Volatility Contagion Research Articles

Impact of Water Management Policies on Volatility Transmission in the Energy Sector

Purpose: This study evaluates the impact of the water management policies of energy companies on their volatility interactions with energy commodities. Design/methodology: We tested for volatility transmissions between 66 energy funds and fossil-fuel commodities. After identifying possible integrations, we investigated whether water management policies, after controlling for other fund characteristics, impact the probability of integration. Results: Our findings indicate strong volatility transmission from oil prices to energy funds. However, a reverse of this information flow was not observed. From the perspective of natural gas, we found strong bi-directional integration with energy funds. When we analyzed the influence of fund characteristics on the previously established integrations, water management policies do not impact the probability of the integration of oil. However, these policies are shown to have a significant influence on integration with the natural gas market. Originality/value: While there are multiple studies that show the integration between energy companies and corresponding commodities, according to our knowledge, this is the first study that evaluates the significance of water management policies with respect to volatility integration. This study highlights the importance of water-related policies with respect to the susceptibility of energy firms to volatility contagion from the natural gas market.

Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market

AbstractOperating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.

Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network

The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.

Volatility spillover between oil prices and main exchange rates: Evidence from a DCC-GARCH-connectedness approach

This paper investigates the co-movements of oil prices and the exchange rates of 10 top oil-importing and oil-exporting countries. First, we estimated the total static spillover index based on vector autoregressive (VAR) models. Second, we adopted the recent DCC-GARCH-CONNECTEDNESS approach proposed by Gabauer (2020) to conduct a time-varying analysis that investigates the directionally dynamic connectedness among WTI and Shanghai crude oil futures and currency markets. We explored contagion spillover volatility by focusing on a sample of major oil-exporting and oil-importing countries using daily data from 4 March 2018 to 25 August 2023. We analysed this relationship during four phases: the entire sample; before COVID-19; during COVID-19; and during the Russian‒Ukrainian war. Our results confirm the persistence of volatility for the series studied, thereby justifying the use of the dynamic connectedness approach. Our findings also reveal strong evidence of volatility transmission between oil prices and exchange-rate markets. However, the COVID-19 pandemic and the Russian‒Ukrainian war have altered this link. The connectedness between the two markets (petrol and exchange) was stronger at the beginning of the crisis period and then gradually depreciated in value over time. Our findings reveal that exchange rates for both oil-exporting and oil-importing countries are more sensitive to oil price shocks during crises than in normal periods. This suggests that volatility contagion between these two markets continues to exist, thus emphasising the role of oil price shocks as net transmitters across the network during extreme scenarios.

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.

Contagion network of idiosyncratic volatility: Does corporate environmental responsibility matter?

This study seeks to explore the impact of corporate environmental responsibility (CER) on idiosyncratic volatility contagion. We first adopt the general dynamic factor model to extract the idiosyncratic volatility. The long-term variance decomposition network is then constructed to examine the contributions of CER the contagion network of idiosyncratic volatility. The results demonstrate that idiosyncratic volatility contagion between sectors will be significantly strengthened during the stock market instability. CER play important role in the idiosyncratic volatility contagion, it will change the contagion ability and direction of idiosyncratic volatility during the stock market instability.

Extreme risk contagion between international crude oil and China's energy-intensive sectors: New evidence from quantile Granger causality and spillover methods

We use quantile Granger causality and quantile spillover indices to analyze the connectedness of returns and volatility between crude oil and China's energy-intensive sectors (steel, electricity, coal, and petrochemical). The results show that both the strength and the direction of the connectedness are related to financial conditions (measured by quantiles), with the tail quantiles having much stronger connectedness than the middle level. Bidirectional Granger causality between crude oil and energy-intensive sectors occurs in the left (right) tail of returns as well as in the right tail of volatility. According to the quantile spillover indices, the spillover indices of crude oil's return and volatility are 2.79 % and 0.09 %, respectively, in the median condition, while they are as high as 71.09 % and 287.42 %, respectively, in the extreme condition. We emphasize that the volatility contagion from crude oil to energy-intensive sectors is significant only in the right tail quantiles. Among different sectors, the strongest connectedness between crude oil and the coal sector is observed at the extremes, followed by the petrochemical sector. The impact of China's energy-intensive sectors on the crude oil market deserves attention, as crude oil is a net receiver of information from China's energy-intensive sectors during certain periods.

CONTAGION EFFECT IN THE BIST STOCK MARKET

The contagion effect refers to the transfer of price fluctuations from one market to others. In periods of turmoil, the relationship between various asset markets can result in substantial risks of contagion that go further the typical risks connected with changes in volatility and correlation. This can occur through various channels, such as the spread of shocks caused by transformations in the statistical relationships on asset returns, including changes in the relationship between average yields and volatility across different markets. This paper analyses the contagion of the BIST stock market on the volatility of gold, foreign exchange, and crude oil markets during the Covid-19 period. The correlation, coskewness, cocurtosis, covolatility test applied to measure the volatility contagion. The result shows contagions among the mentioned markets through higher moments. Our results are essential for asset management and developing preventive measures for financial stability.

