Risk transmission between equity market of China and its trading partners: new evidence from various financial crises

PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

Unveiling commodities-financial markets intersections from a bibliometric perspective

The prominence of commodity markets within the domains of empirical finance and energy economics is well established, largely due to oil's importance and its relationship with other commodities and financial markets. In this study, we present a bibliometric examination of 437 journal articles addressing the phenomenon of commodity connectedness, spanning the period from 1994 to 2022. The research methods include a blend of qualitative and quantitative approaches, incorporating bibliometrics and content analysis. Based on the findings of the analysis, four primary research streams have been identified within the literature concerning commodity connectedness, namely (1) commodity interconnectivity, (2) the relationship between traditional commodities, renewable energy, and cryptocurrencies, (3) the relationship between oil and stock markets, and (4) studies utilizing copula methods to examine the interconnectivity between oil and financial markets. We proposed 15 future research questions for further investigation in the domain of commodity connectedness, including topics such as the impact of the post-COVID era and global uncertainties on commodity markets, how commodities can address the issue of climate change, the exponential growth of cryptocurrencies as a new financial asset, and the impact of the ongoing Russia-Ukraine conflict on commodity and financial markets.

Re-examining oil and BRICS' stock markets: new evidence from wavelet and MGARCH-DCC

This study examines how the relationship between oil and stock market return of BRICS behaves at different investment horizons. Using data ranging from 2006 to 2020, the wavelet and MGARCH-DCC found that the stock markets' return of Russia, Brazil, and South Africa are comparatively more correlated with oil price return across the investment horizons and more volatile particularly during the Covid-19 period. However, the stock markets' return of China and India is less correlated with oil price return and less volatile. It is also revealed that oil price return leads the BRICS' stock markets' return and both are positively correlated.

Safe havens in Islamic financial markets: COVID-19 versus GFC

Using a bivariate dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) model, this study compares the safe-haven properties of various assets against the major Gulf Cooperation Council (GCC) stock indexes during two periods of financial turmoil, the COVID-19 pandemic and the 2008 Global Financial Crisis (GFC). Sovereign bonds offered the highest hedging benefits under both crises. The traditional safe assets, gold and silver, which were reasonably productive under the GFC, have been less so during the pandemic. The Japanese yen emerged as a very safe choice for investors holding GCC stock indexes. Both sector indexes and stock indexes failed to safeguard investors most of the time during each crisis.

Crude oil, crypto-assets and dependence: the impact of the COVID-19 pandemic

PurposeThis paper aims to empirically investigate the extent to which interdependence in markets may be driven by COVID-19 effects.Design/methodology/approachThe current global COVID-19 pandemic is adversely affecting the oil market (West Texas Intermediate) and crypto-assets markets.FindingsThe authors find that the dependence structure changes significantly after the global pandemic, providing valuable information on how the COVID-19 crisis affects interdependencies. The results also prove that the performance of digital gold seems to be better compared to stablecoin.Originality/valueThe authors fit copulas to pairs of before and after returns, analyze the observed changes in the dependence structure and discuss asymmetries on propagation of crisis. The authors also use the findings to construct portfolios possessing desirable expected behavior.

Le vaccin contre la Covid-19 a-t-il un impact sur le marché financier marocain ?

L'objectif de cet article est d'examiner le co-mouvement et la corrélation dynamique entre l'évolution du taux de vaccination contre la Covid-19 au Maroc et la performance de la bourse de Casablanca, sur la base des données quotidiennes couvrant la période du 1 Mars 2020 au 6 août 2021. Pour ce faire, l'approche de Cohérence en Ondelettes (CEO) a été adopté. Nos résultats empiriques montrent la vulnérabilité de ce marché financier à performer en période de crise sanitaire. De plus, CEO révèle une cohérence forte et significative entre la performance boursière et le taux de vaccination contre la Covid-19, avec une dominance positive de la vaccination sur les rendements.Alternate abstract: The aim of this article is to examine the co-movement and dynamic correlation between the evolution of the COVID-19 vaccination rate in Morocco and the performance of the Casablanca Stock Exchange, based on daily data forecast for the period from March 1, 2020 to March 6, 2020. August 2021. We used the wavelet coherence technique (WCT) for this purpose. Our empirical results reveal the vulnerability of this financial market to perform in times of financial turmoil. In addition, CWT reveals a strong and significant coherence between stock market performance and the COVID-19 vaccination rate, with a positive dominance of vaccination over returns.

COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: a wavelet-based analysis

This paper aims to investigate the impacts of the COVID-19 pandemic and Russia-Ukraine war on the interconnectedness between the US and China stock markets, major cryptocurrency and commodity markets using the wavelet coherence approach over the period from January 1 2016 to April 18 2022. The aim is to understand how the COVID-19 pandemic and the Russia-Ukraine war have affected the hedging efficiency of volatile crypto-currencies and gold. Wavelet coherency analysis unveils perceptual differences between the short-term and longer-term market reactions. In the short-run, we find strong co-movements during the first and second waves of the pandemic. During the first wave, longer-term investors were driven by the belief of future pandemic demise. They make use of time diversification that results in positive returns. During the Russia-Ukraine war, S&P 500 leads Bitcoin, BNB, and Ripple whereas Ethereum leads S&P 500 and SSE. © 2023 The Operational Research Society.

Dynamic connectedness of green bond with financial markets of European countries under OECD economies

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. © 2022, The Author(s).

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants. © 2022 Elsevier B.V.

From sentiment to systemic risk: Information transmission in Asia-Pacific stock markets

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.

Co-movement between dirty and clean energy: A time-frequency perspective

In the backdrop of the recent covid-19 pandemic there is a renewed interest to understand the interlinkages between dirty and clean energies. In this regard, the study examines the co-movement structure between clean energy stocks and dirty energies before and during the covid-19 outbreak. The study analyses the interlinkages between the underlying markets by utilizing a vast sample of dirty energies namely crude oil, heating oil, gas oil, gasoline and natural gas, whereas clean energy sector is proxied by S&P Global clean energy index and Wilder Hill clean energy index. We make use of rolling window wavelet approach and wavelet coherence analysis to identify interdependencies between the clean energy stocks and dirty energies. The results exhibit weak linkages between clean energy equities and dirty energies in the short-run, while;we also record few occasions of high co-movements among dirty and clean energy markets in the long-run. Noticeably, a distinct decoupling effect persisted between dirty and clean energy markets. In addition, the findings also illustrate that clean energy market is relatively isolated from dirty energies during the recent pandemic crisis, amplifying portfolio diversification benefits across clean and dirty energy markets. The findings of the study hold meaningful insights for investors, policy makers and other market participants in energy financial markets.

Impact of the Global Fear Index (COVID-19 Panic) on the S&P Global Indices Associated with Natural Resources, Agribusiness, Energy, Metals, and Mining: Granger Causality and Shannon and Rényi Transfer Entropy.

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths.

Sukuk and bond dynamics in relation to exchange rate

PurposeThis study aims to contribute by expanding the existing literature on Sukuk return and volatility and exploring the implications of the Sukuk-exchange rate interactions. Design/methodology/approachThis study examines the dynamic interactions of Sukuk with exchange rate in 15 countries, employing the Wavelet approach that considers both time and investment horizons. FindingsThe results reveal significant evolving coherence of Sukuk return and volatility with the underlying exchange rate. The relationship is more potent than what this study witnesses in their counterpart bond market. For Sukuk returns, the coherence is negative, whereas it is positive for volatility. Notably, the coherence is strong in the medium to long term and intensifies during extreme economic episodes, especially during the COVID-19 pandemic. These findings are further validated by comparing firm-level matched data for Sukuk and conventional bond. Originality/valueTo the best of the authors' knowledge, this is the first study that reports the dynamic relationship of Sukuk return and volatility with the underlying exchange rate in 15 countries. Collectively, this study unites valuable insights for faith-based active Islamic investors and cross-border portfolio managers.

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.

Environmental, social, and governance (ESG) investing and commodities: dynamic connectedness and risk management strategies

Purpose The purpose of this study is to examine the dynamic connectedness and volatility spillovers between commodities and corporations exhibiting the best environmental, social and governance (ESG) practices. In addition, the authors determine the optimal hedge ratios and portfolio weights for ESG and commodity investors and portfolio managers. Design/methodology/approach This study uses the novel frequency connectedness framework to point out volatility spillover between ESG indices covering the USA, developed and emerging markets and commodity indices, including energy (crude oil, natural gas and heating oil), industrial metals (aluminum, copper, zinc, nickel and lead) and precious metals (gold and silver) by using daily data between January 3, 2011 and May 26, 2021, covering significant socio-economic developments and the COVID-19 outbreak. Findings The results of this study suggest a total connectedness index at a mediocre level, mainly driven by the shocks creating uncertainty in the short term. And the results indicate that all ESG indices are net volatility transmitters, and all commodity indices other than crude oil and copper are net volatility receivers. Practical implications The results imply statistically significant hedging and portfolio diversification opportunities to investors and portfolio managers across the asset classes, proven by the hedging effectiveness analyses. Social implications This study provides implications for policymakers focusing on the risk of contagion among the commodity and ESG markets during turbulent periods to ensure international financial stability. Originality/value This study contributes to the existing literature by differentiating ESG portfolios as the USA, developed and developing markets and examining dynamic connectedness and volatility spillovers between ESG portfolios and commodities with a different technique. This study also contributes by considering COVID-19 outbreak.

