Volatility Spillover Effects Research Articles

Impacts of U.S. Stock Market Crash on South African Top Sector Indices, Volatility, and Market Linkages: Evidence of Copula-Based BEKK-GARCH Models

This paper examines the effects of the Standard and Poor’s 500 (SP500) stock index crash during the global financial crisis and the COVID-19 pandemic periods on the South African top sector indices (basic materials, consumer goods, consumer services, financials, healthcare, industrials, technology, and telecommunication). The results of a copula-based BEKK-GARCH approach technique demonstrate the existence of price and volatility spillover during times of stock crashes. We discover that during a stock crisis, strong shocks and higher volatility spillover effects from the United States (U.S.) SP500 index to the top sector indices of the South African Johannesburg Stock Exchange (JSE) markets are more significant. However, there is no integrated economy, as the results did not show any spillover effects from South Africa to U.S. markets. Furthermore, the Gumbel copulas have higher dependence parameters, implying that extreme co-movements occur in the upper tails, suggesting the possibility of a large transmission of shocks from the SP500 to the eight top sector indices of the JSE and showing an asymmetric dependence between these markets. This result is important for investors willing to invest in the South African sector of equity markets to develop hedging strategies to prevent risk spillover from developed markets.

The spillover effects in the “Energy – Carbon – Stock” system – Evidence from China

This paper integrates China's energy market, carbon emission trading market, and stock market of energy-intensive sectors into one framework based on a system perspective. We quantify the return and volatility spillover effects respectively, and deeply examine the structures and driving factors of these two spillovers. Our findings demonstrate that (i) the spillover linkage in the “Energy – Carbon – Stock” system is considerable, particularly the return spillover connectedness; (ii) the stock market occupies a dominant position in the system and primarily serves as a net transmitter in the spillover structure; (iii) policy adjustment and energy market turbulence are the main driving events for the sharp rise of the overall spillover; (iv) there exists an obvious cluster effect in the spillover network structure, and the occurrence of major events will make the most affected assets move to the network center and become the main spillover sources; (v) the state of the energy market, the stock market stability and the monetary policy orientation all have a strong explanatory power for the overall spillover. Our research provides useful implications for policy makers, energy-intensive industries and green investors.

Dynamic volatility spillovers and investment strategies between crude oil, new energy, and resource related sectors

Promoting green and low-carbon development with new energy, and resource related sectors is an important way for mankind to deal with global climate change. With the marketization of new energy, the public and regulators have paid extensive attention to market relevance and risk contagion in China. In order to analyze the risk contagion mechanism, this paper examines the dynamic conditional correlation and the volatility spillover effect between crude oil, new energy, and resource related sectors. In addition, we compared the optimal diversification strategy by calculating hedging ratios and portfolio weights. The main results are as follows：First, new energy and resource related sectors have high dynamic correlation and the volatility spillover effect is significant. Second, from the perspective of net spillover index, new energy is the largest net volatility transmitter and WTI is the largest net volatility receiver. Third, when new energy is a long (short) position, the hedging efficiency of the portfolio with new energy vehicles using the hedging ratio strategy is the greatest. And when new energy is long, taking a short position in new energy vehicles is the most effective hedging strategy, but the most expensive hedging strategies are found in resource related sectors.

Comparative Study of Hedging Models in US

This study focuses on the use of three dynamic hedging models in the American stock market, and it computes the dynamic correlation coefficient, dynamic hedging ratio, and hedging effectiveness of three common models (DCC, BEKK, CCC). This paper fills in the gaps in the application and comparison of three hedging models commonly used in the US stock market during the pandemic. The results showed that NASDAQ index and futures were highly correlated and had significant volatility spillover effect before and during the epidemic. The hedging efficiency of the three models before the epidemic was higher than 0.931, and the hedging efficiency of the three models during the epidemic was higher than 0.936. It is proved that futures hedging is a good way to avoid risk no matter before or during the epidemic, and the highest hedging efficiency is dcc-garch.

Dynamic relationship of volatility of returns across different markets: evidence from selected next 11 countries

PurposeThis paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.Design/methodology/approachThe dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.FindingsFindings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.Practical implicationsThis study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.Originality/valueSeveral studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

The impact of pandemic on dynamic volatility spillover network of international stock markets.

Since the beginning of the twenty-first century, several pandemics, including SARS and COVID-19, have spread faster and on a broader scale. Not only do they harm people's health, but they can also cause significant damage to the global economy within a short period of time. This study uses the infectious disease EMV tracker index to investigate the impact of pandemics on the volatility spillover effects of global stock markets. Spillover index model estimation is conducted using the time-varying parameter vector autoregressive approach, and the maximum spanning tree and threshold filtering techniques are combined to construct the dynamic network of volatility spillovers. The conclusion from the dynamic network is that when a pandemic occurs, the total volatility spillover effect increases sharply. In particular, the total volatility spillover effect historically peaked during the COVID-19 pandemic. Moreover, when pandemics occur, the density of the volatility spillover network increases, while the diameter of the network decreases. This indicates that global financial markets are increasingly interconnected, speeding up the transmission of volatility information. The empirical results further reveal that volatility spillovers among international markets have a significant positive correlation with the severity of a pandemic. The study's findings are expected to help investors and policymakers understand volatility spillovers during pandemics.

