Volatility Transmission Research Articles

Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

PurposeThis paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.Design/methodology/approachThe connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.FindingsThis analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.Originality/valueThe connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Investment modeling between energy futures and responsible investment

This research examines the hedging and diversification ability of energy futures (crude oil, heating oil, natural gas, gasoline, carbon) concerning investment in four different segments of responsible investment. Wavelet coherence and DCC-GARCH are used to inspect the association and volatility transmission between them. Conditional correlation, optimum portfolio weights and hedge ratios are also calculated for real-time data and decomposed series to analyze the optimum portfolio structure. Our analysis reveals that energy futures are more efficient as a hedge to responsible investment during the short-run period. Natural gas is the perfect hedge against all the segments of responsible investment. Each segment of energy futures can hedge investments in sustainability stocks and green bonds. Carbon and Gasoline are not the perfect hedge for ESGL and GCEI but can be used as a cheap hedge. Our study will help responsible investors to hedge their portfolio risk using related assets, countering their familiarity bias.

Interconnected Markets: Unveiling Volatility Spillovers in Commodities and Energy Markets through BEKK-GARCH Modelling

Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how time-varying the spillovers were across a time. Data were daily frequency (prices of grains and energy products) from 1 July 2019 to 31 December 2022, as quoted in markets. The choice of the period was to capture the COVID pandemic and the Russian–Ukrainian war as events that could impact volatility. The returns were duly calculated using spreadsheets and subjected to ADF stationarity, co-integration, and the full BEKK-GARCH estimation. The results revealed a prolonged association between returns in the energy markets and food commodity market returns. Both markets were found to have volatility persistence individually, and time-varying bidirectional transmission of volatility across the markets was found. No lagged-effects spillover was found from one market to the other. The findings confirm that shocks that emanate from fluctuations in energy markets are impactful on the volatility of prices in food commodity markets and vice versa, but this impact occurs immediately after the shocks arise or on the same day such variation occurs.

Stock market spillovers of global risks and hedging opportunities

In this paper, we shed light on the linkage between several global risks and stock market returns. We use a dataset including G7, BRICS, MENA, and SAHEL group countries for a period of more than 30 years. We explore the spillover effects on global stock markets fueled by geopolitical, climatic, and global health risks. We provide evidence of a significant negative impact of geopolitical shocks on stock markets across countries. Further, we document that geopolitical shocks contribute to the spillover transmission of stock market volatility We also document that climate change and the global health crisis constitute risks that should be considered as a factor that heightens geopolitical risk. Finally, we identify possible strategies to hedge against geopolitical risk using a specific set of financial assets such as artwork.

Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH

The objective of this paper is to assess the dynamic volatility connectedness between fossil energy, clean energy, and major assets i.e., Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500 from September 17, 2014 to October 11, 2022. The main motivation of the study relates to examining the dynamic volatility connectedness mentioned during periods of important events such as the recent coronavirus pandemic and the Russia–Ukraine conflict which has shown the vulnerability of economic and financial assets, energy commodities, and clean energy. The novel Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) approach is employed for the investigation of the sample period mentioned. Empirical analysis reveals that both the total and net volatility connectedness between assets is time-varying. The highest connectedness among the assets is observed with the onset of the coronavirus (COVID-19) pandemic, and it increases with some important international events, such as the Russia–Ukraine conflict, the referendum of Brexit, China–US trade war, and Brexit day. On average, the result shows that 32.8% of the volatility in one asset spills over to all other assets. The DCC-GARCH results also indicate that crude oil, bonds, and Bitcoin act as almost pure volatility transmitters, whereas the Dollar index, gold, and S&P500 act as volatility receivers. On the other hand, clean energy is found neutral to external shocks until the first quarter of 2020 and after that time, it starts to behave as a volatility transmitter. Based on the obtained results, we offer some specific policy implications that are beneficial to the US economy and other countries.Graphical Dynamic volatility connectedness between fossil energy, clean energy, and major assets (Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500)

Return and Volatility Spillover in Equity and Commodity Market: Some Indian Evidence

This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns’ causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43

Bitcoin price volatility transmission between spot and futures markets

Bitcoin price volatility transmission between spot and futures markets

The interconnectedness between crude oil prices and stock returns in G20 countries

This study examines the link between crude oil prices and stock returns in G20 countries. Using the dynamic connectedness approach and a dataset from March 24, 2014 to December 15, 2023, we found volatility transmission between stock returns and crude oil prices. Specifically, we observed that an average of 51.22% of shock on one asset spills over to all other assets. Additionally, our study identified the CAC 40 (France), FTSE 100 (United Kingdom), TMX (Canada), and IGBM (Spain) as major transmitters of shocks. For an in-depth understanding of the effects of crude oil prices on the stock market, we divided the countries into oil-exporting and oil-importing nations. We discovered that shock transmission in oil-exporting countries is negligible, while oil-importing countries experience significant shock transmission. Our findings have important implications for investors and policymakers, as they can maximize returns for the former and help the latter in stabilizing volatile markets.

