Volatility Spillover Effects Research Articles

Impacts of the COVID-19 Pandemic on Chinese Banking System: Measuring Volatility Connectedness

In this paper, we study the impacts of the COVID-19 pandemic on the connectedness of the Chinese banking system, and explore the heterogeneity effect of this pandemic. We construct static and dynamic analysis on 14 Chinese banks by building the volatility spillover network proposed by Diebold and Yilmaz to assess the volatility spillover effects caused by COVID-19, and compare it with the previous financial crises in the past 12 years. The empirical findings show that the COVID-19 did impact the connectedness of Chinese banking system and its structure within the system, with the 14 Chinese banks highly interconnected especially during COVID-19 era. In addition, the highly connected and contagious status of Chinese banking system didn't stay long during COVID-19 era compared with previous crises. Moreover, we find a shock-passing path during the COVID-19 era: big banks deliver more shocks to the middle banks, and the middle banks send more shocks to small banks. At the same time, our work presents that state-owned banks, during COVID-19 era, were more contagious and the local banks were more vulnerable compared with their historical performance.

Changes in volatility leverage and spillover effects of crude oil futures markets affected by the 2022 Russia-Ukraine conflict

We investigate the changes in the volatility regular/inverse leverage and spillover effects of three crude oil futures markets—WTI, Brent, and Oman—before and after the outbreak of the 2022 Russia-Ukraine conflict. We find that this geopolitical conflict has significantly changed the leverage effect in the three markets, and increased slightly and stabilized the dynamic conditional correlation and weakened the volatility spillover effect between every pair of them. In contrast to the inverse, the regular leverage effect remains the dominant effect driving oil prices during both the sub- and whole-sample periods. In the face of this conflict, Oman can play a good role in portfolios, not necessarily in hedging, of crude oil futures; and it highlights the importance in risk-taking of Asian crude oil-exporting countries. These findings have implications for crude oil-exporting and importing countries, and futures investors.

Time-varying asymmetric volatility spillovers among China’s carbon markets, new energy market and stock market under the shocks of major events

This paper investigates the dynamic asymmetric (good and bad) volatility spillover effects among China’s carbon markets, new energy market and stock market by considering the volatility asymmetry in the markets. We infer the good and bad volatilities from the GJR-GARCH model, where they correspond to positive and negative shocks, respectively. Moreover, we extend the traditional (symmetric) marginal net spillover measure to asymmetric (good and bad) marginal net spillover measures to analyze the time-varying asymmetric transmission of volatility spillovers across markets based on external shocks of major events, including the Sino–US trade war, COVID-19 pandemic, and Russia–Ukraine conflict. Our empirical results show that there exists significantly time-varying asymmetric volatility spillover effects among China’s carbon markets, new energy market and stock market. Moreover, the bad volatility spillover effect dominates the good volatility spillover effect. The asymmetric (good and bad) volatility spillovers across markets increase under the shocks of major events. In particular, we observe that the good volatility spillovers increase more significantly compared with the bad volatility spillovers during the Sino–US trade war and COVID-19 pandemic, while the bad volatility spillovers increase more significantly compared with the good volatility spillovers during the Russia–Ukraine conflict.

Assessing volatility spillover effect between international milk powder and China’s raw milk markets in the context of import growth

Assessing volatility spillover effect between international milk powder and China’s raw milk markets in the context of import growth

Spillover effect of China's economic development on countries along “Maritime Silk Road” – Based on vector auto regression

Since the cooperation initiative of Maritime Silk Road was put forward, more and more countries and regions have joined maritime silk road trade. The shift of economic growth rate in China and the decline in import and export trade will inevitably have a certain impact on the economic growth of countries and regions along this route. Based on this, this study first analyzed the transmission channels of economic volatility spillover effects. And a transnational panel model and global vector auto-regression are used to study the spillover effect of China's economic development on economic variables of countries and regions along this line. After the 15th quarter, one standard deviation of China's real GDP has a significant positive impact on the real GDP of East Asian countries by 0.597%, averagely. And the autoregressive coefficient of economic spillover effecting of China's economic growth on these countries along Maritime Silk Road is 0.244, which is significant at 1%, indicating that the spillover effect is significant. The regression coefficient of spillover effect of China's economic development on GDP growth in countries along this route is 0.472, which is significant at 1%. And the regression coefficient of spillover effect of China's economic development on trade balance of countries along this line is 0.967, with a significance level of 1%. Based on these research results, suggestions are proposed in this study, China should first optimize its trade structure with countries and regions along the route, and customize different foreign trade policies based on the specific characteristics of countries along the route. While developing and improving trade relations with countries and regions along the route, we will further achieve outward direct investment in these countries and regions, reducing the negative impact of economic fluctuations on China's economic development. The research aims to promote the quality improvement of China's economic development and growth, and achieve win-win cooperation among countries along the Maritime Silk Road.

