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Analyzing time-different connectedness among systemic financial markets during the financial crisis and conventional era: New evidence from the VARX-DCC-MEGARCH model
Analyzing time-different connectedness among systemic financial markets during the financial...
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0002-01-01 00:00:00
JOURNAL OF APPLIED ECONOMICS 2023, VOL. 26, NO. 1, 2212455 https://doi.org/10.1080/15140326.2023.2212455 Analyzing time-different connectedness among systemic financial markets during the financial crisis and conventional era: New evidence from the VARX-DCC-MEGARCH model Xiaoxing Liu and Khurram Shehzad School of Economics and Management, Southeast University, Nanjing, China ABSTRACT ARTICLE HISTORY Received 24 July 2022 This investigation utilized the VARX-DCC-MEGARCH model assimi- Accepted 22 April 2023 lated with skewed-t density to analyze the time-different (i.e., day- time, overnight, and daily) connectedness among S&P 500, DAX 30, KEYWORDS FTSE-100, Nikkei 225, and Shanghai Composite Index. This investi- Returns transmission; gation discovered that the current daytime returns transmission Volatility spillover effect; from the DAX 30, FTSE 100, and Nikkei 225 index to ensuing over- Time-varying correlations; night returns of the S&P 500 index was inconsequential during the Leverage effect; Financial crisis stable period. The study also quantified that shocks befallen in the current overnight returns of the S&P 500 partake bidirectional and negative ties with shocks that occurred in subsequent day-wise returns of the DAX 30 index. Moreover, during crises, only the Shanghai composite index spillovers the volatility of the FTSE 100 index. The study revealed a leverage effect for the day-wise return of the S&P 500, DAX 30, and overnight returns of the FTSE 100 index. 1. Introduction The mounting trend of globalization and liberalization has transformed whole nations into one economy, instigating the integration of financial markets among the nations (Shehzad, Liu, et al., 2021). Besides, Information and Communication Technology (ICT) has played a vital role in connecting people worldwide. Notably, digital apps like bloom berg, yahoo finance, Merril Edge, Charles Schwab, and TD Ameritrade offer opportunities to financiers of any nation to register and buy stocks online at any time in the world (Rosenberg, 2020). So, on account of advanced ICT and globalization, a piece of economic news, political news, industry-specific news, or any other good or abysmal news related to financial markets occurring in any part of the globe would directly impinge on the prices of financial assets at the national level and worldwide (Bala & Takimoto, 2017). Consequently, high vacillations in the asset returns may intensify the financial risk, leading to financial calamities in the markets. In a similar vein, the history of financial markets demonstrates that economic crises, CONTACT Khurram Shehzad khurramscholar64@hotmail.com; 233189917@seu.edu.cn School of Economics and Management, Southeast University, Building 9, Juyuan, Jiulonghu Campus, Nanjing, China © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 X. LIU AND K. SHEHZAD such as sovereign debt crises (1982), stock market crashes (1987), Mexican crises (1994), Asian crises (1997–1998), Russian debt crises (1998), Greek debt crises (2009), and global financial crises (GFC) (2007–2009), had a significant impact on the stability of the global financial system (Shehzad et al., 2020). Jonathan Law (2008) argued that systemic risk was the vital motive of GFC. Thus, the study regarding the assimilation of financial markets and the reckoning of financial risk has grown into an indispensable subject and seized the attention of policymakers, academicians, and finance managers (Shehzad, Bilgili, et al., 2021). Numerous studies (Berkowitz & O’Brien, 2002; McNeil & Frey, 2000; MKP & PLH, 2006) employed univariate GARCH models to discover the volatilities. Given that, to deal with multiple asset returns in a portfolio, a multivariate version of the GARCH model is suitable to evaluate the volatility pattern. Likewise, several multivariate GARCH models have been instituted to quantify the conditional variance and covar- iance of financial assets, i.e., GO-GARCH, BEKK-GARCH, and CCC-GARCH models (for more detail on MGARCH models, see (Ghalanos, 2015; Silvennoinen & Teräsvirta, 2009). This investigation utilized the Dynamic Conditional Correlation (DCC) Multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (MEGARCH) model (Nelson, 1991; R. Engle, 2002), merged with a robust version of the Vector Autoregressive with exogenous instruments (VARX) model. The DCC model cogitates the time-varying correlation between the factors and can manage the vast extent of matrices (Ahmad et al., 2013). Besides, the MEGARCH model can figure out the asymmetric effect (Shehzad, Liu, et al., 2021). Furthermore, a good volatility model not only considers the nature of tail distribution