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.

Return and volatility connectedness among the BRICS stock and oil markets

This study revisits return and volatility contagion among the oil and the stock markets of BRICS over the period of September 14, 2001 to June 17, 2022 using connectedness approach. In terms of shock transmissions, empirical results indicate that the stock market in China and India are net receivers and that in Brazil, Russia and South Africa are net transmitters. In addition, the oil market is the net receiver as well. Both returns and volatility spillovers give us the same conclusions. This evidence provides important implications for the investors, practitioners and government.

Volatility Contagion from Bulk Shipping and Petrochemical Industries to Oil Futures Market during the Economic Uncertainty

The purposes of the research have evidenced the spillover effects of oil-related factors in the oil market and the leading indexes of petrochemical commodities and the bulk shipping markets. The research gap was fitted and explored the effects associated with leading indexes for the shipping and petrochemical markets on the oil market during the US-China trade war, which is seldom bridged with significant relations in the history of oil. The scope of data for the period from 4 January 2016, through 31 August 2022, were analyzed using a generalized autoregressive conditional heteroskedastic mixed data sampling model as methodology of mix frequency to examine volatility spillover of four research hypotheses from the bulk shipping and petrochemical markets to the oil market. Main contributions revealed that spillover from the bulk shipping and petrochemical commodity markets transmitted significant volatility to West Texas Intermediate (WTI) oil returns after the US-China trade war began, a trend that has continued throughout the COVID-19 era until Ukraine–Russia war. These rare events indicate that the realized volatility derived from these market variables can be used to track the more significant contagions on WTI futures volatility in this empirical research than the weak relation in past studies.

Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model

This paper contributes to the ongoing debate on the nature and characteristics of the volatility transmission channels of major crash events in international stock markets between 03 July 1997 and 09 March 2021. Using dynamic conditional correlations (DCC) for conditional correlations and volatility clustering, GARCH-BEKK for the direction of transmission of disturbances, and the Diebold-Yilmaz spillover index for the level of volatility contagion, the paper finds that the climbs in external shock transmissions have long-lasting impacts in domestic markets due to the contagion effect during crisis periods. The findings also reveal that the heavier magnitude of financial stress is transmitted between Asian countries via the Hong Kong stock market. Additionally, the degree of volatility spillovers between advanced and emerging equity markets is smaller compared to the pure spillovers between advanced markets or emerging markets, offering a window of opportunity for international market participants in terms of portfolio diversification and risk management applications. Furthermore, the study introduces a novel early warning system created by integrating DCC correlations with a state-of-the-art deep learning model to predict the global financial crisis and COVID-19 crisis. The experimental analysis of long short-term memory network finds evidence of contagion risk by verifying bursts in volatility spillovers and generating signals with high accuracy before the 12-month crisis period. This provides supplementary information that contributes to the decision-making process of practitioners, as well as offering indicative evidence that facilitates the assessment of market vulnerability for policymakers.

Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective

In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break with a known structural break date, we propose hypothesis test procedures. Using the proposed diffusion model with high-frequency financial data, we investigate the effect of the U.S.–China trade war on stock markets from a financial contagion perspective. From the empirical study, we find evidence of financial contagion from the United States to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.

COVID19 Outbreak Impact on International Stock Markets Volatility Contagion

We analyze volatility contagion between the U.S. and Chinese stock markets and international capital markets. The volatility is modeled using: GARCH, TARCH, EGARCH, APARCH, IGARCH, FIGARCH, ACGARCH and GAS models under Gaussian, GED and t-Student distributions. 21,000 intraday observations of thirteen markets from January/1st to June/25th 2020 are employed. Once volatility is modeled, the incidence of Chinese and American markets on the rest of the bourses is tested employing Vector Autoregressive Markov Switching Models. Evidence confirms incidence of the Chinese and American capital markets volatility in other markets volatility; common breakpoints and Intermarket incidence in high volatility periods stand out.

Volatility contagion and connectedness between WTI and commodity markets

This article analyzes the contagion risk between WTI crude oil prices and several major commodities markets. We study the dynamics of commodity connectedness, and we measure volatility contagion under various market conditions and commodity price cycles between 1982 and 2020. Our results show that the intensity, speed, and duration of contagion vary across periods according to macroeconomic factors. Our findings have implications for optimal portfolio strategies and market regulation to insure against potential losses inherent to volatility contagion across international markets. Our findings are relevant for industrial producers, farmers, investors, market regulators, and financial supervisors.

