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This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

This paper aims to analyze the volatility transmission between an energy stock index and a financial stock index in emerging markets during recent high instability periods. The study considers the impact of both the period under analysis and the data frequency on the direction and intensity of the contagion, as well as the effect of the potential spillovers on the risk measures. These questions still lack definitive answers and have become more relevant in a context of financially unsettling events such as COVID-19 crises.

This study employs an extension of the dynamic conditional correlation (DCC) model that allows for the time-varying dependence relationship between the variables. This dependence is analyzed at daily, weekly and monthly basis using data from the Bloomberg platform on energy and stock market indices for emerging markets between 2001 and 2021.

The results for a sample spanning from 2001 to mid-2021 show bidirectional volatility transmission on a daily basis, whereas only evidence of volatility transmission from the financial to the energy exists for weekly and monthly frequencies. However, considering different subsamples of daily data, the authors only find volatility transmission from financial (energy) index to the energy (financial) during the Great Recession (COVID-19) as a consequence of the different source of the shock and transmission channels.

This study reveals that volatility transmission between energy and stocks in emerging markets has changed and presents a unidirectional pattern from energy to financial markets during the COVID-19 period in contrast to calm and the sub-prime crisis intervals. These results differ from previous studies, focused on global markets, that show bidirectional spillovers during this period.

This paper aims to investigate the return links and volatility transmission between five major equity markets of the Latin American region and the USA over the period 1993-2012.

The authors employ a multivariate vector autoregressive moving average – generalized autoregressive conditional heteroskedasticity (VAR-GARCH) methodology which allows for cross-market transmissions in both return and volatility. Moreover, we show how the obtained results can be used to design internationally diversified portfolios involving the Latin American assets and to analyze the effectiveness of hedging strategies.

The results point to the existence of substantial cross-market return and volatility spillovers and are thus crucial for international portfolio management in the Latin American region. However, the intensity of shock and volatility cross effects varies across the studied markets.

The optimal weights and hedging ratios that we compute from the observed return and volatility spillovers, suggest that adding the Latin American assets helps improve the risk-adjusted return of the internationally diversified portfolios as well as reduce their risk exposure. For policymakers and market authorities, an increase in the level of shock interactions and volatility transmission between the US and Latin American equity markets as well as among these Latin American markets implies that the stability of the financial system in one country can be deeply affected by the disturbances in another country.

The authors extend the previous works on Latin American emerging markets by examining the extent of shock and volatility transmission as well as portfolio design and management from the point of view of both the US (global) and Latin American investors.

The purpose of this paper is to investigate the volatility linkage between global oil market and major South Asian equity markets.

In order to serve the purpose, the authors employ a recently developed vector autoregressive-generalized autoregressive conditional heteroskedastic model to examine whether shocks and volatility spill over from the oil market to various equity markets under consideration.

The findings of the empirical analysis suggest that all the markets studied do receive volatility from the oil market. Not surprisingly, the authors do not find any significant evidence of volatility transmission from the equity markets to the global oil market. Additionally, while computing the optimal portfolio weights and hedge ratios, the authors document that inclusion of oil in the portfolio of stocks tends to reduce the risk of the resultant portfolio.

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In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This study aims to investigate the transmission of shock between the oil market and the Islamic and conventional stock markets of the Gulf Cooperation Council (GCC) countries during the oil shocks of 2008 and 2014.

This study uses two models. First, the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic model has been used to capture the fundamental contagion effects between the oil market and the Islamic and conventional stock markets during the tranquil and turmoil-crisis periods of 2008-2014. Second, the filter of Kalman has been used to capture the effects of pure contagion between the oil market and the GCC Islamic and conventional stock markets. The authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices.

The main findings of this investigation are: first, the estimation of the dynamic conditional correlation– generalized autoregressive conditionally heteroskedastic model for oil market and the Islamic and conventional stock markets proves that the Islamic and conventional stock markets and oil market displayed a significant increase in the dynamic correlation during the turmoil period, from mid-2008 and mid-2014. This proves the existence of contagion between the markets studied. Second, the authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices. They show a strong increase in the correlation coefficients between the oil market and the conventional GCC stock markets, and between the conventional and Islamic GCC stock markets during the oil crisis of 2014. However, there is no change in regime in the figure of the correlation coefficient between the oil market and the GCC Islamic stock markets during the 2008 financial crisis. This pure contagion is mainly attributed to the herding bias in 2014 oil crisis.

This study contributes to identifying the contribution of herding bias on the volatility transmission between the oil markets, and the GCC Islamic and conventional stock market, especially during two controversial shocks: the 2008 oil-price increase and the 2014 oil drop.

The purpose of this paper is to empirically examine return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

The authors make use of an innovative new methodology of capturing spillovers, which is different from what many existing studies use. The authors employ the measures of return spillovers and volatility spillovers of Diebold and Yilmaz (2009, 2012), referred to as spillover indexes. The spillover index facilitates an assessment of the net contribution of one market in the information transmission mechanism of another market.

The empirical results show bi-directional, but weak interdependence between the South African and Nigerian stock markets returns and oil market returns. The results for volatility spillovers show independence of volatilities between Nigeria stock markets and oil markets, while weak bi-directional spillovers were found between South African equity volatilities and oil volatilities. The time-varying total spillover plots for returns and volatilities are broadly similar and show a trend that has been observed in other studies: an increasing trend during the non-crisis period, a burst in the crisis year, a maintained higher level of transmission afterwards.

Existing studies examining spillovers between oil and stock markets have largely ignored Sub-Saharan African markets. A common feature of existing studies is that they have been conducted for two groups of countries: either European and US markets; or Gulf Cooperation Council markets Thus, this study fills this gap in the literature by examining return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.

Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.

Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.

Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

This paper aims to employ GARCH-class models (GARCH, IGARCH and CGARCH) to estimate the volatility persistence on crude oil, US, Gulf Corporation Council (GCC), Brazil, Russia, India and China (BRIC) stock markets. Also, the paper investigates the volatility spillover and the dynamic conditional correlation between crude oil, US stock index and stock indices of GCC and BRIC countries. The results prove a high degree of volatility persistence in the crude oil and stock markets. Based on the BEKK-GARCH and DCC-GARCH results, the paper finds strong evidence of the contagion effect of the oil shock and US financial crisis of 2008 on GCC and BRIC stock markets.

In the beginning, the paper uses univariate GARCH models to estimate the volatility persistence of the oil market, US stock market, and GCC and BRIC stock markets. Then, the paper uses a trivariate BEKK-GARCH model of Malik and Hammoudeh to examine the volatility spillover between oil market, US stock market and stock markets for GCC and BRIC countries. Finally, the paper analyses the dynamic conditional correlation between US market and each stock market of GCC and BRIC countries using the DCC-GARCH model. Also, the paper estimates the dynamic conditional correlation between oil market and all stock markets.

The results indicate the contagion effect of the oil shock and US financial crisis of 2008 on the GCC stock markets which are among the most important oil-exporting countries and also on BRIC stock markets which are among the emergent countries which are characterized by high economic growth level.

The contribution of this paper is to investigate the existence of contagion effect between oil market, US stock market and two panels of emerging stock markets which have different economic characteristics, the GCC and BRIC countries, during the oil shock and US financial crisis period of 2008-2009.