appropriately but can also handle many assets (Malz, 2011). Countless studies have been conducted to define the transmission of the returns and shock spillover among developed, emerging, and under-developing nations (e.g., Ali & Afzal, 2012; BenSaïda et al., 2018a; Jebran & Iqbal, 2016; Li, 2007; Sikhosana & Aye, 2018). However, prior studies do not ponder the time difference to determine the transmission of returns, shock spillover, and portfolio VaR among the financial markets. The actual examination used data from the FTSE 100, S&P 500, Nikkei 225, DAX 30, and Shanghai Composite index (SSEC) to determine the nominations for the United Kingdom, United States, Japan, Germany, and Chinese stock markets, respectively. Figure 1 exhibits the time dissimilarity of each nation with other nations. According to Greenwich Mean Time (GMT), the financial sun arises from London (UK), which is 5 h ahead of the US. Though Japan, China, and Germany are 14 h, 13 h, and 6 h ahead of the US, respectively. The plots displayed that Japan is 1 h ahead of China, 8 h ahead of Germany, and 9 h ahead of London. In comparison, China unveiled 7 h difference from Germany and 8 h from London. In the end, Germany designates a 1 h difference from London. Thus, the stockholders of one’s market are aware of the returns of other markets. Given the time difference between the closing of one market and the opening of another market may provide profitable trading occasions. This phenomenon presents the importance of choosing these stock markets. Moreover, these markets are known as the most developed markets and substantiate a considerable portion of the world’s financial market capitalization (Bayoumi & Bui, 2012). Also, Belke and Dubova (2017) referred to the equity and bond markets of the US, Europe, the UK, and Japan as systemic financial markets. Furthermore, JOURNAL OF APPLIED ECONOMICS 3 Figure 1. Time Transformation graph. these stock markets also have prominence because the currency of these countries is listed in the Currency Basket. of International Monetary Fund (International Monetary Fund, 2016), and acute fluxes befallen in these markets can harm the world’s economy. Consequently, it turns essential to understand the financial risk pattern of these markets during and after the GFC era. So, effective policies can be generated, and any financial calamities in the future can be managed efficiently and timely. The critical contribution of this investigation is to scrutinize the time-different pattern of returns and volatility linkages among DAX 30, S&P 500, Nikkei 225, SSEC, and FTSE 100 index so that it can be determined which markets are significant transmitters and receivers of risk, and whose returns have a bidirectional, unidirectional, positive, and negative association with other markets. Combining these markets should bring super- fluous understandings into financial management research. This research also recognized the summary and plots of time-varying correlation among these markets. Besides, it enlightened the role of global oil prices for return changes in these stock markets. The general research questions of this investigation are as follows. First, does the return transmission of these equity markets show any directional configuration, and can time- different return transmission offer profitable opportunities? Second, what is the nature of time-different shock spillovers among these markets? Third, how differently do return transmission and shock spillover behave during financial crises compared to stable periods? Fourth, is financial risk diversification possible within these equity markets? Fifth, does the time-varying correlation of a stable period differ from GFC? Finally, what A currency basket contains different currencies with specific weights. These currencies are used to determine the market value of other currencies. The currency basket of IMF includes five currencies, i.e., the US dollar, Japanese Yen, Euro, Chinese RMB, and British Pound. 4 X. LIU AND K. SHEHZAD insinuations can be derived from this analysis, especially to understand the financial risk pattern of these stocks? After answering these questions, we aim to extend our knowledge about these markets’ time-different financial risk patterns. According to the author’s best knowledge, no existing investigation has employed this strategy to evaluate these markets’ returns transmission, volatility spillovers, and portfo- lio VaR. The analysis of this study will offer essential policy suggestions for investors, policy builders, finance managers, and portfolio managers. The rest of this article is structured as follows. Section 2 confers the enhanced literature review, section 3 refers to the superior and comprehensive data and methodology of this investigation, section 5 gives an interpretation and detailed discussion of results, and section 6 expresses the conclusion, policy implications, and future recommendations. Finally, the references used in this study are given in section 6. 