Volatility Spillovers Research Articles

Can local government implicit debt raise regional financial market spillover? Evidence from China

This paper investigates how local government implicit debt influences regional financial market spillovers. Utilizing the volatility spillover network of Diebold and Yilmaz (2014), we demonstrate that financial distress stemming from implicit debt can trigger regional financial market spillovers. Further analysis reveals that credit expansion amplifies the impact of local government implicit debt on cross-province volatility spillovers, while an improved financial situation lessens this impact. Moreover, this effect is more prominent in provinces with a higher foreign trade dependency and greater industrial structural change.

Not all REITs are alike: modelling the dynamic connectedness of sectoral REITs and the US yield curve

ABSTRACT We model the dynamic connectedness between key sectoral REITs and the yield curve’s constituents. We employ both return and volatility of REITs and conduct analysis in static as well as time-varying framework. Our static analysis reveals Office REITs as the net spillover transmitter to the system, whiles Mortgage, Timber, and Infrastructure REITs are net recipients of return and volatility spillovers. We document that despite the high interdependencies between sectoral REITs, their response to return and volatility spillovers is heterogenic. Our findings divulge low idiosyncratic spillovers in sectoral REITs, suggesting that controlling the transmission and admission of exogeneous shocks need to be paramount to both investors and policymakers. Our dynamic analysis discloses the consistency of Industrial, Mortgage, and Timber REITs as diversifiers (safe havens) in tranquil (tumult) trading periods. We document contagion in the connectedness of sectoral REITs during crises periods. Therefore, timely policy actions in stressed market conditions are recommended to lessen the degree of effect on fundamental cross-sector linkages through contagion. We discuss the implications of our findings for portfolio management and market regulation.

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors. Design/methodology/approach The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023. Findings The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value. Originality/value The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Volatility spillover across spot and futures markets: Evidence from dual financial system

This paper investigates dynamic returns and volatility spillovers between spot and futures markets in a dual financial system. It further analyses the shock transmission of both volume trading and open interest in the futures market. Empirical results suggest that both spot and futures indices are net transmitters of return spillovers to the volume and open interest of the futures market, whereas the futures volume is the only net transmitter of volatility spillovers to all other variables. This is consistent with the Information Arrival Hypothesis theory. The empirical analysis also evidences the presence of a dynamic interdependence between both Islamic and conventional spot market volatilities and the futures market. In particular, the returns and volatility spillover are bidirectional and ricocheting-off transmission in nature. Specifically, such interdependence is stronger in the case of the Islamic spot index than the conventional spot index and during financial shocks.

How do market volatility and risk aversion sentiment inter-influence over time? Evidence from Chinese SSE 50 ETF options

We propose to implement the TVP-VAR-SV model and the spillover index approach to investigate the dynamics of mutual influences between market volatility and risk aversion sentiment (represented by variance risk premium), from aspects of realized variance (RV) and implied variance (IV). We innovatively study the evolution of volatility information transmission mechanisms by analyzing time-varying impulse response and dynamic connectedness in the Chinese market. Upon inspecting shock effects from investor sentiment to RV and IV, we discover that investor sentiment can cause sudden shocks on realized volatility but produce expansive shocks on expected volatility. As we compare spillover effects in call and put options, we find that (i) sentiments in put option contain more volatility information and can affect market volatility, while sentiments in call option primarily perceive volatility spillovers from market volatility, and (ii) investor sentiments in call (put) option become to present more volatility information in a bullish (bearish) market. Our study provides valuable insights on behavioral finance theory and portfolio risk management.

Quantifying the volatility spillover dynamics between financial stress and US financial sectors: Evidence from QVAR connectedness

This study uses quantile vector-autoregressive to examine volatility connectedness among a global financial stress index (including five categories: credit, equity valuation, funding, safe assets, and volatility) and US financial sectors under low, moderate, and extreme volatility conditions. The dataset includes the special periods covering the global financial crisis, China crisis, COVID-19 pandemic, Russian–Ukrainian war, Silicon Valley Bank failure, and Credit Suisse bank crisis. The findings imply that spillover effects among the series are higher during extreme volatility than during low and moderate volatility periods. During periods of low volatility, the credit category of the financial stress index and the US financial sector indices are net shock transmitters, but during extreme volatility periods, the US financial sectors become net shock receivers alongside the credit and funding categories of the financial stress indices. US financial sectors also exhibit net shock recipient roles at extreme volatility levels during those special periods.