The volatility spillover effect of macroeconomic indicators on inbound tourism in India

Diseño/metodología/enfoqueLos modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021).ObjetivoEl estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India.ConclusionesSe observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India.Implicaciones prácticasLa principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas.Originalidad/valorExiste una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.

Revealing Volatility Spillover Effects Between CDS Premiums and Equity Markets in Developed and Developing Countries: VAR-BEKK-GARCH Model Approach

Revealing Volatility Spillover Effects Between CDS Premiums and Equity Markets in Developed and Developing Countries: VAR-BEKK-GARCH Model Approach

Sustainability and stability: Will ESG investment reduce the return and volatility spillover effects across the Chinese financial market?

This study provides a preliminary investigation of the relationship between sustainability and stability by investigating the impact of ESG investment on the return and volatility spillover effects in the major Chinese financial markets, including the stock, bond, interbank, and foreign exchange markets. We adopted both the TVP-VAR and DY methods to calculate the time-varying total, directional, and pairwise spillover indices. We examined the impact of ESG investment on financial market stability by comparing the spillover effects when ESG investment, represented by the ESG stock index, is considered with those without special consideration on the ESG investment, represented by the general stock index. The results show that when the ESG stock index replaces the general stock index, the total, directional, and pairwise spillover effects in the Chinese financial market generally decrease. Meanwhile, we find that although the overall Chinese financial market spillover index is around 13%, it is occasionally quite volatile. In particular, the markets were hugely uncertain in 2013 and 2020 due to the disequilibrium of supply and demand conditions in the money market and the considerable shocks created by the COVID-19 pandemic. We support the idea that, while the Chinese government develops its green finance, for instance, by advocating for ESG investment, it simultaneously builds a more stabilized financial market. In other words, sustainability and stability are positively correlated and can be achieved together. The reason for this is that ESG investment supports a long-run investment strategy by reducing excessive short-run speculation activities in the Chinese stock market, which accounts for the volatile property of the market since it was launched.

Analysis of Risk Spillover and Asymmetry Between Three Crude Oil Markets and Chinese Financial Markets

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China’s future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations.

Volatility effects of cryptocurrencies on foreign tourism in India

ABSTRACT Cryptocurrencies and tourism have gained traction worldwide in the last few years. However, no research has been conducted to understand the relationship between the two. This paper examines the impact of the volatility spillover effects (VSE) of cryptocurrencies on the tourism sector in India. Using monthly time-series data (from August 2015 to January 2021) of the selected cryptocurrencies and foreign tourist arrivals (FTA) and foreign exchange earnings (FEE) from foreign tourism, we assess the volatility (short and long-term) impacts of cryptocurrencies on tourism (through changes in the monthly number of FTAs in India and FEEs of India through foreign tourism). The study applies Multivariate GARCH models (BEKK-GARCH and mGJR-GARCH). The findings suggest that there is an existence of volatility connections between cryptocurrencies and foreign tourism in India. These findings have noticeable implications for policymakers to understand the importance of cryptocurrency and blockchain for tourism sector policies in India.

Did mega-regional trade agreements reshuffle the financial influence of the US, China, and Japan in ASEAN? Evidence from the volatility-spillover effects

The Trans-Pacific Partnership (TPP) and the succeeding Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) were widely viewed as trade agreements aiming at containing the influence of China and raising that of the US and Japan in Asia. This study utilizes the outward volatility-spillover effect of the equity market as a proxy for a country’s financial influence and analyzes whether the signing of the TPP and CPTPP reshuffled the financial influence of the US, China, and Japan in the Association of Southeast Asian Nations (ASEAN). The study finds that the TPP and CPTPP did boost the financial influence of the US and Japan in some ASEAN countries; however, there is no evidence that they have reduced the financial influence of China in ASEAN.

Price volatility transmission of perishable agricultural products: evidence from China

Volatility transmission is a crucial price phenomenon that influences upstream production and downstream consumption in agricultural commodity markets. However, existing studies offer little evidence on how product perishability is related to price volatility transmission along the agricultural market chain. This study investigates how price volatilities are transmitted across the farm, wholesale, and retail stages using high-frequency data from litchi and apple markets in China. We adopt various MGARCH models and volatility impulse response functions to evaluate the time evolution of price volatility correlation, and the direction and magnitude of price volatility transmission. Empirical results indicate that in the litchi market chain, the wholesale stage plays a dominant role in price volatility transmission, and the wholesale and retail stages have higher volatility spillover effects on the farm stage than vice versa. However, we find little evidence of price volatility transmission along the apple market chain. Our findings suggest that the degree of price volatility transmission is stronger for higher product perishability.

