Volatility Spillover Effects Research Articles

Volatility Spillover Effects and Risk Assessment of Indian Green Stocks: a DCC-GARCH Analysis

Volatility Spillover Effects and Risk Assessment of Indian Green Stocks: a DCC-GARCH Analysis

Research on dynamic spillover effect of stock market volatility in Beijing-Tianjin-Hebei region

To enhance economic cooperation in the Bohai Sea and Beijing-Tianjin-Hebei region, China introduced the Beijing-Tianjin-Hebei integration plan in 2014. Over the past decade, coordinated development has established a solid foundation, emphasizing economic and spatial structure optimization. The region’s development has now achieved comprehensive, qualitative, and profound progress. Recognizing economic integration as crucial, this study analyzes the 2020-2023 stock data of listed firms in the region, utilizing the DY spillover index to examine sectoral volatility spillover effects, both statically and dynamically. Results reveal bidirectional, asymmetric spillovers, with heterogeneous spillover levels. Industry, raw materials, and information technology are key spillover sectors, significantly influencing others and inducing synchronization. Property and finance sectors are primarily recipients, influenced by others. Energy and communication services exhibit strong bidirectional spillovers. Volatility spillover strength is influenced by factors such as inter-sector correlation, market risk, macroeconomic changes. Studying these factors helps grasp volatility spillover patterns and understand sectoral volatility-factor relationships.

Price Discovery and Volatility Spillover: An Empirical Analysis of Indian Futures-Spot Cardamom Markets

Price discovery and its related volatility spillover effect are significant indicators in commodity derivatives market to hedge the risk against sharp price fluctuations. This study empirically analyses the price discovery and volatility spillover using vector error correction model, Granger causality and bivariate exponential GARCH model (EGARCH) in Indian cardamom spot-futures markets. Daily time series data of spot market and near month futures contracts spanning the period from January 2009 to December 2020 have been used for the study. These price series data are based on the authenticated sources of Multi Commodity Exchange. The results of vector error correction model supported by Granger causality test indicates that spot market plays the role of leader as it is more efficient in reflecting the new information to prices. The results of bivariate EGARCH (1,1) model show that volatility spillover from cardamom spot market to futures market is dominant.

주가와 환율 변동성에 관한 실증연구

China's tourism industry has mainly focused on inbound tourism. Starting in 1995, the Chinese government introduced a weekend holiday system, and this policy served as an opportunity to develop China's domestic tourism industry. Thanks to the golden week system introduced in 1999, domestic tourism in China has grown explosively. Accordingly, social overhead capital such as railways, aviation, and highways was rapidly expanded. From then on, the tourism industry developed simultaneously into inbound tourism, domestic tourism, and outbound tourism. It is examine the volatility spillover effect between stock returns and exchange rates for Chinese tourism companies. For the empirical analysis, we use the BEKK-GARCH model of Engle and Kroner (1995). From the empirical analysis, we find the following results. First, the shock that occurred in the previous year was found to have a statistically significant positive effect on the current stock return rate and exchange rate change rate. Second, all conditional volatility spillover effect coefficients were found to have a statistically significant positive effect. Third, a negative spillover effect was found to occur between exchange rate volatility and Shanghai Composite Stock Index returns.

Exchange Rate Responses and Volatility Spillover Effects during the COVID-19 Pandemic in Indonesia

This paper aims to assess the impact of the confirmed COVID-19 cases, the timing of the outbreak, and physical measures on the returns and spillover effects of exchange rate in Indonesia. The model will be tested by the exponential generalized autoregressive conditional heteroskedastic (EGARCH) process and the spillover volatility index. The study discovers that the confirmed cases, outbreak news, and the implementation of large-scale social restrictions simultaneously contribute to a leverage effect on the volatility of a direct quote of Indonesian Rupiah to Australian Dollar, Euro, US Dollar, Singapore Dollar, and Great British Pound. To a certain extent, the heatwave as well as the meteor-shower effects as a result of clustering events and intense spillover effects in the currency market of Indonesia are observed.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

The Spill-over Effects between Oil and Gas Marketing and Exploration Firms of Pakistan Stock Exchange (PSX) KSE-100 index: During the Times of COVID-19 and Russian-Ukraine War