How the Cryptocurrencies React to Covid-19 Pandemic? An Empirical Study Using DCC GARCH Model (2019–2021)

The paper aims to examine the dynamic conditional correlation between three major cryptocurrencies: Bitcoin, Litecoin, and Ethereum, during the Covid-19 pandemic. Our sample includes two panels: before/during Covid-19 covering the periods of 01/01/2019 to 12/31/2019 and from 01/01/2020 to 01/03/2021 using hourly data and the DCC GARCH model. The empirical results found that the Covid-19 pandemic positively impacts the cryptocurrencies prices starting from the second semester of 2020. Furthermore, these digital currencies became more vulnerable and showed a high level of volatility, especially Ethereum and Litecoin. Besides, we concluded that there was no significance on the short-run volatility effect during the Covid-19 period, which means the continuity of persistence in the long term. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spillover effects among crude oil, carbon, and stock markets: evidence from nonparametric causality-in-quantiles tests

This study investigates the spillovers and information transmission between carbon, crude oil, and stock markets under various market conditions in Phase III of the EU ETS. For this purpose, we use a novel causality-in-quantiles test method and quantile impulse response functions based on daily data of carbon futures, Brent spot, and three representative equity indices in the Europe over the period from 27 January 2014 to 18 September 2020. We find that crude oil market has a unidirectional spillover effect on carbon market, and this causality is significant under normal to bullish market conditions. Furthermore, the causality-in-quantiles between crude oil and stock markets varies with specific equality index, and the information transmission from crude oil to stock market is strong in the normal stock market but invalid when stock markets become extremely bearish or bullish. The COVID-19 epidemic may cause structural changes in the oil-carbon and oil-stock nexus.

The transition of the global financial markets' connectedness during the COVID-19 pandemic

This study contributes to the literature on financial research under the presence of the COVID-19 pandemic. Fresh evidence emerges from using two novel approaches, namely network analysis and wavelet coherence, to examine the connectedness and comovement of financial markets consisting of stock, commodity, gold, real estate investment trust, US exchange, oil, and Cryptocurrency before and during the COVID-19 onset. Moreover, unlike the previous studies, we seek to fill a gap in the literature regarding the ex-post detection of COVID-19 crises and propose the Markov-switching autoregressive model to detect structural breaks in financial market returns. The first result shows that most financial markets entered the downtrend after January 30, 2020, coinciding with the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern. Thus, it is reasonable to use this date as the break date due to COVID-19. The empirical result from network analysis indicates a similar connectedness, or the network structure, in other words, among global financial markets in both the pre-and during COVID-19 pandemic periods. Moreover, we find evidence of market differences as the MSCI stock market plays a central role while Cryptocurrency presents a weak role in the global financial markets. The findings from the wavelet coherence analysis are quite mixed and illustrate that the comovement of the financial markets varies over time across different frequencies. We also find the main and most significant period of coherence and comovement among financial markets to be between December 2019 and August 2020 at the low-frequency scale (>32 days) (middle and long terms). Among all market pairs, the oil and commodity market pair has the strongest comovement in both pre-and during the COVID-19 pandemic phases at all investment horizons.

Dynamic connectedness of green bond with financial markets of European countries under OECD economies

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.

CAUSALITY, INFORMATION FLOW, AND CO-MOVEMENT ANALYSIS OF MAJOR STOCK INDICES

Stock indices are key indicators of the economy since they indicate the strength of a country's stock market. For this reason, causality, information flow and co-movement analysis of stock indices gain importance in comparing countries' economies. Here, we apply a novel approach by analyzing the results of two different methodologies;in wavelet coherence (WTC) analysis, the co-movement between stock indices provided and coherent areas can be shown, and information flow is indicated for five-year periods, especially on coherent zones by Transfer Entropy (TE), which detects cause-and-effect relations. This paper analyzed the information flow and co-movement among FTSE100 in the United Kingdom, the DAX in Germany and S&P500 Index in the United States stock indices. Three different results are obtained as follows: (1) DAX is on the leading side in general for five-year periods, (2) bidirectional information flows arise for every pair in the coherent periods and (3) TE-guided WTC analysis shows that TE sign change can be explained by phase angle direction obtained with WTC. These results indicate that both the methods yield proper outcomes in coherent time zones and during financial crisis like the COVID period, which we have faced for two years;for this reason, the results were also obtained for the COVID period, and in general, that shows DAX dominated other indices. We published this study to help researchers understand the connectedness between stock indices and investors avoiding risk in their stock portfolios, especially during financial crisis periods. © 2022 World Scientific Publishing Company.