.63886 11.63029 Germany 0.00002 0.00625 -0.05794 0.04832 -0.01916 0.01615 -0.20458 9.472840 India 0.00019 0.00609 -0.05967 0.07132 -0.01820 0.01576 -0.39565 14.47890 Italy -0.00006 0.00649 -0.08161 0.04771 -0.01921 0.01567 -0.64642 13.51501 Russia 0.00018 0.00864 -0.10978 0.10402 -0.02636 0.02200 -0.53878 22.08402 France 0.00001 0.00605 -0.05711 0.04500 -0.01799 0.01586 -0.23590 9.943080 Singapore 0.00003 0.00494 -0.04270 0.03009 -0.01461 0.01369 -0.21145 9.477420 Turkey 0.00019 0.00847 -0.08562 0.05763 -0.02302 0.02250 -0.23476 10.12935 Malaysia 0.00006 0.00352 -0.04448 0.02950 -0.00954 0.00944 -0.70350 14.71722 Philippines 0.00010 0.0059 -0.06259 0.07073 -0.01675 0.01487 -0.36026 15.20544 Thailand 0.00013 0.00626 -0.07854 0.04966 -0.01634 0.01721 -0.65855 15.42778 Vietnam 0.00014 0.00624 -0.03325 0.02891 -0.01940 0.01747 -0.35373 6.784030 Notes: This paper summarizes the descriptive statistics of 17 economies over the period 2001-2020 based on mean, standard deviation, min, max, percentile 10-percent, percentile 99-percent, skewness and kurtosis. Overall, except Italy having the negative return, the remaining economies exhibit the positive returns during the research period. In which, the emerging markets; for instance, China, Thailand, Turkey, Vietnam, South Korea, outperform the advanced countries with high average returns (Mauro, 2003; Rouwenhorst, 1999). Concomitantly, it holds true for 980
7. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM the expected risk, captured by standard deviation, by having higher values. It is an intuitive fact from the portfolio theory (Markowitz, 1999). Another point that we need to highlight is the abnormal distribution shape, representing in skewness and kurtosis. All equity market experienced the left-tail structure and fat-tail shape. It means that the magnitude of loss can happen frequently and severely among these indices. Figure 1. Correlation matrix among 17 equity matrices Notes: This figure summarizes the linear correlation matrix by using Pearson indices across among 17 equity markets. The scaled range is between -1.0 (perfectly negative) and 1.0 (perfectly positive). Figure 1 represents the Pearson correlation among our variables with three main important points. First, there is a high dependence among European countries such as the United Kingdom, Germany, France, and Italy. Second, the small capitalization economies have a lower level of dependence while the ‘big’ market such as the US and the UK have a high correlation with the majority of markets. In the Southeast Asian countries, Singapore expresses the highest financial integration with the global scope, which can be explained by the advanced and developed features in this market. 981
8. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 3.2. Methodology The main objective of this paper is to investigate the return connectedness network among 17 equity indices. Therefore, our methodology consists two steps. First, we employ the spillovers across the markets using the Vector Auto-Regression (VAR) framework of Diebold & Yilmaz (2012; 2014). The main advantage of using a Generalized Vector Autoregressive framework in which Forecast-Error Variance Decompositions is to capture the dynamics of spillover effects. In this section, we will summarize the main points for the models. First, we consider the covariance stationary VAR model: 푡 = ∑ Ξ푗 푡−푗 + 휖푡 (1) 푗=1 In which, 푡 is the vector of n endogenous variables, which includes 17 equity indices described above. Ξ푗 is the parameter matrices of coefficients and 휖푡 is considered as the residual terms, conveying in the vector with distributing into (0, Σ). In the same time, the moving average process (MA) has been estimated with the dependent variable 푡 is: ∞ 푡 = ∑ Υ 휖푡− (2) =0 In which, Υ = Ξ1Υ −1 + Ξ2Υ −2 + ⋯ + ΞmΥ − . Then, after having this moving average, we