This study aims to trace the mean and volatility spillover effects between oil and gas marketing and exploration firms registered in KSE-100 index in the Pakistan Stock Exchange (PSX). Daily data of 8 firms (MGAS, PPL, PKOL, APL, HITE, HASC, PSO, and SHEL) for the period of 30th November 2019 to 30th November 2022. The whole data set is divided into two subsets, one is after COVID-19 (after 26th February2020) and the other is after the start of Russian-Ukraine war (after 24th February 2022). After that Hamao et.al (1990) uni-variate ARCH-GARCH type modeling is employed to the data in order to explore the dynamic linkages between the Marketing and Exploration firms (oil and gas) registered in KSE-100 index (PSX). The results from the data sets are indicating that there is mix movements between the oil and gas marketing and exploration firms in the KSE-100 index (PSX). The results are providing evidence there is Mean spill over from MGAS to APL, HASC, HITE, PSO, and SHEL. From PKOL to APL, HASC, HITE, PSO, and SHEL. From PPL to APL, HITE, PSO. And there is Volatility spill over from MGAS to APL, HITE, PSO and SHEL. From PKOL to APL, HITE, PSO, and SHEL. From PPL to APL, HASC, HITE, PSO, and SHEL. This linkage is developed between MGAS, PPL, and PKOL to APL, HASC, HITE, PSO, and SHEL due to the co-movements among the mentioned firms. The Augmented Dickey-Fuller Test is run on the return series and the test is insignificant in all series which is indicating that our return series are stationary at level, ARCH-LM test is run on return series and ARCH-LM test is also insignificant in all the return series means that there is no heteroskedasticity present in the return series.

Comparing the EU and Chinese carbon trading market operations and their spillover effects

A carbon trading market (CTM) policy for trading carbon dioxide emission rights as a commodity was created to reduce greenhouse gas emissions. CTMs operate differently in different countries and regions, and their interactions deserve an in-depth study. This study focused on the world's largest CTM, the European Union (EU), and the CTM of China, largest carbon-emitting country. First, we evaluate the liquidity and volatility of the two CTMs. Subsequently, the VAR model is used to explore the mean spillover effect between the two markets and the BEKK-GARCH model is used to explore the volatility spillover effect between the two markets. The study concludes that: (1) The liquidity of China's CTM is better than that of the EU's CTM. (2) Both the EU and Chinese CTMs are unstable, but the volatility of the Chinese CTM is lower than that of the EU CTM. (3) Price changes in the EU and Hubei CTMs have a mutual influence. (4) There are interactions between the market fluctuations of the EU CTM and the Shanghai CTM and those of the EU CTM and the Hubei CTM. The results of this study have implications for the construction and development of CTMs in the EU and China.

Study on the Fluctuation Spillover Effect between China's Carbon Market and New Energy Market

This project analyzes the volatility spillover effect of China's carbon market and new energy market, which is crucial to effectively prevent Systematic risk, promote low-carbon transformation of the energy market and ensure stable economic growth. Firstly, based on the spillover index model, we examine the dynamic interdependence between China's carbon market and the new energy market; Secondly, in order to capture market asymmetry, this project decomposes the total volatility into good volatility and bad volatility based on the GJR-GARCH model, and further explores the asymmetric spillover effects between these two types of markets. Finally, the marginal net spillover index (MNS) method is used to explore the main sources and paths of risk transmission in China's carbon market and new energy market under the impact of major events such as the China–United States trade war, the COVID-19 and the Russia-Ukraine conflict. Therefore, this project has important practical significance in promoting the establishment of a national carbon market, low-carbon transformation of the energy system, and improving macro risk prevention measures.

The contagion and spillover effects of stock returns between quasi-vertically integrated firms in Taiwan semiconductor industry

<p>The study intends to investigate whether there are contagion effects resulting from supply-chain stock returns interaction and spillover effects deriving from returns volatility causation in quasi- vertically integrated Taiwan semiconductor industry. 17 paired groups (34 companies) of parent and subsidiary companies were found. The unit root test, LB-Q test, ARCH tests, Bi-GARCH model, parametric t test and nonparametric sign test, ranked sign test and Kruskal-Wallis test are applied. The major findings are as follows: Positive stock returns contagion effect exist in both parent-to-subsidiary and subsidiary-to-parent. Stock returns volatility spillover main and minor effects exist in quasi-vertically integrated supply chain. In general, the contagion effect and volatility spillover effect of parent-to-subsidiary are greater than those of subsidiary-to-parent.</p> <p> </p>

Multidimensional risk spillover among power, coal, and carbon markets: empirical evidence from China.