Volatility and Spill Over: Empirical research on USD-INR Exchange Rate and Sensex

The present research is based on measuring volatility between dollar rupee exchange rate and BSE Sensex. The data collection is carried out during April 2001 to March 2022 and exploratory design of research is applied for the study. Descriptive statistical analysis is applied to process gathered data including GARCH (1,1) correlation and regression analysis. We found the presence of return and volatility spillover effects between INR-USD exchange rate and the BSE Sensex. The results of this study imply that the INR-USD exchange rate and the BSE Sensex are interdependent, and changes in one can have an impact on the other.

US Originated Asymmetric Volatility Spillover Effects: An Intraday Comparison Between Korean and Japanese Stock Markets

US Originated Asymmetric Volatility Spillover Effects: An Intraday Comparison Between Korean and Japanese Stock Markets

Cross‐border and cross‐commodity volatility spillover effects of Chinese soybean futures

AbstractThis study examines the volatility spillover between the US and China's soybean futures markets, and between China's soybean, soybean oil, and meal futures markets. A synchronization technique is used to overcome the bias from differences in the closing time of two exchanges. On the basis of BEKK‐GARCH and DCC‐GARCH on the data from January 2010 to April 2023, our results indicate that: First, there is a one‐way spillover from the US to China's No.1 soybean futures and a two‐way spillover between the US and No.2 soybean futures. Second, after 2018, the volatility spillovers of No.1 soybean futures declined, while that of No.2 increased significantly. Third, the dynamic conditional correlations confirmed our results that the spillover between No.2 soybean and soymeal, and between No.2 soybean and soyoil futures increased significantly. Our findings demonstrate the leading role of US soybeans in cross‐border trading and the increasing role of No.2 soybeans in cross‐country and cross‐commodity spillover.

Uncovering Systematic Risk in Crypto currency Markets: An Empirical Investigation Using DCC-GARCH Model.

This study presents an analysis of the occurrence of structural flaws and spillovers of volatility among eight popular digital currencies, such as Bit coin (BTC), Litecoin (LTC), Ripple (XRP), BNBPrice, DOGECOINPrice,ETHEREUMPrice, TETHERPrice, and USDCOINPrice. The analysis covers the period from December 25, 2019, to August 25, 2022, utilizing various statistical tests such as the Chow Breakpoint Test, Cumulative Sum test, The Granger Causality Test, the LM test for ARCH, and Dynamic Conditional Correlation (DCC) GARCH model. The results of this research reveal being present structural breaks in all the evaluated cryptocurrencies, highlighting the unpredictable nature of the cryptocurrency market. Additionally, these cryptocurrencies exhibit notable volatility spillovers and substantial positive correlations, which point to limited benefits of diversification within the cryptocurrency market. (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). These results have implications for investors, policymakers, and other stakeholders in the cryptocurrency market. The study recommends including cryptocurrencies as an important component in investment portfolios to stimulate returns and reduce overall portfolio risks, it is noted that direct investment incryptocurrencies can generate higher abnormal returns, but this comes with increased risk due to their inherent volatility. Investor preference for firms involved in cryptocurrency is influenced by factors such as legal protection and familiarity. Thus, policymakers should prioritize financial stability and implement careful regulation of cryptocurrency-related announcements to prevent artificial premiums and fraudulent activities (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). Furthermore, the analysis highlights the presence of high volatility spillover effects among certain cryptocurrencies, particularly Bitcoin, Ethereum, and Litecoin.. While volatility offersdiversification advantages, concerns arise due to the lack of intrinsic value and dividends in cryptocurrencies (Özdemir, 2022). The presence of systematic structural breaks suggests the possibility of manipulative behaviors and potential trading strategies that warrant further investigation. The DCC GARCH analysis reveals a high correlation and significant volatility spillover effects among most cryptocurrencies. These findings emphasize the need for a more diversified cryptocurrency market to mitigate risk and promote stability within this emerging financial sector.

Multiple time-scales analyses of nickel futures and spot markets volatility spillovers effects

Multiple time-scales analyses of nickel futures and spot markets volatility spillovers effects

Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation using DCC GARCH

Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation using DCC GARCH

Asymmetric Risk Connectedness between Crude Oil and Agricultural Commodity Futures in China before and after the COVID-19 Pandemic: Evidence from High-Frequency Data

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China’s crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security.