COVID-19 pandemic and linkage between stock markets in Middle Eastern countries

PurposeThe purpose of this study is to evaluate the linkage between stock markets in Middle Eastern countries before and during the COVID-19 pandemic by using daily and monthly data sets for the period from 2011 to 2021.Design/methodology/approachThe multivariate BEKK-GARCH model was computed to evaluate the existence of non-linear linkage among Middle Eastern stock markets. A correlation approach was used in this study to determine the type of linear connectivity between Middle Eastern stock markets. The study used monthly and daily data sets covering the years 2011 to 2021 to investigate the linkage between stock returns and the volatility spillover between the stock markets in Palestine, Jordan, Syria and Lebanon, both before and during COVID-19. To understand the types of relationships between markets before and during COVID-19, the daily data set was split into two periods.FindingsResults from the pre-COVID-19 suggest that the Syria stock market is not related to any stock market in the Middle East markets; the Palestine and Lebanon stock markets exhibit a weak relationship, but Jordan and Palestine stock markets are strongly linked. Conversely, results from COVID-19 evince a very strong bidirectional volatility spillover between Middle East stock markets. Overall, the results indicate the existence of increased linkage during the COVID-19.Research limitations/implicationsThe data collection on a daily and monthly basis, both before and during COVID-19, presents certain limitations for the paper. Another limitation is that the data cannot be generalized to all other Middle Eastern countries; rather, the conclusions drawn can only be applied to these four countries. This is especially true if the scholars collected most of the necessary data but were unable to obtain certain data for various reasons.Practical implicationsThese findings have implications for risk management, market regulation and the growth of local stock markets. Facilitating the growth of smaller, more specialized markets to improve integration with other Middle Eastern markets is one of the goals of the domestic stock market development policy. To ensure financial stability, Middle Eastern stock market linking policies should consider spillover risk and take steps to minimize it. Enhancing the range of investment opportunities accessible to shareholders and functioning as confidential risk-sharing mechanisms to facilitate improved risk management in Middle Eastern stock markets will not only significantly influence the mobilization of private capital to promote investment and local economic growth but also lay groundwork for integrated market platforms.Originality/valueThis paper adds to the body of literature by demonstrating the nature of the connections between these small markets and the larger markets in the Middle East region. Information from the smaller markets provides institutional insights that enhance the body of existing research, guide the formulation of evidence-based policies and advance financial literacy in these markets. This study contributes by comparing data from different stock markets to better understand the type and strength of the link and relationship between Middle Eastern stock markets, as well as any underlying or reinforcing factors that might have contributed to the relationship and the specific types of links that these markets shared prior and during COVID-19.

Does CEA or EUA matter for major commodity markets? Fresh evidence from the analysis of information spillovers and portfolio diversification

PurposeThis study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.Design/methodology/approachThe study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.FindingsFirst, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.Originality/valueThis study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

Mapping the landscape of energy markets research: A bibliometric analysis and predictive assessment using machine learning

This study examines the evolving dynamics of research on the energy market that focuses on understanding its interactions and interdependencies with other markets. Using published articles from 2002 to 2022 indexed by the Web of Science, we employ bibliometric methods, complex network measurements, and machine learning algorithms to analyze trends and predict academic success or interest. Our bibliometric analysis highlights the growing emphasis on new topics, such as clean energy, over traditional energy topics like crude oil and volatility spillovers. In a horse-race setup, we use supervised regression techniques to predict the paper’s academic success, measured in terms of the average number of citations over the years. We use meta-information from the paper, including keywords, as predictive attributes. The Random Forest achieves the best out-of-sample performance. We complement this analysis by using Shapley Additive Explanations to assess the contribution of each attribute to the overall prediction, thus allowing model interpretability. We find non-linear relationships between some numeric attributes, such as the number of keywords in the paper, and the target variable, highlighting the flexibility of non-linear methods compared to linear ones. Our findings offer valuable insights into emerging research trends and provide educators, policymakers, and finance professionals with critical information to navigate the evolving landscape of energy market research.