Amidst the "double carbon" target, China is vigorously promoting the transformation of the electricity and coal markets and carbon markets widely regarded as an effective policy tool for managing carbon emissions. Leveraging sample data from January 3, 2017, to December 16, 2022, this study investigates the risk spillover effects among China's power, coal, and carbon markets via rolling window technology and the DY spillover index. The empirical results indicate a significant long-term two-way asymmetric spillover effect among these markets. Specifically, the Guangdong carbon market acts as both an exporter and receiver of risky fluctuations across the two sample periods. The coal market primarily exhibits a net risk spillover effect on the Guangdong and Hubei carbon markets. Furthermore, the aggregate spillover index reveals that the volatility spillover effects of the power, coal, and carbon markets are significantly amplified by extreme risk events. The rise and volatility of coal prices under the influence of these extreme risk events may lead to government intervention in the power sector, which in turn has an impact on the coal market. These findings underscore the time-varying nature of risk spillovers among markets and have important implications for risk management and the construction of diversified energy markets.

Volatility spillovers and Asymmetric effects of Chinese A-share markets—Enterprise-Level data Based on high-dimensional social network models

ABSTRACT This paper investigates volatility spillovers and their asymmetry in the Chinese A-share market from a firm-level perspective. We calculate the connections of the 5-minute high-frequency realized semivariance volatilities of 81 stocks by using a high-dimensional social network approach. The results show that (i) the volatility spillover effect of corporate-level stocks is significant, with high dynamic total connections, and exhibits sector clustering characteristics. (ii) Both extreme contingencies and economic stimulus can exacerbate the asymmetry of volatility spillovers. During periods with economic stimulus, positive volatility spillovers are much larger than negative ones; at other times, negative spillovers are larger than positive spillovers. (iii) There is a positive linear relationship between the asymmetries in the volatility spillovers received and released by individual stocks. (iv) The financial sector is more sensitive to positive volatility spillovers and dominates the positive volatility spillover system. The telecommunication services and utilities sectors are net emitters of negative volatility spillovers. Our research has substantial ramifications for investors and policy makers with respect to distinguishing different transmission paths of upside and downside risks, conducting better portfolio optimization and establishing risk warning mechanisms.

Fossil Fuel-Based versus Electric Vehicles: A Volatility Spillover Perspective Regarding the Environment

Due to environmental concerns, electric vehicles (EVs) are gaining traction over fossil fuel-based vehicles. For electronic devices, including vehicles, copper is the key material used for building. This situation draws attention to the impact of copper prices, crude oil prices, and exchange rates on the economic viability of using EVs over fossil fuels. We use the volatility spillover effect (VSE) to determine the financial viability of these two types of vehicles in the context of environmental issues. Daily data on copper prices, crude oil, exchange rate, and the BSE100 ESG (“Bombay Stock Exchange 100 Environmental, Social and Governance”) index are taken from 1 November 2017 to 20 September 2022. Two popular multivariate GARCH (“Multivariate Generalized Autoregressive Conditional Heteroscedasticity”) family models, i.e., the BEKK (“Baba–Engle–Kraft–Kroner”)-GARCH (BG) and DCC (“Dynamic Conditional Correlation”)-GARCH (DG) models, are utilized to find volatility connections between these variables. These are appropriate GARCH models to observe the volatility dependence of one market on another market. It is found that there exist volatility effects of copper and exchange rate on the S&P BSE100 ESG Equity Index Price, which we will refer to here as ESG. However, crude oil is found to be insignificant for ESG. The novelty of this study is in the use of volatility spillover to determine economic viability. The volatility effects of copper prices are positive for ESG in the short run and negative for long-term volatility. The exchange rate has a positive volatility effect on ESG in the long run. Surprisingly, we find that EVs are technologically better than fossil fuel-based vehicles as a possible sustainable energy source. We observe studies that have raised similar concerns about EVs’ lack of business sense compared to fossil fuels. However, using VSE to explore financial viability offers a fresh perspective. Based on the findings of the current study, it is recommended that policymakers and researchers revisit their support for EVs as an alternate and sustainable source of energy.

Volatility Spillover Effect of Chinese and American Stock Indices in the Context of COVID-19 based on DCC-GARCH Model

The COVID-19 pandemic in 2020 had a significant impact on the global economy, leading to a downturn in both the Chinese and American stock markets. The United States experienced four historic circuit breakers. This paper selects four representative indices from the Chinese and American stock markets, namely the CSI 300 Index, the Shanghai Composite Index, the S&P 500 Index, and the Dow Jones Industrial Average. Closing prices from January 2, 2018, to January 4, 2022, are selected, with January 2, 2020, serving as the dividing point. The time span is divided into pre-pandemic and post-pandemic periods to investigate the volatility spillover effects of Chinese and American stock indices under the backdrop of the COVID-19 pandemic. We employs the DCC-GARCH model to empirically analyze and quantify the volatility spillover effects of Chinese and American stock indices. Results reveal that under the backdrop of the pandemic, the volatility spillover effects between Chinese and American stock indices intensified. As time progressed, these effects gradually stabilized and began to decrease slowly. Based on the research findings, we provide relevant recommendations for policymakers’ and investors’ perspectives.