Volatility contagion between oil and the stock markets of G7 countries plus India and China

It is imperative to analyze over the potential risks persisting in the financial systems from time to time. In this connection, this study examines volatility spillover between crude oil price and stock prices of G7 plus India and China using Diebold and Yilmaz (2012) technique from Jan 4, 2005–June 9, 2021. Although, our results show high stock market returns volatility connectedness among the sample countries but we find no significant transmission between oil prices and stock markets for the whole sample. The sub-sample results indicate that the period marked by COVID-19 witnessed substantial bidirectional spillovers between oil market and stock market. Empirical results of this study is expected to offer important policy inputs for investors, and policymakers to devise cautious strategies concerning their investments during uncertainties.

Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach

The international dry bulk shipping market is closely related to the commodity and crude oil markets. At the same time, the Baltic Dry Index (BDI) is usually considered as the main indicator of economic activities. In order to clarify the correlations between the three markets, this paper employs the Copula-VAR-BEKK-GARCH-X model to explore the dynamic dependence and volatility spillovers between the Baltic Dry Index, iron ore price and Brent crude oil price. In addition, the influence of exogenous variables (BDI/Iron ore/Brent) on market volatility and co-volatility is further discussed. The empirical results indicate that: first, the dependence between BDI, iron ore price and Brent crude oil price is time-varying and time-lag, especially when suffering from major crises. Second, dynamic dependencies and volatility spillovers between BDI, iron ore price and Brent crude oil price have significantly strengthened during COVID-19, manifesting that the impact of market turmoil has reinforced the linkages between markets. Third, this paper reaffirms the role of BDI as an intra and inter market indicator, while finding indication that iron ore is beginning to emerge its predictive indicator post-COVID-19. The results are not only conducive to the investment portfolio selection of individual investors and institutional investors, but also beneficial to formulating trade policies or shipping strategies by countries or shipping organizations.

Assessment of Volatility Contagion Effect Between Major Financial Markets During COVID-19 Pandemic Catastrophes

Across the globe, the havoc of the pandemic known to be a black swan event has brought miseries, deaths, uncertainty, loss of lives and jobs holding the humanity in a state of despair. The financial markets have been equally hit by the pandemic due to on-going uncertainty and hopelessness among the masses. The aim of this study is to examine the volatility contagion and dynamic conditional correlations between eight stock indices during the gloomy period to validate that there is a scope for revisiting the investment portfolio, create natural hedge in the investment portfolio by using exponential generalised autoregressive conditional heteroscedasticity (EGARCH) and dynamic conditional correlation generalised autoregressive conditional heteroscedasticity (DCC-GARCH) approach. We conducted an in-depth analysis of capturing volatility among stock indices ranging from tracking the volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19. There are very few studies that have conducted an in-depth analysis of capturing volatility of stock indices ranging from tracking the asymmetric volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19.

Information and volatility linkages between real estate, equity, bond and money markets in Australia

PurposeThe purpose of this paper is to examine information and volatility linkages among real estate, equity, bond and money markets in Australia.Design/methodology/approachA novel rational expectations framework of financial contagion (Kodres and Pritsker, 2002), along with a combination of robust statistical methods including simple and dynamic correlations and generalized impulse response (Fereidouni et al., 2014) have been employed using data covering three dynamic pre-pandemic economic cycles, namely, global financial crisis (GFC) period, pre-pandemic housing boom and pre-pandemic housing downturn from 2008 (February) to 2019 (December).FindingsResults reveal information linkages across real estate, equity, bond and money markets through correlations in return and volatilities of these series. Finding indicates that the three financial markets (equity, bond and money markets) are interdependent and integrated through information and volatility linkages during the GFC period and pre-pandemic housing downturn period. Financial markets have stronger associations with real estate market during pre-pandemic housing boom. The findings contribute to the general notion that the performances of three financial markets are closely related to the “boom” phase of the real estate cycle.Originality/valueThis research provides an extension of existing literature regarding the information and volatility contagion of the expanded set of core investment markets in Australia. The findings could assist household buyers and investors in designing strategic investment portfolios/hedging strategies and minimizing asset specific risks through diversification over short-term and long-term. In addition, results could support the maintenance, growth and development of a combination of competitive balanced investment markets including real estate, equity, bond and money markets in post-pandemic economy.

Does the world smile together? A network analysis of global index option implied volatilities

This study uses directed acyclic graph and spillover index models to find significant evidence of both implied volatility contagion and spillovers. First, we observe strong regional clustering among the implied volatility smiles of global markets. European and American options markets form a separate contemporaneous contagion cluster from markets in the Asia-Pacific region. However, no market is completely independent from the markets in the other two regions in the long run. The European index options markets demonstrate the strongest implied volatility smile contagion. Second, we observe the heterogeneity across different markets in terms of implied volatility spillover, and extreme market conditions, such as crises, seem to intensify the spillover effects. The trend in the short-run underlying index return, the implied volatility of at-the-money options, and the interest rate term spread are key determinants of implied volatility spillovers.