Dynamic impact of the US yield curve on green bonds: Navigating through recent crises

We examine the effect of the US yield curve on the global green bond markets at the forefront of the climate change fight. For this purpose, we compute three components (level, slope, and curvature) of the US yield curve based on daily data of the treasury yields with several maturities from January 2009 to June 2022 and employ country-level S&P green bond indices. Our dynamic network analysis shows that the level component of the yield curve is more influential in transmitting return and volatility shocks to green bonds, while curvature is primarily absorptive. The European, the US, and Hong Kong green bonds are the leading players in shock propagation. Both return- and volatility spillovers are time-varying and remain high during periods of systemically important events, especially COVID-19 and the Russia-Ukraine war, supporting the Global Financial Cycle Hypothesis. The war also changes the net behaviors (transmitter versus receiver) of the components and the indices. Investors and issuers of green bonds are advised to keenly observe the shape of the US yield curve and systemic events for better decision-making regarding investment horizons and contagion risk management.

Interlinkage of an Emerging Market with Financial Assets: Evidence from Three Well-Known Econometric Models

This research seeks to offer a thorough investigation of the return and volatility spillover dynamics between DSEX and five financial assets from Gold, currency rates, energy, and stock indexes. Previous studies only used one econometric model to estimate the results, but this work used three well-known models for cross validation and also used portfolio weights and an optimal hedge to control risk. The DCC-GARCH technique is used to examine volatility spillovers, and the Quantile VAR and TVP-VAR frameworks are used to examine return spillovers. Surprisingly, three separate methodologies all provided equal results and arrived at the same conclusion. This study's findings corroborate the premise that these six components frequently exhibit considerable co-movement in futures trading. This paper illustrates that investors can greatly benefit by altering their portfolios based on the dynamic weights and hedge ratios, even if there is a trade-off amongst the degree of risk mitigation and portfolio revenue.

The Federal Reserve’s Quantitative Easing policy and volatility spillovers: Evidence from Australia

The Federal Reserve’s Quantitative Easing (QE) policies have injected significant liquidity into financial markets. While a vast literature examines the implications of QE, its effect on the financial markets of relatively small open economies is yet to be fully understood. This paper uses Australia as a case study to reveal evidence of intensified volatility spillovers resulting from QE. Using ex-ante implied volatility measures, we derive time-varying indices that exhibit the risk transference between financial markets in Australia and the U.S. Our results show intensified spillovers explainable by QE, especially for equity markets during periods of policy normalization. An amplification of spillovers is also found to follow contractionary QE shocks, implying that risk increases as asset purchases decline. In all, we provide evidence of an international volatility channel of unconventional monetary policy, demonstrating the ramifications of central bank-induced portfolio balancing among investors.

Analyzing time–frequency connectedness between cryptocurrencies, stock indices, and benchmark crude oils during the COVID-19 pandemic

We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.

Impact of COVID-19 Crisis on Volatility Spillovers across Global Financial Markets: Evidence from Asymmetric GARCH Models

This study investigates the market volatility and asymmetric behavior in the commodity market, foreign exchange market, cryptocurrency, and stock markets by employing asymmetric GARCH models on the daily time series returns. The data covers the period from March 8, 2017, to March 17, 2023, and is divided in to three sub-periods: the entire sample period (March 8, 2017, to March 17, 2023), the pre-COVID-19 period (March 8, 2017, to March 10, 2020), and the during the COVID-19 period (March 11, 2020, to March 17, 2023). The empirical results show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Additionally, the results indicate significant positive asymmetric behavior in the crude oil and stock markets during the pandemic. The findings further document that gold exhibits a strong resilience during the pandemic period, indicating its hedging ability during crisis periods. Moreover, the results suggest that the EGARCH model is the most appropriate model to capture the volatilities of the financial markets both before and during the pandemic. The findings of this study provide useful insights for investors and policymakers, enabling them to adopt effective strategies for investing in portfolios during crisis periods in the future.