estimate the Generalized Forecast Error Decompositions (GFED) 2 휎−1 ∑ −1(푒 ∑ 푒 ) 푗푗 ℎ=0 푖 ℎ 푗 (3) 휙푖푗( ) = −1 ∑ℎ=0(푒푖 ℎ ∑ ℎ푒푖) In which, 휎푗푗 denotes the standard deviation of residual extracting from variable j th while 푒푖 is the vector with value 1 for the i element and 0 otherwise. In accordance with the aforementioned point, we can estimate the directional spillover from variable (j) to variable (i) is defined as the share of GFVED in variable (i), which can be explained by the variable (j). Then the spillover effect should be estimated with: 휙 ( ) ̃ 푖푗 휙푖푗( ) = 푛 (4) ∑푗=1 휙푖푗( ) 982
9. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM To sum up, our estimations consists of the following steps. First, we cleaned up the data and define the stationary with appropriate order. Second, we employ the Vector Auto-Regression for model (1) with the pool of 17 equity indices. At this stage, we also focus on the optimal lag selection order proposed by the Akaike Information Criteria. Third, we estimate spillover among these variables by using GFED in Model (3). 4. Findings and results In this section, we categorized our main findings into three subgroups. First, the total connectedness, the net spillover effects, and the network analysis by using the dynamic connectedness. 4.1. Total return connectedness Figure 2 summarizes the total connectedness among 17 indices by looking at the full period from 2001 to April 2020. It is important to observe some highlighted patterns. First, the financial crisis experienced the highest connectedness among these equity markets in 2006-2009. The percent of market dependence has been 85% and its number is persistent over this difficult time, implying the adverse role of financial integration in the financial turmoil (Graham, Kiviaho & Nikkinen, 2012; Minoiu et al., 2015). Following this global meltdown, the European debt crisis in 2014 is likely to shake the total connectedness again with 75 percent. However, this risk would not persist for a long time across the different regions and markets (Demirer et al., 2018; Antonakakis, Gabauer & Gupta, 2019). Figure 2. The total connectedness across 17 equity markets Notes: The total connectedness of 17 economies, including United States, China, Japan, United Kingdom, South Korea, Australia, Germany, India, Italy, Russia, France, Singapore, Turkey, Malaysia, Philippines, Thailand, and Vietnam over the period from 2001 to 2020. 983
10. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM More noticeably, the total connectedness spiked in March 2020 when it was defined as the global health crisis – COVID-19 pandemic. It is an unprecedented event which has induced the financial shocks as well as the spillover risks in not only equity markets but also in all financial asset classes (Bouri et al., 2021; So, Chu, and Chan, 2021; Albulescu, 2021). Our study contributes a fresh insight into the financial market connectedness to the strand of literature by using the dynamic connectedness. Therefore, we highlight the role of ‘extreme event’, also known as ‘Black-Swan’ on the global scope of interconnectedness. 4.2. Net spillover effects Both Figure 3 and Table 2 systematically indicate the net directional connectedness in 17 equity markets. Accordingly, there are five markets, particularly the US, the UK, Germany, Italy, and France, which play the ‘sending’ role in terms of return shocks. What we found is also consistent with the current literature (Chevallier et al., 2018). The advanced economies are more likely to act as the ‘shock sender’ while the opposite side is pronounced to the emerging markets. Our findings are consistent with the previous study of Klửòner and Sekkel (2014) Figure 3. The net spillover effects of 17 equity indices Notes: Net total directional connectedness values are presented by each countries. The blue areas represent the overlap