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HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TOÀN CẦU VÀ CHIẾN LƯỢC CỦA VIỆT NAM 975 1Phan Thi Bich Nguyet* Huynh Ngoc Quang Anh* Huynh Luu Duc Toan* Abstract This paper studies how the[.]
HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 exhibits main results on the connectedness network, timevarying analyses, and predictive regression Finally, the paper is concluded in Section with a selected number of policy implications 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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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-Valueat-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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 Klưßner 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 Baruník 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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 worthmentioning point that this study takes into account the negative effects of COVID-19 pandemic, which is an unprecedented event over the last decade Data and methodology 3.1 Data We retrieved the equity index data from Thomson Reuters to calculate natural-logarithm 𝑃𝑡 return (𝑅 = 𝑙𝑛 𝑃 𝑡−1 ; in which, 𝑃𝑡 is the current index and 𝑃𝑡−1 denotes the one previous day) In addition, all the variables are winsorized at the 1st and 99th percentile to alleviate the impact of outliers on our analysis Table summarizes the descriptive statistics of our main variables, which will be employed to analyze the spillover risk as well as network analysis Table Summary of descriptive statistics Std Min Max Percent 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 Variables Mean 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 HỘI THẢO KHOA HỌC QUỐC GIA ĐỊNH HÌNH LẠI HỆ THỐNG TÀI CHÍNH TỒN 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 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 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 ... 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... 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... 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