Significant Volatility Spillovers Research Articles

Volatility spillover effect between cryptocurrency and stock market using MGARCH Bekk model

This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.

The Post-Internationalization Evolution of the Price Discovery Pattern in China’s Iron Ore Markets

Previous literature provided evidence of a negative impact of internationalization on the quality of China’s iron ore futures market within a 6-month window (i.e. June 1, 2018 to November 31, 2018), but little is known on the long-term impact. Using a longer, 4-year-7-month window from June 1, 2018 to December 30, 2022, we examine the market’s price discovery function, an important measure of market quality. We find significant structural break in China’s iron ore data series in August 2021, which largely coincide with a period of rising commodity prices to all-time high. Before the breaks, the price discovery function of China’s iron ore futures appears quite weak, with the spot market leading the futures market, which is consistent with the findings of Fan et al. During the post-break period, however, the futures market demonstrates a stronger price discovery function and takes over the leading role. The day-of-the-week effects in iron ore futures also seem to weaken post-break as well. Our GARCH model results exhibit significant bi-directional volatility spillover between the futures and spot markets.

Volatility Connectedness of MOVE Index and Bond Returns

Research Originality: To the best of our knowledge, this is the first study which measures the impact of the MOVE index on bond yield volatility by comparing its effect on both short-term and long-term. The paper contributes to the existing literature by providing a better understanding of the relationship between maturity and volatility spillover. Research Objectives: This study examines the dynamic volatility connectedness between 2 and 10-year bond yields and the MOVE Index. Furthermore, it is aimed at detecting the transmitter of volatility among variables. Research Methods: Using a data set from 2010 to 2022, the study utilizes a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyse the dynamics and relationship between bond yields and the MOVE Index. Empirical Results: The study finds a significant volatility spillover between the MOVE Index and Türkiye's bond yields. Notably, the linkage between the MOVE Index and the 10-year bond rates is stronger than with the 2-year rates. Additionally, the MOVE Index emerges as the primary transmitter of volatility, impacting both bond yields. Implications: This study sheds light on the complexity and dynamics of volatility spread in Türkiye's bond market, providing essential insights for forecasting bond yields and shaping financial policies.JEL Classification: G17, C22, G12How to Cite:Baykut, E., Gökgöz, H. (2024). Volatility Connectedness of MOVE Index and Bond Returns. Etikonomi, 23(1), 27 – 46. https://doi.org/10.15408/etk.v23i1.36131.

Asymmetric dynamic spillover and time-frequency connectedness in the oil-stock nexus under COVID-19 shock: Evidence from African oil importers and exporters

Amid the profound disruptions triggered by COVID-19 in financial markets, this paper investigates changes in the interconnectedness behavior in terms of mean and volatility contagion between crude oil and stock markets in African oil importers and exporters. To this aim, we employ asymmetric BEKK-Copula-GARCH and Wavelet Coherence methods, and the analyses are evaluated before and during COVID-19. The findings indicate that the interdependence between crude oil and African stock returns is relatively weak before COVID-19, but the strength is amplified during the pandemic. Significant volatility spillover from crude oil to stock markets is exhibited in South Africa, Tunisia, and Tanzania during COVID-19. However, the results are insignificant in all the selected African countries before the pandemic. Furthermore, evidence of asymmetric volatility transmission is uncovered in South Africa and Tunisia during COVID-19. The findings also reveal that the connectedness between crude oil and African stocks is time and frequency dependent, with the strong linkage observed in the mid-term and long-term at specific time points. Overall, this study holds vital contributions as it unveils new insights into how COVID-19 reshaped the traditional behavior regarding the oil-stock nexus in Africa, thus informing policy responses and investment strategies at times of extreme events.