2. Literature review One of the critical concerns in financial markets is the notion of financial risk due to information spillovers. Numerous essential studies related to the financial market’s risk and their dependence on each other have been reviewed in this study. For example, Aumeboonsuke (2019) identified that co-movements among equity markets upsurge financial instability. The outcomes of the vector error correction model recommend that returns of US and UK stock markets have some degree of sway on ASEAN markets. However, investment in ASEAN markets provides a healthier mean-variance portfolio. The study of Natarajan et al. (2014) stated that mean returns of the US meaningfully transfer to Australia and Germany. The analysis also found high volatility persistence for these markets. Furthermore, Jawadi et al. (2015) found weak evidence of volatility transmission between European and US markets, while through the post crises period, the inspection recorded bidirectional volatility spillover and returns transmission impact between US and European equity markets. Yoon et al. (2019) stated that the US is a major contributor to the transmission of returns, and financial crises intensify the spillover effects among financial markets. Y. Wang et al. (2018) argued that GARCH models present poor out-of-sample forecast results. The study used an in-sample strategy to gauge the stock markets of Canada, Japan, Germany, the US, and the UK and revealed noteworthy volatility spillover evidence from the US to other nations. Additionally, Belke and Dubova (2017) analyzed volatility transmission among equity and bond markets of the US, Europe, the UK, and Japan. The investigation indicated that these nations’ equity and bond markets highly accompany each other. Further, Sarwar et al. (2019) exploited daily data from SSEC, the Nikkei index, the Bombay stock exchange, and the oil market. The investigation publicized that oil returns and Nikkei index returns have a bidirectional spillover relationship. The study suggested that investors should choose more equity markets than oil assets to gain more profit. Indeed, BenSaïda et al. (2018b) examined volatility spillover impact across financial markets for GFC and tranquillity. The examination discovered that during the GFC, directional volatility spillovers grow into highly intensive and vary among net risk transmission and net risk receivers, while during the standard period, it showed a moderate impact. Moreover, Lien et al. (2018) questioned the shock spillovers among East Asian and US equity markets during the period of US subprime credit crises and JOURNAL OF APPLIED ECONOMICS 5 Asian currency crises. The consequences revealed a unidirectional shock spillover effect from the US to East Asian markets during both periods. However, Yarovaya et al. (2016) Stated that financial markets are highly vulnerable to local and region-specific volatility. Likewise, Smolović et al. (2017) evaluated different GARCH models using the daily returns of the Montenegrin stock market index. The study made known that ARMA (1, 2)-TS-GARCH (1, 1), ARMA (1, 2)-T-GARCH (1, 1), and ARMA (1, 2)-EGARCH (1, 1) combined with student-t distribution and Johansen distribution has accepted the Christoffersen test at 95% confidence level. Additionally, Louzis et al. (2011) utilized the fully parametric approach and mentioned that GARCH and realized volatility models united through filtered historical simulation and extreme value theory methods gues- stimate superior VaR during the GFC. The study also stated that skewed student distribution is a good alternative when high market fluctuations. This recent literature review exposed that, according to many studies, shocks signifi- cantly impact the stability of other markets. It designates the importance of unveiling the world’s largest stock markets’ behavior. Moreover, the literature showed that no investiga- tion had considered the time difference among the US, Japan, China, Germany, and the UK stock markets to capture returns transmission and volatility spillover effects. Additionally, we could not find a study that employed the VARX-DCC-GARCH model combined with skewed-t density to evaluate the volatilities. Therefore, to fill the gap, this research ponders the difference between these stock markets’ opening and closing times and evaluates the returns transmission and volatility spillover during and after the GFC period. 3. Data and methodology 3.1. Data This study has employed daily data from five stock markets of reformist economies, e.g., S&P 500 (US), Nikkei 225 (JAPAN), DAX 30 (Germany), SSEC (China), and FTSE 100 (UK). This investigation has utilized the data from 2007 to 2019 and divided it into two panels, i.e., panel A from 4 January 2010 to 27 November 2019, which epitomizes the regular period, and panel B from 4 January 2007 to 31 December 2009, which represents the GFC. The study also includes the impact of crude oil prices as an exogenous factor, and all the data is occupied from the database of yahoo finance and US Energy Information and Administration. 