The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain

This paper investigates the relationship between domestic and export prices of rare earth elements (REEs) taking into account both, the time and the frequency domain using Granger causality tests and cross wavelet analysis. We find areas of significant comovement between both, returns and volatility of domestic and export prices, with the degree of connection being low at higher frequencies, increasing over medium frequencies and lower frequencies. Moreover, the lead-lag effects between the Chinese and the export market are time-varying and heterogenous, with no particular market as the leader. The analysis of volatility transmission shows that own-market volatility rather than spillover from the Chinese to the export market or vice versa accounts for the biggest share of volatility. While volatility transmission shows substantial variation over time, the Chinese market is generally a net giver of volatility to the export market, especially at the medium frequency band. Accordingly, market participants buying REEs in the export market might want to track Chinese prices as leading indicators of price fluctutations due to spillovers.

Can fiat currencies really hedge Pax gold? Evidence from range-based DCC models

PurposeThis paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.Design/methodology/approachThis paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.FindingsWavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.Practical implicationsA comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.Originality/valueThis study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China’s emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers.

Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets

Should investors and policy makers in agricultural markets consider oil market's incontestable impact on portfolio risk management? This paper investigates the time-varying market linkages between energy and agricultural commodities in the presence of two important exogenous shocks, viz., the COVID-19 pandemic and the subsequent 2022 Russia–Ukraine military conflict. We use a novel time-varying parameter vector autoregressive model with a common factor error structure to estimate the tail connectedness between energy and agricultural commodities for the period December 31, 2019 to December 18, 2023. Our findings provide clear evidence of asymmetry in the volatility evolution. We determine that volatility spillover magnitudes are much stronger across quantiles than at the mean. We note that crude oil is the main transmitter of shocks in the system before the onset of the 2022 Russia-Ukraine conflict at the lower tail of the distribution. While crude oil and natural gas transmit volatility in both pre- and post-conflict announcement periods. Furthermore, the 2022 Russia–Ukraine conflict is found to impact the transmission of volatility between energy and agricultural commodities. Numerous agricultural commodities are observed to shift their position from transmitters to receivers of volatility, and vice versa, due to the military conflict in Ukraine. Our causality results depict time-varying patterns in the connectedness between crude oil and other commodities. We determine that crude oil has varying impact on agricultural markets in pre- and post-conflict announcement periods. Commodities for which both conflicting countries are major world exports of, such as wheat, have notably increased their dependency on crude oil. Thus, we advise investors and policymakers in agricultural markets to seriously consider oil market's impact on portfolio risk management and monitoring policies.

Vulnerability of a developing stock market to openness: One-way return and volatility transmissions

Drawing from two independent literature sources on vulnerability and stock price volatility, we show that the high magnitude and persistence of stock return volatility evident in the Nigerian Exchange Group (NGX) are, at least in part, indications of vulnerability to openness, which paves the way for the transmission of external shocks. Using a uniquely modified set of bivariate asymmetric GARCH models, we investigate one-way return and volatility transmissions from the international Brent oil market, natural gas markets, carbon markets, and naira-dollar exchange market to the NGX. Firstly, we document direct and asymmetric return and volatility transmissions from the Brent oil market to the NGX, denoting the fragile sensitivity of the NGX to international oil price turbulence. Secondly, we find a direct volatility transmission from natural gas markets to the NGX, implying an increasing reliance on natural gas exports by the Nigerian government. Thirdly, we document an inverse volatility transmission from carbon markets to the NGX, indicating Nigeria's negative reaction, as a major oil and gas producer, to increasing global sustainable production activities. Fourthly, the insignificant effect of the naira-dollar official exchange rate on volatility in the NGX demonstrates the spurious impression that Nigeria's exertion of control over the exchange rate is shielding the economy. In sum, our results confirm that the openness of the NGX makes it vulnerable to external shock transmissions from related external markets subsisting outside Nigeria. Our results are robust to an alternative estimation procedure that employs a different measure of price volatility from the three external markets.