Commodity trading and inflation: ground reality in India using bivariate GARCH models

PurposeThe nexus of commodity prices with inflation is one of the main concerns for a nation's economy like India. The literature does not have enough volatility-based study, especially using the multivariate GRACH family of models to find a link between these two. It is the main reason for the conduct of this study. This paper aims to estimate the volatility effects of commodity prices on inflation.Design/methodology/approachFor ten years (2011–2022), future prices of selected seven agriculture commodities and inflation indices (wholesale price index [WPI] and consumer price index [CPI]) are gathered every month. BEKK GARCH model (BGM) and DCC GARCH model (DGM) are employed to determine the volatility effect of commodity prices (CPs) on inflation.FindingsThe authors find that volatility's short-term (shock) impact on agricultural CPs to inflation does not exist. However, the long-term volatility spillover effect (VSE) is significant from commodities to inflation.Practical implicationsThe study's findings have a significant implication for the policymakers to take a long-term view on inflation management regarding commodity prices. The findings can facilitate policy on the choice of commodities and the flexibility of their trading on the commodities derivatives market.Originality/valueThe findings of the study are unique. The authors do not observe any study on the volatility effect of agri-commodities (agricultural commodities) prices on inflation in India. This paper applies advanced techniques to provide novel and reliable evidence. Hence, this research is believed to contribute significantly to the knowledge body through its novel evidence and advanced approach.

Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to three cryptocurrencies, taking into account their respective levels of “dirty” energy consumption from 16 May 2018 to 15 May 2023. The purpose is to determine whether the eventual increase in correlation resulting from the events of 2020 and 2022 leads to volatility spillovers between clean energy indexes and cryptocurrencies categorized as “dirty” due to their energy-intensive mining and transaction procedures. The level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen’s methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the t-test of the heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of this study’s results are significant for investment strategies, and this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policy makers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.

Analyzing the volatility spillover and cointegration relationship between daily spot West Texas intermediate crude oil price and US dollar

In the study, it is aimed to analyze the diffusion and cointegration relationship between WTI and US Dollar in the period of 2016-2021. In the study, after a comprehensive literature review of the theoretical review, the econometric analysis section was started. In the first part of the analysis, the short and long-term relationships between the variables were examined with the autoregressive distributed lag methodology and the existence of a cointegration relationship was reached. According to the findings, the effect of WTI on foreign exchange volatility in the long run is statistically significant and negative. In the short-term evaluation, ECT is negative and significant within expectations. In this context, the changes between the variables approach the long-term equilibrium level. According to the results obtained in the causality and variance causality analyzes applied in the last part of the analysis, it is understood that there is a volatility spillover effect from WTI to foreign currency.

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

Effectiveness of price limits: Evidence from China's ChiNext market.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

Swings in Crude Oil Valuations: Analyzing Their Bearing on China’s Stock Market Returns amid the COVID-19 Pandemic Upheaval

The advent of the COVID-19 pandemic has markedly affected energy valuations and financial markets. As such, this article aims to scrutinize the dynamic interplay between stock market returns and crude oil prices, with a particular focus on China, factoring in the second-moment effect of volatility spillover. Employing an EGARCH process to model the leverage impact on returns’ volatility, the analysis utilizes daily data spanning from January 30, 2020, to August 30, 2022, and incorporates causality-in-mean and variance assessments. Empirical findings indicate that the QDII-LOF benchmark, representing oil prices, exerts a substantial influence on stock market returns. Nevertheless, the complete sample reveals no discernible spillover effects attributable to oil price fluctuations. These insights imply that the Chinese government’s actions should carefully weigh the ramifications of spillovers. Concurrently, investors are advised to attentively monitor the crude oil market when making portfolio allocation decisions.

Asymmetric risk spillovers and its determinants in global equity markets

This study examines the asymmetric volatility spillover effect using realized measures across 22 international stock markets. The findings suggest a high connectedness among global equity markets, with downside risk spillover significantly higher than upside risk spillover over time. Besides, Europe has a higher connectedness than the American and Asia-Pacific markets, and risk contagion is from the European and American developed markets to the Asia-Pacific regions. The study further identifies implied volatility index and market liquidity as the main positive determinants of risk spillover, which are robust across different rolling window lengths and horizons. We also find that the explanatory power is more significant during financial crises.

The Asymmetric Volatility Spillover and Dynamic Correlation Across Equity Markets in China and The United States

This paper aims to study the volatility spillover effects as well as the dynamic conditional correlation between stock market returns in China and the U.S. Firstly, the analysis uses a vector autoregression with a bivariate BEKK-GARCH model to capture the asymmetric volatility transmissions between the two markets during the sample of 1996-2019. Then a VAR-DCC-GARCH model is employed to estimate the dynamic conditional correlation between these two market returns. Finally, linear regression and Granger Causality test are conducted to further explore the effect of the U.S policy rates on such correlation. In order to account for the U.S monetary stances during the unconventional period, a combination of Fed fund rates and Shadow rates developed by Wu and Xia (2016) is used as policy rates. The main empirical results suggest (1) evidence of unidirectional volatility spillover from the U.S. to China market; but no spillover from China to U.S; (2) the dynamics of the conditional correlations from the VAR-DCC-GARCH model exhibit increases in correlation between the stock returns of China and U.S after 2008 financial crisis and recent trade war; (3) a linear regression shows that there is negative relationship between U.S policy rates and the dynamic conditional correlation, with the correlation coefficient r=-0.62. Granger Causality test suggests that the U.S policy rates do cause the change of the conditional correlation but not the other way around.