COVID-19 pneumonia was first identified in Wuhan in 2019, and the Chinese government imposed a lockdown on the city on 23 January 2020. Since that time, the outbreak has relentlessly spread across the globe, without showing signs of abatement. Global financial markets experienced significant downturns and experienced a level of financial instability not observed since the 2008 financial crisis (Corbet et al. 2021). Compared with that of prior crises, such as the global financial crisis (GFC), the duration of the crisis precipitated by the current coronavirus pandemic appears to have been markedly longer. In the initial stages of the COVID-19 pandemic, approximately 30% of the value of global stock markets was eradicated within a few weeks, and this loss had not completely recovered by the end of 2021 (Guo and Zhou 2021). Furthermore, the onset of a full-scale war between Russia and Ukraine in February 2022 emerged as a black swan event, surpassing most investors’ expectations and significantly destabilizing global financial markets. Given the role of both Russia and Ukraine as crucial global commodity exporters, the ramifications of the Russia-Ukraine war have the potential to considerably affect the global commodity supply chain, leading to a resurgence in global commodity price increases and exacerbating worldwide inflation (Kuzemko et al. 2022). The assessment of the interdependence and connectedness of financial markets during times of crisis has attracted the interest of researchers and public policymakers for two primary reasons: first, to assist in identifying any potential alterations in the nonlinear dynamic relationships between markets before and after a significant crisis, thereby enhancing the understanding of the patterns of financial system shocks and shifts in intermarket dependencies as a consequence of such major emergencies; second, this understanding is instrumental in formulating strategies and policies aimed at mitigating the economic impacts of COVID-19 and establishing portfolio diversification arrangements that align with investor needs both before and throughout the crisis period. In this context of global financial instability, the economic ties between China and ASEAN countries have gained particular significance, as these emerging markets navigate the challenges of an increasingly interconnected global economy.
Amid intensifying global financial integration and economic interdependence, the frequency and ease of trade and economic interactions between China and ASEAN nations have notably increased. This escalation has led to the heightened impact of stock market interconnections on the valuation of securities and the strategic decisions of investors. Moreover, the prominence of financial risk transmission has increased, highlighting the need for a thorough exploration of risk dispersion in diverse financial market contexts. Following the inauguration of the China-ASEAN Free Trade Area (CAFTA) on January 1, 2010, economic connections between China and ASEAN member states strengthened, fostering deeper financial and economic integration that affects the comovement of returns (Chiang 2019). Currently, the ACFTA is recognized as the world’s third-largest trade bloc, trailing only behind the European Union (EU) and the North American Free Trade Agreement (NAFTA) (The Balance 2021). The brisk growth in bilateral commerce between ASEAN countries and China, accompanied by their rising GDP, underscores their ongoing economic growth. Such developments are anticipated to promote closer collaboration among equity markets in China and ASEAN countries (Guesmi et al. 2017). In 2020, ASEAN countries’ aggregate nominal GDP reached US$3.173 trillion, while China’s GDP was US$14.73 trillion. China’s investments in ASEAN countries were reported at US$14.36 billion, and ASEAN countries’ investments in China amounted to US$7.95 billion (World Bank 2020). This resulted in ASEAN countries becoming China’s top trade partner in 2020, with China being the primary export market for ASEAN countries (ASEAN 2021). Consequently, financial linkages across borders between China and the ASEAN region have strengthened. Considering the differences in economic features, regulatory frameworks of capital markets, and cultural diversity across these nations, the equity markets in China and the ASEAN region display a sophisticated and nonlinear interplay. Thus, it is crucial to explore emerging connections and the dynamics of volatility shocks among these markets with the advent of new information. Furthermore, with China hosting the largest emerging equity market worldwide, its influence on the international financial landscape is growing. In essence, the economic and financial bonds linking China with ASEAN economies, along with their increasing significance in the global economic and financial infrastructure, stand as formidable.
This paper selects stock price indices from Asia and developed countries, as well as international crude oil and digital currencies, as research objects because of their importance in international financial markets. The crude oil, cryptocurrencies, and developed stock markets represent commodities, alternative assets, and traditional assets, respectively. These are currently the most favored asset classes among international investors, particularly in developed economies (Rao et al. 2022). Analyzing these asset classes within a unified system helps to comprehensively understand the interactions and risk spillover effects between different markets. Furthermore, we focus on the stock markets of several Asian countries, including China, Vietnam, Thailand, Malaysia, Singapore, the Philippines, and Indonesia. These countries play crucial roles in the Asian economy. Notably, China, as the world’s second-largest economy, has a significant influence on global investors through its financial markets. As important trading partners of China, the stock markets of ASEAN countries are expected to exhibit a high degree of interdependence with the Chinese market. Additionally, this study delves into the transmission of risk during recent market crises, aiming to provide novel investment perspectives for global investors and guidance for policymakers to mitigate systemic financial risk.