of the dynamic total directional connectedness TO and FROM others. The upside area (the benchmark 0) denotes the positive (TO > FROM) whereas the downside area represents the negative (FROM > TO). This illustration is based on the Generalized Forecast Error Decompositions (GFED), retrieved from the VAR estimates with the optimal lag. One of the worth-noting points from this figure is the case of China and Russia. The former acted as the ‘return shocks receiver’ over the period from 2001 to 2016. After that, this market turned into the active role of sending the shocks across these markets. While 984
11. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM Johansson and Ljungwall (2009) confirmed that there is no long-run effects from the Greater China region over the period from 1994 to 2005 by using GARCH model, our study continues to contribute the new empirical evidence that China gradually improves its influential position in the stock markets (Sui and Sun, 2016; Yin, Liu and Jin, 2020). It emphasizes the pivotal importance of studying the stock market interdependence because of the complex and the system’s rapid change. In addition, Russia had a positive directional connectedness over the 2008-2012 period while the other periods indicate negative values, which represent the ‘absorbing return shocks’ from other markets. Moreover, Singapore and Turkey also had at least one time in flipping position (from sender to receiver, and vice versa). The last point is that Japan, South Korea, Australia, India, Malaysia, Philippines, Thailand, and Vietnam are all shock receivers 985
12. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM Table 2. Static and dynamic connectedness among 17 economies Panel A: Static USA CHINA JAPAN UK KOREA AUSTRALIA GERMANY INDIA ITALY RUSSIA FRANCE SINGAPORE TURKEY MALAYSIA PHILIPPINES THAILAND VIETNAM FROM connectedness USA 30.175 2.504 1.584 11.403 1.739 2.640 12.763 2.331 10.014 3.994 12.170 3.144 2.367 0.633 0.446 1.995 0.099 69.825 CHINA 5.911 27.203 4.495 5.132 7.156 4.871 5.061 5.444 3.965 4.164 5.036 9.432 2.177 3.720 1.555 4.532 0.148 72.797 JAPAN 8.655 4.869 26.355 7.536 5.237 4.596 7.819 2.864 7.001 3.698 8.172 5.853 1.851 1.752 1.004 2.382 0.359 73.645 UK 9.400 2.396 1.631 21.329 1.679 2.528 14.233 2.531 13.112 5.061 16.596 3.360 2.756 0.958 0.381 1.967 0.081 78.671 KOREA 5.887 7.655 5.493 5.064 29.016 4.955 6.147 4.196 4.243 3.199 5.548 8.011 2.265 3.007 1.591 3.625 0.098 70.984 AUSTRALIA 8.905 4.805 4.354 8.300 4.397 26.001 7.471 3.487 6.510 3.748 7.814 5.986 1.821 2.029 1.629 2.519 0.225 73.999 GERMANY 9.749 2.242 1.501 14.256 1.925 1.837 21.429 2.302 14.097 4.299 17.617 3.230 2.587 0.782 0.245 1.834 0.070 78.571 INDIA 4.646 6.699 2.451 4.830 4.816 4.226 4.670 35.124 4.165 3.552 4.795 8.301 2.412 2.967 1.640 4.595 0.113 64.876 ITALY 8.242 1.963 1.461 14.152 1.319 1.933 15.158 2.364 23.039 4.320 17.654 2.893 2.394 0.782 0.316 1.875 0.134 76.961 RUSSIA 5.925 4.106 2.148 8.554 2.711 2.251 7.236 3.314 6.667 35.162 8.037 4.345 4.719 1.604 0.491 2.560 0.170 64.838 FRANCE 9.233 2.170 1.550 15.623 1.613 2.014 16.551 2.344 15.384 4.544 20.068 3.207 2.514 0.920 0.298 1.883 0.086 79.932 SINGAPORE 5.885 8.301 4.306 5.510 6.588 5.250 5.488 5.892 4.529 3.532 5.539 24.996 2.682 4.695 1.597 5.087 0.123 75.004 TURKEY 4.655 3.112 1.483 6.010 2.662 1.859 5.678 2.937 4.812 6.107 5.794 4.290 45.530 1.500 1.085 2.411 0.075 54.470 MALAYSIA 5.219 5.440 2.465 4.402 4.121 3.237 4.388 3.853 3.830 3.181 4.713 7.892 2.179 36.942 3.212 4.840 0.087 63.058 PHILIPPINES 6.938 3.692 2.019 5.136 3.340 3.328 4.873 3.420 4.017 2.983 5.185 4.624 2.400 4.103 39.889 3.729 0.324 60.111 THAILAND 4.598 6.038 2.670 4.547 4.596 3.419 4.256 4.862 3.884 3.552 4.511 7.479 2.385 4.362 2.342 36.322 0.176 63.678 VIETNAM 3.498 1.649 1.547 2.246 0.884 1.371 2.092 0.963 2.208 2.220 2.168 1.460 0.936 0.637 0.694 0.673 74.755 