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Although the spillover effects of return and volatility risk across commodity markets have been demonstrated, evidence of extreme risk spillovers is limited. Using an autoregressive conditional density model, this study estimates the conditional skewness of nine S&P Goldman Sachs Commodity indices and then applies the Diebold-Yilmaz TVP-VAR-based approach to investigate the higher moment spillovers across commodity markets. Our findings provide evidence of extreme risk transfers from one commodity index to another. Among three energy indices including crude oil, natural gas and gasoil, crude oil transmits the most return, volatility risk and extreme risk to the agricultural indices and precious metal indices. Furthermore, our results confirm that spillovers in all three moments were significantly strengthened by extreme events such as the September 11 attacks, the global financial crisis, the food price crisis, the violent shock of international oil prices and the coronavirus disease of 2019. However, different events may have different impacts on spillovers. Finally, the results indicate that return spillover and skewness are affected by extreme events with almost the same intensity and direction for most periods.
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By regarding the Chinese financial and economic sectors as a system, this article studies the stock volatility spillover in the system and explores its effects on the overall performance of the macroeconomy in China. The recent outbreak of COVID-19, U.S.-China trade friction, and three historical financial turbulences are involved to distinguish the changes in the spillover in these distinct crises, which has seldom been unveiled in the literature. By considering that the stock volatility spillover may vary over distinct timescales, the spillovers are disclosed through innovatively constructing the multi-scale spillover networks, followed by connectedness computation, based on variational mode decomposition (VMD) and generalized vector autoregression (GVAR) process. Our empirical analysis first demonstrates the different levels of increases in the total sectoral volatility spillover and changes in the roles of the sectors in the system under the aforementioned crises. Besides, the increases in the sectoral spillover in the long-term are verified to negatively impact the macroeconomy and can thereby act as warning signals.
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We investigate connectedness between energy cryptocurrencies and common asset classes, including oil, using TVP-VAR modeling, evidencing that energy cryptocurrencies, as diversifiers, normally have strong connections with bitcoin and nothing else. However, their connectedness to other assets changes rapidly during shocks such as COVID-19 and the start of the Russian-Ukraine war. Connectedness spiked in April 2020, when WTI oil prices fell to negative pricing. Economic policy uncertainty, Twitter-based uncertainty, and infectious disease-related uncertainty all have significant impact on the system's total connectedness. Energy cryptocurrencies, while normally diversifiers, are highly sensitive to shocks and changes in uncertainty.
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Because of the increasing importance of and demand for ethical investment, this paper investigates the dynamics of connectedness between sustainable and Islamic investment in nineteen countries that represent developed and emerging financial markets worldwide. To this end, we apply models proposed by Diebold and Yilmaz and Barunik and Krehlik to explore the overall and frequency-based connectedness between selected ethical investments. Our results reveal evidence of a moderate to strong intra country-level connectedness between sustainable and Is-lamic investment and limited cross-country connectedness between ethical investments. The time-varying connectedness analysis suggests enhanced connectedness during periods of market-wide turmoil, such as the European debt crisis, the Chinese financial crisis, and the COVID-19 pandemic. Moreover, the COVID-19 subsample analysis shows an enhanced and idiosyncratic country-level and cross-country connectedness structure between ethical investments, indicating the evolving nature of the relationship between sustainable and Islamic investment. Copyright (c) 2022 Borsa Istanbul Anonim S,irketi . Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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PurposeThis paper examines the time-varying return connectedness between renewable energy, oil, precious metals, the Gulf Council Cooperation region and the United States stock markets during two successive crises: the pandemic Covid-19 and the 2022 Russo-Ukrainian war. The main objective is to investigate the effect of the Covid-19 pandemic and the Russo-Ukrainian war on the connectedness between the considered stock markets. Design/methodology/approachThis paper uses the time-varying parameter vector autoregression approach, which represents an extension of the Spillover approach (Diebold and Yilmaz, 2009, 2012, 2014), to examine the time-varying connectedness among stock markets. FindingsThis paper reflects the effect of the two crises on the stock markets in terms of shock transmission degree. We find that the United States and renewable energy stock markets are the main net emitters of shocks during the global period and not just during the two considered crises sub-periods. Oil stock market is both an emitter and a receiver of shocks against Gulf Council Cooperation region and United States markets during the full sample period, which may be due to price fluctuation especially during the two crises sub-periods, which suggests that the future is for renewable energy. Originality/valueThis paper examines the effect of the two recent and successive crises, the Covid-19 pandemic and the 2022 Russo-Ukrainian war, on the connectedness among traditional stock markets (the United States and Gulf Council Cooperation region) and commodities stock markets (renewable energy, oil and precious metals).