The nature of the correlations between national financial markets plays a pivotal role in determining the extent of global risk spillovers within the international financial system. Previous studies on asset correlations have faced numerous limitations, particularly when employing linear regression analysis, VAR models, and multivariate GARCH models (e.g., BEKK and DCC). These models are constrained by their ability to describe only linear correlations between variables and struggle with nonlinear correlations and tail dependence (Diebold and Yilmaz 2009; Behera and Rath 2021). Given that financial asset returns are not normally distributed and show significant tail dependence and asymmetry, the copula function emerges as a superior solution. It offers the flexibility to measure nonlinear or asymmetric dependencies and construct a dependence structure between assets with less restrictive distributional assumptions (Li et al. 2020). Owing to its flexible distributional premises, the copula approach, which is especially influential in financial research, is adept at explaining joint tail risk. The academic, portfolio management, and policymaking spheres have shown keen interest in asset market spillovers to comprehend the internationalization of asset markets, portfolio construction, and market risk diversification. The Vine copula model, known for its detailed depiction of multiple dependence structures, utilizes binary copulas as foundational elements, permitting arbitrary selection of binary copula types. This paper extensively applies the vine‒copula framework to articulate market dependencies, including tail correlations. Advancing the analysis, we use the quantile VAR (QVAR) connectedness method, developed by Ando et al. (2022), to explore both gross and net connectedness between paired markets. This method surpasses mean-based connectedness approaches by offering interquartile-based connectedness, thereby accommodating different market conditions (extreme upside, extreme downside, and median) and quantifying the role of market segments at various quartiles. A potential limitation of the QVAR approach is the loss of observations during rolling window estimations. Nonetheless, this enhances and informs the paper’s findings. Despite the strengths of the copula approach, like all methodologies do, it has limitations, particularly in capturing the evolution of asset connections. Our research sheds light on market interdependence and volatility connectedness across different markets, aiding stakeholders in identifying diversification opportunities.
Although a few studies have employed average or mean-based connectedness methods to measure the spillovers between Chinese and ASEAN equity markets during periods of both market turbulence and tranquillity (Hung 2019; Chen and Wang 2021; Zeng et al. 2023a, b), to our knowledge, the sole extant investigation into the extreme tail interconnectedness between these markets is that of Yousaf et al. (2023). Utilizing the QVAR connectedness approach, they analyzed the return spillovers between the Chinese and ASEAN stock markets under varying market states. Therefore, the main reason for selecting a combination of GARCH-EVT-Vine-Copula and QVAR connectedness methods in this study is that these methods are particularly suitable for capturing the nonlinear and extreme tail dependencies of financial markets during crisis periods. Our innovation lies not in the methods themselves but in applying these methods to understand the interactions between China-ASEAN markets and emerging alternative assets (such as cryptocurrencies), an approach that has been less explored in the literature. Through this combined analysis, we are able to reveal the risk transmission mechanisms of these assets during market crises and discover potential investment opportunities.
Our article contributes incrementally to the extant literature in three principal ways: (1) Our examination of Chinese and ASEAN equity markets extends to include the crude oil market, the cryptocurrency market, and the MSCI Global Developed Markets Index. Theoretically, both oil and cryptocurrencies exhibit close correlations with equity markets. Consequently, during these market periods, investors might derive benefits from a precise evaluation of the connections between oil, cryptocurrencies, and the China-ASEAN equity markets. Conversely, by incorporating the MSCI developed markets index, we underscore the impact of external shocks to this index on the volatility link between the China-ASEAN equity markets. As global integration hastens systemic interconnectedness, leading to more profound financial relationships, risks originating from developed markets are more readily transmitted to emerging regions, which is attributable to the high degree of global economic and financial interconnectedness (Diebold and Yilmaz 2015). Thus, our study offers greater insights for international investors, particularly those from developed economies, whose portfolios are likely to include a larger share of these popular assets, such as cryptocurrencies. (2) We concurrently investigated the risk associations and the efficacy of portfolio hedging during recent periods of uncertainty, including the COVID-19 pandemic and the 2022 Russo-Ukrainian War, thereby providing the literature on the Chinese-ASEAN stock markets with more comprehensive insights. (3) Methodologically, our use of extreme value theory (EVT) for tail risk modeling sets our study apart. Extreme value statistics, a branch of statistics concerned with deviations significantly from the median of the probability distribution, are often applied to analyze events with extremely low probabilities, such as rare natural disasters (Beirlant et al. 2006). EVT concentrates on the tail characteristics of the risk loss distribution, and models based on the generalized Pareto distribution (GPD) utilize the limited information on catastrophic loss data more efficiently, establishing it as the predominant technique in extreme value theory (Calabrese and Osmetti 2013). Hence, we posit that EVT is an effective tool for analyzing extreme events in financial markets following the outbreak of COVID-19, where it is reasonable to employ EVT to characterize the tail distribution and better measure extreme tail risk (Taleb et al. 2022).
Our study’s principal findings indicate that portfolio exposure to the MSCI developed markets index and Indonesian stock market equity substantially heightens the risk of losses during the COVID-19 crisis, as evidenced by strong lower tail dependence and greater risk in the lower tail than in the upper tail. Furthermore, following the outbreak of the Russia-Ukraine war in 2022, investors are advised to eschew investments in Thailand-Malaysia market pairs. Nevertheless, the risks associated with the Russia-Ukraine war in 2022 should not be overstated. Singapore’s role as a pivotal hub for interdependent market linkages within ASEAN countries underscores its status as the economic center of the region. Our QVAR connectedness analysis reveals that the markets under study exhibit stronger connectedness at the extreme upper and lower quartiles than at the median quartile. Time-variant connectedness analysis during the COVID-19 pandemic suggests that the total connectedness at the median quartile surpassed that of the lower quartile, signifying a marked rise in market risk and a temporary breakdown of conventional risk management strategies. Additionally, our portfolio calculations demonstrated that the Chinese and ASEAN equities did not provide substantial hedging effectiveness against the crude oil and cryptocurrency indices or against the MSCI developed markets index at any point. Moreover, there was a propensity for the allocation of funds into Chinese and ASEAN equities as a hedging strategy, irrespective of the prevailing market conditions. Our findings offer market participants valuable insights into portfolio allocation and risk management during the two most recent ranges of market turbulence across various market conditions.
The remainder of this work is organized as follows: First, the literature review offers a concise summary of existing research in this field. The Data and Methodology section outlines the econometric approach utilized and describes the dataset employed. The empirical findings and analysis section subsequently offers and interprets the empirical findings. Finally, the Conclusion section encapsulates the study’s key findings and offers actionable insights.