Dynamic connectedness and hedging opportunities of the commodity and stock markets in China: evidence from the TVP-VAR and cDCC-FIAPARCH

This study examines the dynamic connectedness and hedging opportunities between CSI300 (China Security Index 300) and copper, gold, PTA (purified terephthalic acid), and soybean in China from January 09, 2008, to June 30, 2023. A TVP-VAR and cDCC-FIAPARCH modeling framework was used for the empirical investigation. The results show that the total connectedness index can effectively capture cross-asset information transmission in China’s financial markets. Copper returns are the dominant volatility transmitters, while CSI300, gold, and soybean returns are net recipients. The Russian–Ukraine war reinforced the safe-haven role of gold. Finally, investors with CSI300 long positions may benefit from prioritizing gold for hedging, while those with CSI300 short positions profit more from allocating gold to PTA. Portfolio managers and investors can use the findings to track the dynamics of systemic risk and adjust their long/short positions when investing in China's stock and commodity markets.

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.

Global financial risk and market connectedness: An empirical analysis of COVOL and major financial markets

This study presents a novel investigation into the dynamic connectedness between the recently developed global risk measure, COVOL, created by Engle and Campos-Martins (2023), and the major international stock, gold, and crude oil markets in China, the US and Europe. Applying conditional and aggregated connectedness approaches, we systematically uncover the multi-dimensional interlinkages to understand volatility transmissions within these markets. Results reveal that COVOL predominantly receives volatility transmissions, with the US and European stock markets acting as principal transmitters of shocks across all three regional systems. The research also highlights the crucial role of significant events, such as the Global Financial Crisis, the Chinese stock market crash, and the COVID-19 pandemic, when increasing the intensity of connectedness. The volatility transmission of the COVOL-gold pairing is comparatively weaker than the COVOL-stock and COVOL-oil pairs. At the same time, the US and Europe consistently transmit stronger shocks to global financial risk, contributing significantly to the global risk transmission channel, whereas, in contrast, China’s financial markets transmit less international uncertainty. Robustness testing verifies these findings.

Volatility transmission between upstream and midstream energy sectors

This research studies volatility dynamics of the returns to upstream (exploration and production) and midstream (pipeline and storage) energy (oil and gas) sectors over time and across sectors. We find significant structural breaks in volatility of upstream sector returns possibly triggered by major events, while no breaks are detected in the midstream sector. These results indicate the midstream sector is fairly insulated from major events relative to the upstream sector. Results based on a bivariate generalized ARCH model, which controls for breaks, indicate that the conditional variance of the upstream sector is significantly more affected by midstream volatility than vice versa. This finding could be due to the fundamental difference in functions performed by the midstream and upstream sector firms. These patterns in volatility dynamics have practical significance for financial market participants.

Agricultural commodities market reaction to COVID-19

This study examines the volatility transmission mechanism in agricultural futures returns during crisis periods, with a specific focus on the COVID-19 pandemic. The research employs the Markov Switching model to analyze the market dynamics of key agricultural commodities—cotton, sugar, rice, wheat, and corn. The study period is divided into three phases: Pre-COVID, COVID, and Post-COVID, allowing for a comprehensive analysis of market trends and shifts in response to the pandemic. Key findings reveal significant disruptions and heightened volatility in these markets during the COVID-19 period, with notable deviations from traditional market behaviors. The research reveals how various macroeconomic factors, such as the S&P 500, US 10-year Treasury bills, Brent crude oil prices, and the US Dollar Index, distinctly affect these commodity markets. Crucially, it sheds light on the dual nature of these markets, demonstrating both their resilience and susceptibility to worldwide disruptions, along with their recovery patterns following the pandemic. The policy implications of these findings are crucial for investors and policymakers. The study provides insights into managing and mitigating the impacts of global crises on essential commodities, emphasizing the need for robust strategies to navigate future market uncertainties. Additionally, the research contributes to the broader discourse on the interplay between macroeconomic conditions and agricultural market dynamics, offering valuable perspectives for informed decision-making in these sectors.

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.

Volatility spillovers among leading cryptocurrencies and US energy and technology companies

This study investigates volatility spillovers and network connectedness among four cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin), four energy companies (Exxon Mobil, Chevron, ConocoPhillips, and Nextera Energy), and four mega-technology companies (Apple, Microsoft, Alphabet, and Amazon) in the US. We analyze data for the period November 15, 2017–October 28, 2022 using methodologies in Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Economet 16(2):271–296 2018). Our analysis shows the COVID-19 pandemic amplified volatility spillovers, thereby intensifying the impact of financial contagion between markets. This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets. Moreover, we show that Bitcoin, Ethereum, Chevron, ConocoPhilips, Apple, and Microsoft are net volatility transmitters, while Tether, BNB, Exxon Mobil, Nextera Energy, Alphabet, and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium- and long-term spillovers, and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected. Furthermore, in contrast to short-term dynamics, longer term patterns display superior hedging efficiency. The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies. The findings in this study have implications for both investors and policymakers.