The Analysis of Volatility Spillover Effect between Bitcoin and Developed and Developing Countries via The TVP-VAR

Bu çalışmada, Bitcoin ile gelişmiş ve gelişmekte olan ülkelerin hisse senedi piyasaları arasındaki volatilite yayılım ilişkisinin incelenmesi ve bulguların finansal piyasaları etkileyen küresel olaylar bağlamında değerlendirilmesi amaçlanmıştır. Bu amaçla 03.01.2017-25.03.2022 dönemi, Bitcoin, MSCI ABD, MSCI Avrupa ve MSCI gelişmekte olan piyasalar endeksi günlük verilerine zamanla değişen parametre vektör otoregresif (TVP-VAR) modeli uygulanmıştır. Uygulama sonucunda Bitcoin’in MSCI ABD ve MSC Avrupa karşsısında net volatilite alıcısı olduğu ve MSCI gelişmekte olan piyasalar karşısında net volatilite yayıcısı olduğu gözlenmiştir. MSCI ABD’nin net volatililite yayıcısı ve MSCI gelişmekte olan piyasaların ise net volatilite alıcısı olduğu tespit edilmiştir. Ayrıca Bitcoin’in gelişmiş ve gelişmekte olan piyasalarla zayıf bağlantılı olduğu gözlenmiştir. Bulgular, volatilite yayılımının aşırı artış-azalış gösterdiği dönemlerde tüm dünyayı etkileyen küresel olaylar olduğunu göstermiştir.

Spillover effects between internet financial industry and traditional financial industry: Evidence from the Chinese stock market

In order to explore the connectedness of financial industries in the internet era, this research measures the spillover effects between internet financial industry and each traditional financial industry. The results show that the spillover effects between internet financial industry and each traditional financial industry have time-varying characteristics, and the return spillover effects mainly occur in the short term while the volatility spillover effects mainly occur in the long term. By further exploring the spillover direction, it demonstrates that the securities industry is a net spillover exporter in the financial system, while the internet financial industry is a net spillover receiver. Moreover, cross-quantilogram analysis shows that the directional predictabilities of both return and volatility are significant when one industry is in the worst 10% situation. It confirms that the inter-industry spillover effects increase as fundamental or market uncertainty rises.

Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them.

What Leads to the Changes of Volatility Spillover Effect Between Chinese and American Soybean Futures Markets?

ABSTRACT This paper examines the volatility spillover between the soybean futures contracts traded in the US Chicago Board of Trade (CBOT) and China Dalian Commodity Exchange (DCE) through a normalized Copula – GARCH(1,1) - t model with structural changes. The structural change points are identified through a combination of Bayesian diagnosis with Z-test. The study finds that the volatility spillover exists between the DCE and CBOT soybean futures and weakens through time. We further identify seven structural change points in the volatility spillover relationship, suggesting it is going through significant structural changes. The changes are related to major social-political events including the trade conflict between China and the US, the COVID-19 pandemic and the Russia-Ukraine war.

Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model

AbstractThe shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial‐temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out‐of‐sample results, we found the following features. First, the proposed spatial‐temporal GNN spillover model outperforms the benchmark models in short‐ and mid‐term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean‐variance framework.

BDI와 CCFI 및 BDI와 SCFI 운임지수 사이의 변동성 파급 효과

BDI와 CCFI 및 BDI와 SCFI 운임지수 사이의 변동성 파급 효과

Dynamic Volatility Spillover Effects and Portfolio Strategies among Crude Oil, Gold, and Chinese Electricity Companies

This paper examines the dynamic relationships and the volatility spillover effects among crude oil, gold, and Chinese electricity companies’ stock prices, from 2 December 2008 to 25 July 2022. By estimating the dynamic conditional correlation (DCC) model, we identify the time-varying correlation between crude oil, gold, and Chinese electricity stocks. Then, we use the time-varying parameter VAR model (TVP-VAR) to analyze the total and net volatility spillover effects. In addition, we compare the hedge ratio strategy and the portfolio weights strategy, as well as the corresponding hedging effectiveness among the crude oil, gold, and Chinese electricity companies. Considering the impact of the extreme events, we also extend the examination to the special period analysis of two crises, the Chinese stock market crash in 2015 and the COVID-19 pandemic in 2020. The results indicate that significant volatility spillover effects exist among crude oil, gold, and Chinese electricity companies’ stock volatility, and the total spillover effects show a sharp increase under the impact of the crisis. On average, gold is a much cheaper hedging tool than crude oil, whereas these two commodity assets remain net volatility receivers during the whole period and the crisis. However, it is worth noting that for specific assets, the impact of the crisis on spillover effects depends on the characteristics of crisis events and the assets analyzed. Additionally, most optimal weight strategies provide better hedging effectiveness than hedging strategies from the perspective of hedging effectiveness.