Is China’s hog futures market effective? Based on the perspective of price discovery and hedging functions

ABSTRACT Based on daily data of China’s hog futures and spot prices from January 2021 to October 2023, this paper uses VAR-GARCH-BEKK to test the effectiveness of China’s hog futures market. The results of the study indicate that there is a two-way positive leading relationship between the hog futures market and the spot market. There is a significant two-way volatility spillover effect between hog futures market and hog spot market, but the hedging performance of hog futures is poor. Therefore, the effectiveness of China’s hog futures market needs to be further improved.

Causal relation and volatility spillover between commodity market and equity market of India using VAR Granger Causality and BEKK-GARCH Model

The financial markets play a discerning role in accentuating the growth of industry and commerce whereas, a volatile market has whopping reverberations on economic and financial stability of a country. The interconnectedness and reciprocity among the financial markets in economies are veraciously liable for the pricing of securities and provide investors, hedgers and speculators with copious opportunities for portfolio diversification. In this context, the study has been taken up to explore the relationship between the commodity and stock markets of India. The study has used the daily closing prices of MCX iComdex composite index, BSE Sensex and NSE Nifty 50 index from 1st January 2016 to 31st December 2022. The Johansen’s cointegration test estimates the absence of cointegration between the indices under study. The results of VAR Granger Causality Tests substantiate a bi-directional causality between the equity and commodity market of India. Whereas, it refutes any causal relationship between the Sensex and Nifty index. The estimates of BEKK-GARCH model confirm a significant volatility spillover between the two markets. The empirical outcomes of the study have poignant implications for all the stakeholders such as individual and institutional investors, policymakers, government agencies, traders, hedgers and other participants in the financial markets.

Time‐Frequency Connectedness between Green Bonds and Conventional Financial Markets: Evidence from China

This paper examines the dynamic evolution and volatility spillovers between China’s green bond market and conventional financial markets (bond, stock, commodity, and foreign exchange markets) using time and frequency connectedness measures. The empirical findings are as follows: firstly, there is a significant volatility spillover effect between the green bond market and other traditional financial markets, with the spillover effect showing clear time‐varying characteristics. The total spillover effects among all markets increase notably during extreme market conditions. Secondly, the green bond market primarily acts as a net risk spillover transmitter in most periods. Thirdly, compared to the stock, foreign exchange, and commodity markets, the green bond market has a more significant spillover effect on the traditional fixed‐income market. Finally, the volatility spillover effect of the green bond market on other markets exhibits a time‐frequency evolution, predominantly driven by short‐term factors. However, as market maturity increases, the influencing factors of the spillover effect gradually shift from short‐term to medium and long‐term factors. These findings offer insights for portfolio and risk management.

Dynamic spatiotemporal ARCH models

ABSTRACT Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.

Dynamic volatility transfer in the European oil and gas industry

The study examines dynamic volatility transmissions among European energy industry participants along the production lines of Upstream, Midstream, Downstream, and Integrated Oil Gas (IOG) segments. Using Diebold-Yilmaz (2012, 2014) spillover index, during the sample period of October 2006 to June 2022, we find significant internal volatility spillover among the European energy sector participants, primarily emanating from Upstream companies. In subsamples, we show that Downstream and Midstream segments can also become volatility transmitters under certain conditions. More importantly, the large Russian IOG companies became significant volatility transmitters after 2022 with the onset of Russia's war on Ukraine, potentially causing major system instability because these IOG firms were traditionally volatility absorbers in the network. Overall, we provide insights about the interconnectedness among European energy companies during normal and extreme market conditions and highlight important system dynamics that could be useful for policy makers and investors.

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.

Bitcoin market networks and cyberattacks

This paper assesses the interconnectedness across Bitcoin trading platforms leading to and in the aftermath of large-scale cyberattacks using a network topology and daily data over the period 1 January 2015–31 May 2019. We find significant volatility spillovers, especially when the frequency and the magnitude of cyberattacks rise. While this suggests the presence of informational inefficiencies, these are not uniformly distributed, as the observed heterogeneity in volatility spillovers depends on the size of the cyberattack. Moreover, cyberattacks appear to change the network structure of Bitcoin trading platforms. Specifically, while the Bitcoin in U.S. dollar platform remains systemically important, the Bitcoin trading platforms of the Yen and Australian dollar have become more relevant in recent times. From a practitioner and market professional perspective, our results suggest that an informed investment strategy can exploit such volatility spillovers to generate abnormal returns. From a policy point of view, the easing of Bitcoin regulation in Asia seems to be linked with the increasingly prominent importance of the trading platforms of the Yen and Australian dollar.