Covid-19 Pandemic and Volatility Spillover Effects on Foreign Exchange Rates

Covid-19 Pandemic and Volatility Spillover Effects on Foreign Exchange Rates

A multiscale analysis of returns and volatility spillovers in cryptocurrency markets: A post-COVID perspective

ABSTRACT Since the onset of the COVID-19 pandemic, leading cryptocurrencies have undergone significant price fluctuations, prompting widespread interest in the interdependence and spillover effects among cryptocurrency markets, as well as in identifying the key cryptocurrencies that drive market movements. This study contributes to the existing literature by utilising innovative vector wavelet coherence (VWC) and wavelet local multiple correlation (WLMC) frameworks to investigate the time-frequency co-movements among four cryptocurrencies (Bitcoin, Ethereum, Tether, and Binance). By exploring the co-movements across multiple time scales over a period from 01/01/2020 to 10/04/2023 through continuous and discrete wavelet coherency analysis, we identify four key empirical findings. Firstly, the returns connectedness is stronger than the volatility connectedness. Secondly, high-frequency co-movements are more erratic and correspond to positive and negative unexpected news, while low-frequency co-movements vary with changes in US monetary policy. Thirdly, Tether and Binance exhibit the weakest returns and volatility connectedness with other cryptocurrencies. Lastly, Ethereum and Tether (not Bitcoin) are the primary cryptocurrencies that account for returns and volatility movements in the market. We discuss the implications of these findings for various stakeholders in cryptocurrency markets.

The COVID-19 Outbreak and Volatility Spillover Between Stock Exchange’s Overall and Small- and Medium-enterprise Indices: Evidence from India

The research examines the dynamic correlation between the volatility of the Bombay Stock Exchange (BSE) and the Small- and Medium-enterprises (SMEs) Index of BSE in India. It focuses on analysing the volatility spillover between SME and BSE by utilising the generalised autoregressive conditional heteroskedasticity (GARCH) model. The study extends its scope by examining the impact of the current pandemic, COVID-19, on the volatility of both SME and BSE by utilising the exponential GARCH and threshold GARCH models. Findings suggest that there is volatility spillover between the two indices. The results also highlight that the pandemic significantly affected the volatility of both indices. The findings of the study provide important implications for policymakers, portfolio managers, and investors, particularly when the Indian government has been emphasising on the growth and continuous support of the small and medium enterprises and increasing foreign investments in India’s SMEs in recent years.

Physical climate risk attention and dynamic volatility connectedness among new energy stocks

Based on a sample of 41 Chinese new energy stocks during the period from 2016 to 2022, this paper uses the improved Diebold-Yilmaz method to analyze the volatility connectedness among new energy stocks. We study the essential characteristics of the volatility connectedness network, including major risk transmitters and risk receivers, and find that large company stocks play an important role in volatility spillovers in the new energy stock network. Furthermore, we use the Baidu search volume index of 20 keywords related to the physical climate risk to measure the physical climate risk attention and study the impact of physical climate risk attention on the dynamic volatility connectedness among new energy stocks. We find that the effect of physical climate risk attention on volatility connectedness among new energy stocks is significantly positive. The results remain robust through multiple tests.

Strategic insights into carbon markets, digital finance and geopolitical risks for advancing green sustainability investments

PurposeSustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns.Design/methodology/approachThis research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations.FindingsOur findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes.Research limitations/implicationsThis study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively.Practical implicationsThe study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices.Originality/valueThe implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.

Volatility Spillovers Effects between Energy Commodities and Islamic Stock Markets

Volatility Spillovers Effects between Energy Commodities and Islamic Stock Markets