Market Connectedness Research Articles

Quantile-frequency connectedness among climate change stocks: The roles of climate change attention and global uncertainties

Concerns regarding climate change and the environment have heightened interest in climate-conscious investing, necessitating a deeper understanding of climate-friendly financial products. This study investigates climate change stock market connectedness and spillover effects, focusing on quantile-frequency dependency. The study utilizes a novel quantile-frequency connectedness with seven MSCI climate change market indices and daily data from January 2014 to December 2022. The empirical analysis reveals that climate change stocks are highly interconnected across various market conditions, including bearish and bullish periods. Spillover effects have amplified during increased stock market turbulence, regardless of market directions. Short-term spillover effects have a stronger impact than long-term effects, regardless of market conditions. Climate change stock markets in Japan and the Asia Pacific consistently experience net shocks, while the United States, Europe, Emerging Markets (EM), North America, and the European Economic and Monetary Union (EMU) exhibit variations based on market conditions and timeframes. The study identifies events including the 2015 Paris Agreement, the 2016 US withdrawal from the Paris Agreement, the COVID-19 pandemic, the 2021 US rejoining of the Paris Agreement, and the 2022 Russian-Ukraine War that have influenced the dynamic connectedness and spillover of climate change markets. The analysis also reveals that increased investor attention to climate change leads to higher interconnectedness among climate change stock markets across various market scenarios. Moreover, global financial stress, economic policy uncertainty, geopolitical risk, global carbon emission futures, oil market uncertainty, and stock market uncertainty significantly impact the interdependence of climate change markets. The findings have implications for portfolio managers, emphasizing the need for hedging techniques to mitigate risks in climate change markets.

Bitcoin market connectedness across political uncertainty

In this study, we examine the risk transmissions between the bitcoin market, political uncertainty, and various financial market uncertainty measures. We find that the returns and volatilities of Bitcoin are the receivers of the information from political uncertainty measures, such as the geopolitical risk index and the party conflict indices. These effects are strong during periods of financial stress. Further, the financial market uncertainty measures switch between the roles of transmitters and receivers over time. Our results further show that Bitcoin has hedging and safe-haven properties that mitigate political uncertainty. Our results provide a full understanding of how political uncertainty is transmitted to the Bitcoin market.

African and international financial markets interdependencies: Does Covid-19 media coverage make any difference?

This study examines the co-movement and time-varying integration between equity, exchange rate, and international market volatility indices across different time–frequency domains using the wavelet and dynamic conditional correlation-generalised autoregressive conditional heteroskedasticity (DCC-GARCH) model and BEKK GARCH for selected African countries. First, the findings indicate that the co-movement between equity and exchange rates during the pandemic was exacerbated by global COVID-19 media coverage. The findings indicate that there has been a substantial risk transfer between exchange rates and stock returns during the COVID-19 pandemic, resulting in a decline in domestic stock returns and subsequent capital outflows, which in turn increased the exchange rate. Given the growing difficulties in diversification, specific information on the volatility of financial market connectedness is required to plan hedging strategies. To explore the influence of global market volatility on Africa’s equity and currency markets, it is crucial to analyse the relationship between regional and global market fluctuations, especially given the negative impact of the COVID-19 pandemic. Our empirical research demonstrates that the VIX and OVXCL indices play a significant role in transmitting spillovers to currency and equity markets in Africa. This suggests that the sentiment indicators provided by the VIX and OVXCL can be useful in predicting the behaviour of Africa’s currency and equity markets

MEASURING CONNECTEDNESS AND NETWORK ANALYSIS IN STOCK MARKETS FOR DEVELOPED AND DEVELOPING COUNTRIES

Countries are experiencing a surge in political, economic, and financial integration, consequently shaping international market linkages. Financial crises can rapidly spread between countries, emphasizing the need to monitor and assess stock market connections. This paper investigates the degree of financial market connectedness using daily stock returns from January 1997 to August 2017 for 13 countries, both developed and developing. The connectedness measure of Diebold and Yilmaz (2009-2012) is applied to examine the connectedness of stock market returns and the direction of spillovers for all stock markets. This study also analyzes the dynamic connectedness from the U.S. stock market to all other stock markets. The results indicate that the U.S. stock market is the most influential stock market to the others. The results of the dynamic analysis show that connectedness changes over time, specifically during turmoil periods. Most developed countries are transmitters of return spillover shocks while developing countries are recipients.

The role of uncertainty and sentiment for intraday volatility connectedness between oil and financial markets

We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 min quotations for most heavily traded financial assets: crude oil, the US dollar, S&P 500 index, gold and US treasury bonds. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents a different picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 min frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market-specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand financial and commodity markets dynamics.

Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R[formula omitted] decomposed connectedness measures

In this study, we investigate the return propagation mechanism across four clean energy indices, namely, the NASDAQ OMX Green Economy Index, NASDAQ OMX Solar Energy Index, NASDAQ OMX Wind Energy Index, and NASDAQ OMX Geothermal Energy Index ranging from December 21st, 2010 until June 2nd, 2023 by using a novel DCC-GARCH-based R2 decomposed connectedness approach. This framework allows us to efficiently decompose dynamic conditional R2 goodness-of-fit measures into its decomposed components. Furthermore, we introduce the concept of minimum R2 decomposed connectedness portfolios and multivariate hedging portfolios. We find that the dynamic total connectedness is heterogeneous over time and economic-event dependent. In addition, the empirical results highlight that the NASDAQ OMX Green Economy Index is a net transmitter of shocks while all others are net receivers of shocks. Finally, we find that our proposed portfolio technique outperforms the NASDAQ OMX Green Economy Index as well as all alternative multivariate portfolio techniques regarding the hedging effectiveness score and the Sharpe ratio.

Green bonds forecasting: evidence from pre-crisis, Covid-19 and Russian–Ukrainian crisis frameworks

PurposeWithout precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.Design/methodology/approachIn their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.FindingsAuthors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.Originality/valueThis study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

Asymmetric connectedness and investment strategies between commodities and Islamic banks: Evidence from gulf cooperative council (GCC) markets

The study uses the data of thirteen Islamic Banks (IB) in the GCC region and sixteen commodities that include soft agriculture, energy, industry, and precious metal commodities. Murabaha transactions makes up most of the revenues of the Islamic banks, whereas commodities set up the biggest portion of the assets among Murabaha transactions, therefore commodities and IB revenues are expected to comove. Interestingly, empirical findings suggest that most of the Islamic banks and commodities are not significantly affected by the shocks from other markets. We further observe that negative shocks have a higher impact on market connectedness among used assets compared to the positive returns and find a significant role of risk variables in explaining the magnitude of spillover between used assets. We perform robustness of our results in sub-samples periods during Shale Oil Revolution, Global Financial Crisis, and the COVID-19. Additionally, the multivariate portfolio analysis shows some risk reduction properties of the majority of the used assets. The performance evaluation measures demonstrate that weights selected based on dynamic connectedness network presents diversification opportunities. These findings help investors and portfolio managers to remain alert to the movements of the risk factors and calibrate there hedging and portfolio management strategies by taking long and short positions as incorporation of Islamic banks and commodities in the GCC region in a portfolio could yield risk reduction benefits and profitability.

The connectedness between three traditional assets and cryptocurrency during the COVID-19 pandemic and Russia–Ukraine conflicts

ABSTRACT This paper analyses the connectedness between three traditional financial assets and cryptocurrencies from June 2019 to December 2022. We find that cryptocurrencies have the highest within-market connectedness, suggesting they are less influenced by other asset categories. The dynamic overall market connectedness undergoes four structural changes during the sample period, with two significant increases aligning with the early stages of the COVID-19 pandemic and the Russia–Ukraine conflict. When comparing the two stages before and after these increases, we observed an increase in spillover effects from cryptocurrencies to the other three asset categories.

Japanese stock market sectoral dynamics: A time and frequency analysis

AbstractThis study examines the Japanese stock market connectedness across different sectors, focusing on both the time and frequency dimensions. The dataset used spans from January 1999 to April 2022 and employs various methodologies, including time‐varying parameter vector autoregressions, TVP‐VAR frequency dependency, and Quantile coherency. The empirical findings reveal that cyclical or aggressive stocks predominantly act as net transmitters of shocks across sectors. Moreover, short‐term spillovers are more significant compared to intermediate‐term spillovers, indicating that Japanese sectors are more pronounced to market shocks in the short run. The spillover effects are also asymmetric and vary over time. Additionally, the real estate sector exhibits diversification benefits across different time horizons, while the energy sector provides protection primarily in the short run. This research contributes to the development of financial policies aimed at reducing sectoral imbalances and promoting stable growth. Furthermore, it offers insights for investors seeking to devise optimal portfolio diversification strategies.

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.