Time-frequency volatility spillovers between Chinese renminbi onshore and offshore markets during the COVID-19 crisis

This article investigates the time-frequency volatility spillovers between Chinese renminbi onshore and offshore markets during the COVID-19 crisis. By employing wavelet analysis, we find that: (i) As the timescale increases, the volatility spillovers between renminbi onshore and offshore markets are gradually significant and bidirectional, and they have increased significantly after the COVID-19 outbreak. (ii) The significant volatility spillovers of the two markets are decomposed into many sub-spillovers on different timescales, most possibly precipitated by heterogeneous behaviors across various investment horizons. (iii) During the COVID-19 crisis, the onshore market has the dominant position on price discovery and leads the offshore market.

Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns

AbstractThis paper investigates the causal relationships and volatility spillovers between the BSE Bankex index and the stock prices of five major Indian banks (Axis Bank, HDFC Bank, ICICI Bank, Kotak Bank, and SBI). Daily data from January 2, 2018 to March 8, 2023 are used, and statistical techniques such as descriptive statistics, Unit Root test, Cointegration test, Ganger Causality test, OLS regression, and GARCH model are employed. The study finds bidirectional causal relationships between the bank stocks and BSE Bankex returns, suggesting that the movement of the bank stocks significantly affects the overall market returns and vice versa. The study also finds significant volatility spillovers between the bank stocks and BSE Bankex returns, implying that the shocks in the bank stocks affect the market returns and vice versa. The study's outcomes have practical implications for investors and policymakers. Investors can use the results to make informed investment decisions in the Indian stock market, while policymakers can use the findings to monitor the financial stability of the banking sector and design appropriate policy interventions to address any potential financial crises. Overall, the study's findings suggest that policymakers should proactively monitor and manage market risks to safeguard overall financial stability.

BRICS EXCHANGE RATES IN THE POST-FINANCIAL CRISIS: AN EMPIRICAL ANALYSIS

ABSTRACT In this study, we have investigated the volatility of the exchange rate, the direction of spillover, and the long- and short-run relationships that exist in Brazil, Russia, India, China, and South Africa (BRICS). Further, we will look into how the BRICS economies' volatility is transmitted to one another so that investors can better manage risk and make prudent investment decisions. Understanding volatility transmission is an essential driver for constructing a diversified portfolio. We conducted the study using foreign exchange rate data from 2009 to 2019 for all BRICS economies covering the period after the financial crisis but before the outbreak of COVID 19. To obtain equilibrium relationship dynamics and the asymmetric effect of information in the market, we have applied the Johansen cointegration test, the pairwise Granger causality test, and the GARCH model. The study successfully analyzed long-term relationships and explained the direction of impact as well as volatility transmission in some market segments. We found significant volatility spillovers among the emerging markets that led to convergence between these economies. Within the BRICS economic bloc, Brazil has shown the highest number of associations with other members. Additionally, we discovered weak association between the selected economies. The findings of this study will provide insights into the behavior of exchange rates in the BRICS economies. Keywords BRICS, Foreign Exchange Rate, Cointegration, Granger Causality, Volatility.

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.

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.