4. Methodology The examination has premeditated the daily returns by following the methodology of Bhuyan et al. (2016) as follows, R ¼ ðlnðCS =CS ÞÞ� 100 (1) t t t 1 Moreover, this examination has alienated daily returns into overnight returns and day- wise returns as follows, DR ¼ ðlnðCS =OS ÞÞ� 100 (2) t t t 6 X. LIU AND K. SHEHZAD NR ¼ ðlnðOS =CS ÞÞ� 100 (3) t t t 1 here, R , DR and NR denote daily, day-wise, and overnight returns, respectively. t t, t Moreover, CS , CS and OS symbolize the closing price of a stock on day t, day t t-1, t t-1, and the opening stock price on day t, respectively. Hence, the labels of overnight returns (NR) and daytime returns (DR) can be abbreviated as; SPNR (S&P 500), SPDR (S&P 500), DAXNR (DAX30), DAXDR (DAX30), LSENR (FTSE 100), LSEDR (FTSE 100), SSENR (SSEC), SSEDR (SSEC), NKNR (Nikkei 225) and NKDR (Nikkei 225). In order to internment vibrant possible evidence of returns transmission and risk spillover, this investigation has applied a newly designed econometric model. Indeed, this investigation employed the Dynamic Conditional Correlation (DCC) Multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (MEGARCH) model (Nelson, 1991; R. Engle, 2002) with the combination of a robust version of Vector Autoregressive incor- porated with exogenous variable (VARX) model (Croux & Joossens, 2008). The VARX-DCC-MEGARCH model has plentiful qualities as compared to standard GARCH models. The GARCH model only ruminates the magnitude of stock returns to compute future volatility, and an increase or decrease in stock returns is ignored. Whereas the MEGARCH model assumes a parametric approach for conditional heteroskedasticity. Moreover, the GARCH model placed some con- straints on parameters despoiled by estimated coefficients and confines the pro- cedure of conditional variance. Also, in the GARHC model, it is hard to ensure that shocks to conditional volatility persist or not (Nelson, 1991). However, the MEGARCH model can handle asymmetric volatility shocks as it does not impose non-negativity constraints on parameters (Shehzad, Liu, et al., 2021). Financial risk analysis and asset allocation mainly rely on correlations among financial assets, requiring many correlation series (BenSaïda et al., 2018a). Further, building an optimal portfolio with maximum return and minimum variance needs fore- casting of the covariance matrix of asset returns, and it is also needed to determine the standard deviation of a portfolio. The DCC GARCH model esti- mates the covariance matrix and conditional correlation directly. Moreover, the number of factors to be evaluated in the correlation procedure is not dependent on the number of series. Consequently, calculating the copious quantity of corre- lation matrices becomes possible (R. Engle, 2002). 4.1. Weighing up of the VARX model This investigation employed a robust version of the VAR model by utilizing the Multivariate Least Trimmed Square (MLTS) estimator. Hence, by way of (Croux & Joossens, 2008), we postulate VARs with one lag for both periods as (Liu et al., 2022); r ¼ w þ w r þ v ; for i ¼ 1; . . . :; n; (4) i;t i;0 i;j j;t 1 i:t i¼0 Here, in the mean model Eq. (4), r denotes assets return series i at time t, and w i;t i;0 nominates the constant term of series i at time t. However, when i� 0 then w nominates i;j the coefficient that quantifies the transmission impact of financial asset return series j to i. JOURNAL OF APPLIED ECONOMICS 7 However, when i = j, it calculates the lagged impact of its own returns on the succeeding value. Furthermore, v indicates the error term of series i at time t, and n represents the i:t number of variables included in the study, i.e., n = 10. 4.2. DCC-MEGARCH model 8 0 1 9 � � > > < n = � � X � � v v j;t 1 j;t 1 B C 2 2 σ ¼ exp μ þ n qffiffiffiffiffiffiffiffiffiffiþ @ qffiffiffiffiffiffiffiffiffiffi þ δ ln σ ; for i ¼ 1; . . . ; n: @ A i;j j i i;t i i;t 1 > > 2 2 : ; σ σ i¼0 j;t 1 j;t 1 (5) Similarly, in the variance equation Eq. (5), σ signifies the conditional variance of i;t series i at time t. Additionally, μ symbolizes the constant term of variance series i, and when i � j, n is the factor that delineates risk transmission impact from i,j financial asset series j to i. Nonetheless, when i = j, n represents the ARCH i,j coefficient that reckons the impact of shocks in returns on its own variance series i at time t + 1. Moreover, δ and @ determine the impact of changes in the i j volatility of its own variance series i at time t + 1, i.e., GARCH effect, and asymmetry impact of return series j, i.e., leverage effect, respectively. Moreover, DCC incorporated with a skewed-t density model delivers enriched findings (Bala & Takimoto, 2017). By following (Bauwens & Laurent, 2005), this study considers the multivariate skewed-t student distribution as follows, � � � � n ψþn nþψ=2 2 υ S Y Y i i pffiffiffi fðY jυ; ψÞ ¼ 1þ (6) t � ψ n=2 π 1þ υ ψ 2 Γ ½πðψ 2Þ i¼1 where Y ¼ Y . . . ::Y ; (7) t 1;t n;t Ii Y ¼ ðs z þ mÞυ ; (8) i;t i i i pffiffiffiffiffiffiffiffiffiffiffiffi ψ