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This study aimed to investigate the return connectedness between Sukuk and green bonds at the middle, left and right tail using the new quantile-based connectivity methodology from Ando et al. (2018). We find that the average level of connectedness estimated at the mean/median is lower than that estimated at the left and right quantiles. Therefore, return connectedness between Sukuk and green links is higher in the left and right tails, indicating that the application of the mean-based connectivity measure is inappropriate. Next, we show that the connectedness of returns varies over time but varies less in the tails. In particular, the dynamic connectivity analysis indicates that the COVID-19 pandemic has significantly impacted the Sukuk and green bond markets. The Return connectedness driver's analysis shows the importance of macroeco-nomic conditions, particularly in the middle and lower quintiles. The US dollar bodes well positively for both bears and bulls, while uncertainty in equity markets amplifies return spillovers in the lower quintile. Moreover, the weak return spillovers between Sukuk and green bonds in-dicates that there is indeed an opportunity for optimal asset allocation. The highest hedging ef-ficiency can be achieved by taking short positions in US Green Bonds.
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This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they in-crease dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers.
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This study provides a comprehensive sentiment connectedness analysis in Asia-Pacific. We implement a time-frequency framework and a quantile connectedness approach while analyzing the impact of three crises: the global financial crisis, the Chinese Stock market turbulence (2015-2016), and the COVID-19 pandemic. We find a significant sentiment spillover across markets, though the magnitude is more pronounced in the long run. Although sentiment connectedness is higher during extreme states of the sentiment than in the average state, the systemic risk intensifies further when the sentiment is exceptionally high. Notably, Japan appears to contribute moderately to the sentiment network, while China is the lowest contributor. The three crises strengthened the total sentiment connectedness, while the COVID-19 pandemic had the most substantial impact. Our sentiment network findings have insightful implications on cultural and behavioral factors that drive sentiment systemic risk in Asia-Pacific.
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PurposeThis study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets. Design/methodology/approachThe authors use time-frequency connectedness method to study the uncertainty spillover among the asset classes. FindingsThis study's findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility. Originality/valueThis study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.
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Models of crisis prediction continue to gain traction with the increased frequency of global crisis such as the ongoing COVID-19 pandemic. Moreover, the connectedness of financial markets appears to be of central importance in determining how shocks spill through asset market linkages. The study thus applies the time-frequency connectedness measures of Diebold & Yilmaz (2012) and Barunik & Krehlik (2018) to examine return and volatility connectedness dynamics in East African Community (EAC) member states. The study found a strong interdependence among the considered EAC markets as indicated by the high values of total return and volatility spillover indices. This high degree of interdependence is reflected in both static time and frequency domain return and volatility connectedness, especially at the longer term frequency bands, an indication that return and volatility shocks are persistent. This result lends further support to existing evidence on the suitability of the EAC regional economic integration, including the possible eventual establishment of a monetary union. In addition, the dynamic spillover analysis indicates that connectedness among these EAC markets is highly time-varying and appears to be amplified during global crisis events such as the European debt crisis, Kenyan elections, commodity price shocks and the COVID-19 pandemic. However, the results suggest that relative to periods of domestic turbulence, financial market connectedness in the EAC is more likely to get amplified during periods of external global shocks. The study also contributes to emergent literature on connectedness among financial markets during the COVID-19 pandemic. Importantly, the study finds that the COVID-19 pandemic had a significant effect on all the considered EAC markets although the magnitude and direction of impact varies across markets and countries. In addition, the study finds that Brent Crude oil prices are a significant source of return and volatility spillovers to EAC markets especially during crisis periods.