Oil-exchange rate volatilities and returns nexus

The study evaluates the channel of volatilities and returns between global oil prices and exchange rates of 21 developing countries. The structural vector autoregression (SVAR) findings are that oil-producing and exporting countries would have their exchange rates fluctuate slightly due to changing oil prices. For Markov-regime switching estimations, whereas, exchange rate volatility does not significantly influence volatility in oil prices at both regimes of flexible and fixed exchange rates, there is the presence of significant volatility spill-over from oil prices to exchange rates. Oil price movements do significantly induce appreciation or depreciation of exchange rates. In effect, volatilities in exchange rates do not trigger volatilities in oil prices but positively and considerably influenced crude oil returns in the fixed regime by 0.59%. Notwithstanding the 0.092 low transition probability, all other probabilities that the influence of volatility in the exchange rate on oil market volatility would persist are high for both flexible and fixed regimes of exchange rates. The significant positive coefficients of exchange rates together with high transition probabilities reported are indicative of rising exchange rates, implying devaluation and hence, a negative influence on oil returns and prices. Market agents can therefore diversify risks by investing in oil markets and forex markets independently.

A volatility spillover analysis between bond and commodity markets as an indicator for global liquidity risk

This study aims to analyze the volatility spillover between bond and commodity markets in terms of global liquidity risk. The data covers the daily closing prices of bond markets in specified countries - Brazil, Russia, India, China, and Turkey - and certain commodities - gold and oil - for the period January 2008 to January 2022. We utilized the DCC-GARCH model to analyze volatility spillover between these markets and the Copula DCC-GACRH model to determine dependence structures between them. Additionally, we applied the Hong Causality in Variance Test to determine the direction of the causal relationships between these markets. Our empirical findings indicate the existence of significant volatility spillovers between gold and most of these bond markets (Brazil, China, Russia, and Turkey), and between oil and some of these bond markets (Russia, India and Turkey). Our results indicate a limited diversification benefit for investors and portfolio managers.

VOLATILITY SPILLOVER AND PANDEMIC – ANALYSIS OF SELECTED SECTORAL INDICES IN INDIA

The COVID-19 pandemic has impacted economies worldwide, and it has been reflected in their stock markets, as well. The effect was evident in the Indian stock markets, yet the nature and level of this impact are not very clear. This paper examines the short- and long-term spillover in the volatility between coronavirus cases on the broader market index, Nifty 50, and the indices of selected sectors: information technology, healthcare, and pharmaceuticals. Data for the period from January 2020 to July 2022 has been analyzed. The Dynamic Conditional Correlation GARCH model was used for analyzing the volatility spillover of coronavirus cases on Nifty 50, Nifty IT, Nifty Healthcare, and Nifty Pharma. The results show that there has been a significant long-term volatility spillover of infections on the broader market index, Nifty 50. However, there is no long-term persistence of COVID-19 on the sectoral indices. Only Pharma and Healthcare have exhibited significant short-term persistence. All the indices were negatively correlated with case numbers. Even though the sectoral indices did not exhibit significant long-term volatility spillover, they were positively correlated with the broader market index, Nifty 50, which in turn showed the significant long-term persistence of COVID-19. The results of the study are useful to policymakers and investors to understand the level of market impact due to the pandemic.

A Bayesian analysis based on multivariate stochastic volatility model: evidence from green stocks.

Green stocks are companies environmental protective and friendly. We test Green stock index in Shanghai Stock Exchange and China Securities Index as safe-havens for global investors. Suitable multivariate-SV model and Bayesian method are used to estimate the spillover effect between different assets among local and global markets. We choose multivariate volatility model because it can efficiently simulate the spillover effect by using machine learning MCMC method. The results show that the Environmental Protection Index (EPI) of Shanghai Stock Exchange (SSE) and China Securities Index (CSI) have no significant volatility spillover from Shanghai Stock index, S &P index, gold price, oil future prices of USA and China. During COVID-19 pandemic, we find Green stock index is a suitable safe-haven with low volatility spillover. Green stock indexes has a strongly one-way spillover to the crude oil future price. Environmentally friendly investor can use diversity green assets to provide a low risk investment portfolio in EPI stock market. The DCGCt-MSV model using machine learning of MCMC method is accurate and outperform others in Bayes parameter estimation.