25.245 Contribution 107.346 67.643 41.157 122.702 54.782 50.316 123.881 53.103 108.437 62.151 131.348 83.506 38.444 34.451 18.523 46.507 2.369 1.146.666 TO others Contribution 137.521 94.846 67.511 144.031 83.798 76.317 145.310 88.227 131.476 97.314 151.416 108.503 83.974 71.393 58.412 82.829 77.124 TCI including own - Net spillovers 37.521 -5.154 -32.489 44.031 -16.202 -23.683 45.310 31.476 -2.686 51.416 8.503 -16.026 -28.607 -41.588 -17.171 -22.876 67.451 11.773 Notes: Variance decompositions are based on a Vector Auto Regression with the lag length of order 1 (BIC) and a 10-step-ahead forecast. This table demonstrates the results of the return connectedness performed with the static generalized FEVD (GFEVD). Directional return spillovers (SExy) which correspond to the percentage share of error variance in stock return x (rows) contributed by shocks to stock return y (columns) including the existence of 17 countries. Total return receiving connectedness for return x is given by its row sums reported in the columns added to the right of the table, both including “Contribution to others” and excluding “Contribution including own”. Panel B: Dynamic USA CHINA JAPAN UK KOREA AUSTRALIA GERMANY INDIA ITALY RUSSIA FRANCE SINGAPORE TURKEY MALAYSIA PHILIPPINES THAILAND VIETNAM FROM connectedness USA 33.590 2.691 1.586 10.314 1.705 1.820 11.795 2.046 9.590 4.076 11.492 2.514 2.749 0.956 0.725 1.709 0.643 66.410 CHINA 6.658 28.962 3.753 5.221 6.862 3.993 5.388 4.799 4.289 3.815 5.396 8.076 2.526 3.701 1.611 4.136 0.812 71.038 JAPAN 9.536 4.128 27.534 6.814 5.418 4.009 7.767 2.547 6.696 3.413 8.089 5.204 2.288 2.076 1.138 2.469 0.875 72.466 UK 8.776 2.628 1.649 22.417 1.858 1.903 14.136 2.464 12.771 5.016 15.854 2.941 3.284 1.178 0.739 1.955 0.429 77.583 KOREA 6.395 7.264 5.623 5.384 29.015 4.389 6.076 3.789 4.747 3.410 5.938 6.325 2.635 3.232 1.852 3.226 0.701 70.985 AUSTRALIA 11.245 3.886 3.830 7.986 3.984 27.561 7.898 2.548 6.921 3.583 7.921 4.753 2.021 1.819 1.213 2.036 0.796 72.439 GERMANY 8.892 2.478 1.698 13.504 1.924 1.521 21.280 2.414 14.248 4.578 17.774 2.802 3.123 0.983 0.591 1.808 0.383 78.720 INDIA 5.132 6.016 2.487 4.737 4.364 2.613 5.190 38.912 4.273 3.328 5.210 5.776 2.755 2.791 1.755 4.010 0.649 61.088 ITALY 7.616 2.205 1.482 13.357 1.501 1.515 15.609 2.265 23.557 4.245 17.307 2.560 2.964 0.964 0.693 1.743 0.418 76.443 RUSSIA 5.954 3.763 1.679 7.589 2.399 2.022 7.074 2.683 5.990 39.630 7.361 3.439 4.571 1.737 0.933 2.492 0.684 60.370 FRANCE 8.633 2.472 1.743 14.478 1.806 1.569 17.024 2.366 15.094 4.538 20.301 2.897 3.055 1.088 0.651 1.840 0.446 79.699 SINGAPORE 6.532 7.771 4.416 5.334 5.792 4.529 5.468 4.404 4.459 3.467 5.658 28.481 2.770 4.293 1.529 4.365 0.731 71.519 TURKEY 4.954 3.034 1.545 6.040 2.434 1.606 5.960 2.877 5.090 5.255 5.998 3.525 45.694 1.788 1.293 2.325 0.582 54.306 MALAYSIA 6.141 5.062 2.562 4.504 4.231 2.520 4.637 3.333 3.954 3.424 4.841 6.155 2.576 37.579 3.314 4.194 0.973 62.421 PHILIPPINES 7.287 3.414 1.792 4.967 3.240 1.949 4.889 3.064 4.453 3.200 5.131 3.578 2.604 4.040 42.424 3.137 0.831 57.576 THAILAND 4.706 5.684 2.939 4.072 4.161 2.349 4.159 4.175 3.587 3.450 4.165 6.027 2.633 3.890 2.253 40.699 1.051 59.301 VIETNAM 3.791 1.954 1.850 2.494 1.627 1.601 2.701 1.303 2.729 2.083 2.897 2.122 1.687 1.607 1.221 1.612 66.720 33.280 Contribution TO others 112.248 64.449 40.634 116.795 53.307 39.907 125.772 47.076 108.891 60.879 131.034 68.694 44.242 36.142 21.513 43.059 11.004 1.125.643 Contribution including own 145.838 93.411 68.168 139.212 82.322 67.468 147.051 85.989 132.447 100.510 151.334 97.175 89.936 73.721 63.937 83.758 77.724 TCI - Net spillovers 45.838 -6.589 -31.832 39.212 -17.678 -32.532 47.051 14.011 32.447 0.510 51.334 -2.825 -10.064 -26.279 -36.063 -16.242 -22.276 66.214 Notes: Variance decompositions are based on a Vector Auto Regression with the lag length of order 1 (BIC) and a 10-step-ahead forecast. This table demonstrates the results of the return connectedness performed with the dynamic generalized FEVD (GFEVD). Directional return spillovers (SExy) which correspond to the percentage share of error variance in stock return x (rows) contributed by shocks to stock return y (columns) including the existence of 17 countries. Total return receiving connectedness for return x is given by its row sums reported in the columns added to the right of the table, both including “Contribution to others” and excluding “Contribution including own”. 986
13. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 4.3. Network analysis Figure 4. Return connectedness network across 17 countries Notes: The size of the node shows the degree of the net-pairwise connectedness. The color of the node indicates whether a country is a net transmitter (red) or net receivers (blue). Our findings are consistent with the current literature about the interrelated among the markets (Jayasuriya, 2011). In particular, our study also highlights the role of significant interconnection between the equity markets of China and other markets. More importantly, the spillover effects are likely to be found in the US, Australia and European countries, which are in line with the previous findings. More noticeably, the centering position is likely to be China. And Vietnam seems to have an independent role in the network hub. Our findings also challenges the previous results from Nguyen (2011), which highlights the significant role of the US macroeconomics news on the Vietnamese stock markets. However, this study focuses on the set of macroscopic indicators such as Non‐farm payroll, unemployment level, Gross Domestic Product percentage level, and so forth. Our study explores the effects from equity return shocks only with the decomposition of residuals in the Vector Auto-Regression models. Although the return connectedness levels from ASEAN countries are relatively low and they are facing the possibility of volatility transmission (Korkmaz, ầevik, and Atukeren, 2012). Currently, our findings mainly shed a new light on the return spillover network and we leave this issue for future research. 987
14. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 4.4. Vietnam net connectedness with the rest of the world In this subsection, we extracted the net spillover effects of Vietnam to estimate how the uncertainties could predict the Vietnamese stock return connectedness. We only chose two main ‘key indicators’, proxied for the market uncertainties namely Infectious Disease Equity Market Volatility (EMV) and the US Economic Policy Uncertainty. The main reason that we choose their factors is the popularity and the economic intuition. The former represents the market shocks during the disease period, containing the keywords of fear2 while the latter demonstrates search results from 10 large newspapers in the United States, having the terminology of uncertainties and policy-related changes. Table 3. The predictive power of uncertainties on Vietnam return connectedness Variables Model (1) Model (2) Model (3) Panel A: The Equity Market Volatility (EMV) with tracking of infectious disease Infectious Disease EMV -0.014 -0.011 -0.005 [-11.30] [-8.45] [-5.23] Constant -1.296 3.965 2.812 [-98.54] [39.25] [6.84] Control variables No Yes Yes Time-effect control No Yes Yes Observation 5,064 5,064 2,545 R-squared 0.004 0.328 0.115 Panel B: The Economic Policy Uncertainties (EPU) Economic Policy Uncertainty -0.001 -0.001* 0.001 [-9.39] [-1.91] [0.53] Constant -1.148 3.994 3.042 [-55.36] [39.45] [7.43] Control variables No Yes Yes Time-effect control No Yes Yes Observation 5,064 5,064 2,545 R-squared 0.017 0.326 0.113 Notes: * < 0.1; < 0.05; < 0.01. The t-statistics are reported in the brackets. The control variables for Model (1) and Model (2) are the