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The CEE stock markets are more and more integrated in the European financial markets. The growth of the integration of financial markets favours the volatility and return spillover between them. The current study analyses the volatility spillover among the stock markets in the countries from Central and East Europe (CEE) and Germany and France with the aim to identify the pos-sibilities of reduction of a portfolio risk. A special attention is granted to the analysis during the pandemic caused by COVID-19. The time-varying parameter vector autoregressive (TVP-VAR) model on which is based the methodology proposed by Antonakakis and Gabauer (2017) is used to estimate the evolution in time of volatility spillover. The empirical results obtained for the period January 2001 - September 2021 highlight the increase in volatility spillover between the countries analysed when the pandemic caused by COVID-19 was confirmed. The lack of volatility integration of the markets analysed enables the making of arbitrages in order to reduce the risk of a portfolio. The results obtained are important in the management of financial asset portfolios.
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PurposeThis work investigates the volatility spillovers across stock markets and the nature of such spillovers through different periods of crises and tranquility.Design/methodology/approachUsing daily stock return volatility data from June 2003 to June 2021, the generalized forecast error variance decomposition method (based on Diebold and Yilmaz, 2012 approach) is employed to measure the degree of volatility spillovers/connectedness among stock markets of 24 Asia-Pacific and 12 European Union (EU) economies.FindingsThe empirical results from static analysis suggested that about 28.1% (63.7%) of forecast error variance in return volatility for Asia-Pacific (EU) markets is due to spillovers. The evidence from dynamic analysis suggested that during mid of the global financial crisis, European debt crisis (EDC) and Covid-19, the gross volatility spillovers for Asia-Pacific (EU) was around 67% (80%), 65% (80%) and 73% (67%), respectively. The degree of net volatility transmission from Singapore (Denmark) to other Asia-Pacific (EU) markets was found to be highest.Practical implicationsThe findings have crucial implications for the investors and portfolio managers in assessment of risk and optimum allocation of assets and investment decisions.Originality/valueThis study adds to the literature on risk management by systematically examining the impact of global financial crises, EDC and Covid-19 on the market interactions by capturing the magnitude, duration and pattern of the shock-specific market volatilities for a large sample of Asian and European markets using recent and large data set.
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Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011-10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers.
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In this paper, we employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to study interlinkages between the cryptocurrency and Vietnam's stock market by characterizing their connectedness starting from January 1, 2018, to December 31, 2021. We report that the COVID-19 health shocks impact the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that the cryptocurrency market significantly impacts Vietnam's stock market, especially those with the largest market capitalization like Bitcoin and Ethereum. This market can be held accountable for Vietnam's stock market volatility. In encountering the COVID-19 pandemic, the effect of the three cryptocurrencies reduced before 2020, around the end of 2019 and the beginning of 2020. However, from the end of 2020-2021, while cryptocurrencies continued their roles as net transmitters for Vietnam's stock market.
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Purpose The authors attempt to explore fat tails and network interlinkages of oil prices and the six largest cryptocurrencies from 1st January 2018 and 1st August 2021. The authors also investigate the influences of the COVID-19 pandemic on these network interlinkages. Design/methodology/approach The authors follow Diebold and Yilmaz (2012) to calculate the spillover index the dynamic correlation coefficient model firstly employed by Engle (2002) to study how the volatility of oil prices are transmitted to those of cryptocurrency return and liquidity and vice versa. Findings The results confirm the presence of time-varying interlinkages between the volatilities of the oil market and the cryptocurrency market. Notably, uncertain events like the COVID-19 health crisis significantly influence the time-varying interlinkages they augment dramatically during the COVID-19 health crisis. The turbulence of the cryptocurrency market, especially from Bitcoin and Ethereum, significantly impacts those of the oil market. The role of the oil market in transmitting the effect of respective shocks to the cryptocurrency market, on the other hand, is time-varying, which is only reported when the COVID-19 pandemic first appeared at the beginning of 2020. The turbulence of the cryptocurrency market in the system is greatly explained by themself rather than a transmission mechanism of shocks to the oil market. Practical implications Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets. Originality/value The most significant benefit of the approach is how simple it is to calculate net pairwise connectivity, which identifies transmission channels between these commodity and financial markets. The authors are also the first to use the quasi-maximum likelihood (QML) estimator to estimate the DCC model to measure the volatility spillover index to reflect the level of interdependence between the different markets. By using a daily and up to date database, the authors can observe the role of each market in transmitting and receiving the shocks between two different sub-periods: (1) before and (2) during the COVID-19 pandemic crisis.