Information spillovers in Hong Kong REITs and related asset markets

This study examines information spillovers among Hong Kong (HK) REITs, major HK financial markets, major Chinese Mainland financial markets, and the VIX index. Our results reveal that time-varying spillover effects peak during crises, notably during the COVID-19 pandemic. The HK REITs market consistently receives return and volatility spillover effects from the HK stock market and real estate market. Since 2014, the HK REITs market has also been a significant recipient of volatility spillovers from Chinese Mainland stock and real estate markets. Additionally, the VIX index plays a driving role in the risk contagion effect of HK REITs. These findings inform investors in adjusting strategies, optimizing portfolios, and mitigating financial risks.

Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

Risk Spillovers between China’s Carbon and Energy Markets

In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the “carbon market”, provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China’s carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China’s carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China’s carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China’s carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China’s carbon and energy markets, i.e., the linkage of price fluctuations between China’s energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets.

Information overflow between monetary policy transparency and inflation expectations using multivariate stochastic volatility models

The forward-looking policy is useful for joint decision-making between public and monetary authorities. The study calculates the monetary policy transparency index, inflation expectations, and their volatility spillover effects at a data-driven angle. Optimization mechanisms of monetary policy transparency influencing inflation and inflation expectations volatility, and transparency index based on market participants’ optimization decisions are analysed. Multivariate stochastic volatility models for measuring the volatility of inflation and inflation expectations are analysed. Additionally, the relationships between inflation and inflation expectations volatility and transparency are tested empirically. Tests show that: first, the most suitable models for measuring volatility of inflation and inflation expectations are obtained. Second, monetary policy transparency has a negative effect on the volatility of inflation and inflation expectations. Third, there is a unidirectional Granger relationship from the volatility of inflation expectations to the volatility of inflation, and it indicates that the volatility of inflation expectations has an important guiding effect on that of inflation, and it is necessary to stabilize inflation expectations for stabilizing inflation. Fourth, improving monetary policy transparency can not only reduce inflation, inflation expectations, inflation volatility, inflation expectations volatility, but also reduce the sacrifice rate, that is, to stabilize the price level with less anti-inflation costs.

Study on The Impact of Shanghai Crude Oil Price Volatility and Stock Indices of China's Oil-related Industries

This paper takes Shanghai crude oil futures price and the industry stock index related to it as the research object, and selects the daily data from March 26, 2018 to December 31, 2022, and adopts the BEKK-GARCH model. To explore the correlation between Shanghai crude oil futures price volatility and different segmented industry stock markets. It is found that there is a significant asymmetric volatility spillover effect effect between the vast majority of oil-related industry markets and the Shanghai crude oil market. Based on the relationship between Shanghai crude oil futures prices and oil-related industry stock indexes, investors can make investments based on the fluctuations of Shanghai crude oil futures prices, rationally study the changes in the stock prices of oil-related industries, and do not follow the market panic operations using futures to control risk. And with the oil industry chain related to a higher degree of industry, by increasing the asset allocation method, can effectively reduce risk.

Research on Co-movement Between China and USA Stock Market under the Impact of COVID-19

The worldwide economic market has fluctuated wildly due to COVID-19, therefore revealing the co-movement of international stock markets should reduce its destructive force and shock. Based on the "Economic Basic Hypothesis" and "Market Contagion Hypothesis," this work uses VECM and GARCH models to examine international market volatility using the Shanghai Composite Index (SSEC), Shenzhen Composite Index (SZSE), and S&P 500 index. The total trading data of three indexes are divided into 2 periods, with September 01, 2019 as the cut-off point. The period 1 is from January 5, 2015 to August 30, 2019 and the period 2 is from September 3, 2019 to December 30, 2022. The study shows that: 1) During the COVID-19, the short-term return of both stock markets has declined and volatility has increased; 2) there is no reliable co-integration relationship between S&P 500, SSEC and SZSE which also declaims no long-run equilibrium between two markets; 3) before and during the COVID-19, the close to close return rate of America stock market guided the return rate of China stock market; 4) both S&P 500, SSEC shows an asymmetric effect and the leverage effect is distinct; 5) the co-movement was stronger when the rate of return was relatively high; 6) volatility spillover effect of China to USA stock market was significant.