exchange rate return (VND/USD), interbank interest rate while Model (3) included the government bond yield in the short term period (less than 1 year). The dependent variable is the net return connectedness of Vietnam equity market. The other daily determinants are retrieved from Thomson Reuters EIKON over the period from 2001 to 2020. The uncertainties indices are collected from Table 3 summarizes our main predictive power of these uncertainties on the net return connectedness in Vietnam. The first and most important feature, which we highlight, is that the higher the level of uncertainties negatively predict the more severe ‘return receiving position’. In other words, the higher the market fear is, the higher likelihood of receiving 2 The list of uncertainty keyword is Epidemic, Pandemic, Virus, Flu, Disease, Coronavirus, MERS, SARS, EBOLA, H5N1, H1N1 on the relevant newspaper. 988
15. HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HèNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM return connectedness in Vietnam equity markets is. However, the effects of disease fear are quite strong and persistent after controlling the rigorous variables for macroeconomics factors. In contrast, the US Economic Policy Uncertainty seems to have weak predictive power on the Vietnamese total return connectedness. Therefore, to our best knowledge, this is the first study to explore how the market fear with economic and policy-related uncertainties as well as disease fear could induce the ‘receiving’ position in terms of spillover effects. Our findings offer three main insights about the policymakers to mitigate the potential market crashes. First, the disease outbreak is more severe than the economic changes. Once again, this manuscript appreciate the greatest efforts of Vietnamese government to contain the COVID-19 pandemic to have financial stability. Second, the authorities should keep their eyes on the market fear sentiment to make the necessary alert when the market is in a passively extreme position, particularly receiver. Third, it should neither overlook the US policies nor the economic indicators change. Although the effect is marginal at present, this effect might not hold true when the other situation fluctuates, especially during the political risk, geopolitical uncertainties. This is the biggest challenge for stabilizing the Vietnamese financial markets. 5. Conclusion This paper employs the daily stock returns of 17 economies over the 2001-2020 period to explore the interconnectedness network by using the Dynamic Connectedness approach based on Vector Auto-Regression (VAR), measuring the Generalized Forecast Error Decompositions. Our main findings highlight three main points. First, the worldwide crises are associated with the total return connectedness; for example, the financial crisis (2007-2008), the European debt crisis (2012-2014) and especially, the coronavirus period (the early 2020). Second, we observed the heterogeneous effects of risk transmitter in two groups, such as advanced economies (the US, the UK, Australia, Germany, France, and so on) as well as emerging ones (Vietnam, Thailand, China, Singapore, Turkey, China, etc.). More importantly, Vietnamese equity market tends to have an independent role with return shocks, having the low and negative level of net return connectedness, representing the ‘shock receiver’. Finally, we found the predictive power of uncertainty indicators (Disease volatility market and Economic Policy Uncertainty) could worsen the net spillover risk of Vietnam. It implies that the higher uncertainties could link the ‘receiving role’ in Vietnam. Therefore, the policymakers should be cautious when the global markets have the relevant signals about the diseases as well as changes in economic plans and policy packages. 989
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