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Purpose This study examines the information transmission (return and volatility spillovers) among energy commodities (crude oil, natural gas, Brent oil, heating oil, gasoil, gasoline) and Asian stock markets which are net importers of energy (China, India, Indonesia, Malaysia, Korea, Pakistan, Philippines, Taiwan, Thailand). Design/methodology/approach The information transmission is investigated by employing the spillover index of Diebold and Yilmaz, using daily data for the period January 2000 to May 2021. Findings A Strong connectedness is documented between the two classes of asset, especially during crisis periods. Our findings reveal that most of the energy markets, except gasoil and natural gas, are net transmitters of information, whereas all the stock markets, excluding Indonesia and Korea, are net recipients. Practical implications The findings are helpful for portfolio managers and institutional investors allocating funds to various asset classes in times of crisis. Originality/value All data is original.
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This study uses a high-dimensional time-frequency volatility spillover model to examine risk interactions across 32 global stock and credit default swap (CDS) markets during the coronavirus (COVID-19) pandemic. Our empirical results mainly show that cross-market risk spillovers between these two types of markets are rare over the whole crisis period. Adding CDS assets to the stock portfolio would significantly decrease the overall risk spillovers in the mixed portfolio. Then, the time-frequency spillover and rolling window analyses confirm this finding and provide further evidence of frequency heterogeneity of risk spillovers during the pandemic. The outbreak of the COVID pandemic only sharply increases short-term risk spillovers between the stock and sovereign CDS market but exerts insignificant impacts on the medium and long-term cross-market risk spillovers. Moreover, our results show that developed countries are net transmitters in the stock markets. In contrast, the emerging markets account for the most risk spillovers in sovereign CDS markets over the sample period, emphasizing the heterogenous adverse impacts of the pandemic across various countries and markets. Finally, we find that bailouts implemented by the US and EU central banks, though with heterogeneous impacts across frequencies, gradually enhance the confidence and resilience of investors in these markets.
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This paper examines the dynamic connectedness between green bonds and OECD financial markets of European countries. The study is conducted on daily price of green bonds and selected European stock markets from January 27, 2015, to August 4, 2021. Top ten European countries namely Luxembourg, Switzerland, Norway, Denmark, Germany, Netherlands, Iceland, Austria, Sweden, and Belgium are included within the OECD economies. The study uses Diebold and Yilmaz and Barunik & Krehlic tests to examine the connectedness between the economies and green bonds in short, medium, and long term. Result exhibits volatility across all frequency cycles. Brussel Stock Exchange and Euronext Amsterdam are identified as high-risk markets in the OECD European market. Evidence emerging from this study advocate the inclusion of green bonds in these financial markets for shorter time periods only. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, suggesting incorporation of green bonds in investor portfolio for efficient diversification of risk.
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As China's carbon market continues to develop, its close connection with the financial and energy markets is becoming increasingly apparent. A systematic study of the spillover effects between markets is important, as it can help prevent excessive fluctuations in carbon prices. With this in mind, this study proposes a time-varying parameter vector autoregression with Lanne-Nyberg decomposition extended joint connectedness approach to analyze quantitatively the spillover effects in the "carbon-energy-financial" system. Empirical results show that a bidirectional spillover effect exists among markets. Not only does the carbon market have the most pronounced return (volatility) linkages with the natural gas (clean energy) market, but the information connected with the energy markets is also more closely linked than with the financial markets. We also find that market fluctuations, caused by the China-US trade conflict and the COVID-19 pandemic, have increased spillovers in the system.
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We contribute to the literature on the linkage among shocks in oil price and stock markets fluctuations in oil-exporting economies. We evaluate the effects of COVID-19 pandemic on the magnitude and persistence of responses of stock markets to oil price sudden changes both before and during the pandemic. We also identify the most important structural breaks in stock markets during the period 2013-2021. We find evidence that the majority of developing oil-exporting countries faced a major structural break in their stock markets in 2014 following the spectacular fall of oil price in that year. By contrast, their developed counterparts have been subject to structural break during the CIVID-19 pandemic. The outcomes of the generalize d impulse response analysis suggests that, shocks in oil price tend to hit oil exporters more severely throughout the COVID-19 pandemic than before the outbreak of the pandemic. © 2022 IEEE.