Measuring the extreme linkages and time-frequency co-movements among artificial intelligence and clean energy indices

This is the first study analyzing the volatility connectedness and time-frequency interdependence between AI index and clean energy index. Specifically, we use the QVAR frequency connectedness, Wavelet Local Multiple Correlations (WLMC) and Granger causality quantile methods to check the risk spillovers and multivariate time and frequency relationships among the eight clean energy indexes and the AI index. This is over the period from December 18, 2017 to April 4, 2023. Our results show: (1) NASDAQ OMX Geothermal Index is the strongest net sender of short- and long-term shocks in the system during extreme upside market conditions. In downturn conditions, the S&P Global Clean Energy Index is the largest net shock sender. The AI Index exports shocks at all frequencies. In addition, market connectedness among markets is stronger under extreme market conditions. (2) We find that the AI Index predominantly exhibited positive co-movements with clean energy indices, primarily concentrated within the long-term frequency domain. However, they displayed robust cooperative dynamics across all frequency domains within the context of multivariate wavelet interconnections. (3) The quantile granger causality analysis revealed that below the extreme bullish threshold (0.95), the NASDAQ CTA Artificial Intelligence & Robotics index could predict changes in the risk associated with all clean energy indices. However, under extremely bullish quantile conditions, the NASDAQ CTA Artificial Intelligence & Robotics index statistically exhibited Granger causality only with respect to the NASDAQ OMX Renewable Energy Index, NASDAQ OMX Geothermal Index, and WilderHill Clean Energy Index.

International trade network and stock market connectedness: Evidence from eleven major economies

Depth of cross-country international trade engagement is an important source of (the strength of) stock-market connectedness, depicting how directional attributes of trade determine the magnitude of spillover of stock returns across economies. We premise and test this hypothesis for a group of eleven major economies during 2000 m1-2021 m6 using both system-wide and directional evidence. We exploit the input–output network of Bilgin and Yilmaz (2018) to construct a trade-network, and use Diebold and Yilmaz’s (2009, 2012, 2014) Connectedness Index to proxy for stock-market connectedness among economies. We reveal China’s instrumental role in the trade-network and its rising influence in stock markets dominated by the US. Motivated by the fact that shocks on an economy’s imports and exports may lead to different magnitude of stock market spillover to its trade partner, we further carry out a pairwise directional level investigation. Once the directional dimensions of both the trade flows and the stock market influences are considered, we find that an economy’s stock return spillover to its trade partner is generated from its position as an importer and exporter. More importantly, being an importer is found to be a stronger source of such spillover than being an exporter.

The historical transition of return transmission, volatility spillovers, and dynamic conditional correlations: A fresh perspective and new evidence from the US, UK, and Japanese stock markets

<abstract> <p>This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-<italic>t</italic> errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.</p> </abstract>

Clean Energy Market Connectedness and Investment Strategies: New Evidence from DCC-GARCH R2 Decomposed Connectedness Measures

Clean Energy Market Connectedness and Investment Strategies: New Evidence from DCC-GARCH R2 Decomposed Connectedness Measures

How do leading stock markets in America and Europe connect to Asian stock markets? Quantile dynamic connectedness

<p>We used the quantile vector autoregressive (QVAR) dynamic connectedness framework to examine whether leading stock markets in America and Europe would have any impact on major stock markets in Asia.<sup>1</sup> More precisely, we analyzed systematically the stock market connectedness in 15 countries, namely Germany, the UK, the USA, and 12 Asian countries, which include five major ASEAN countries, namely Indonesia, Malaysia, Philippines, Singapore, and Thailand from 1996 to 2023. The findings indicated that Hong Kong and Singaporean stocks were major transmitters of financial shocks at the extreme low price market condition, while Germany and UK were minor transmitters. By contrast, the USA could be considered the major transmitter of financial shock during the extreme high market price returns condition. In the normal market condition, these three countries in Europe and America are important transmitters of financial shock. More interestingly, the empirical findings indicated the centrality of Singapore in the stock market connectedness in Asia.</p><p><sup>1</sup> The authors are grateful to Professor David Gabauer who makes available the R codes for all calculations in this paper.</p>

Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks

This paper analyzes the connectedness between the financial markets in the U.S. and China based on non-linear Granger causality and quantile Granger causality networks. We extend the linear Granger causality network proposed by Billio et al. (2012) and support the importance of multi-class market interactions highlighted by Bollerslev et al. (2018). We find that non-linear Granger causality between stock, bond, and foreign exchange markets in the U.S. and China provides more interconnectedness than linear Granger causality. Both the strength and the direction of the connectedness are related to the quantile of market returns, as evidenced by the fact that the left (right) tail quantiles provide more causal effects than the middle quantiles and the mean. We characterize this phenomenon as the “smiling curve” of causality connectedness, and we show the “smiling curve” for each country and financial submarket. Compared to the financial markets in China, those in the U.S. appear to be more central to the connectedness network, with the main route being the stock market. When market returns are in the right tail quantile interval, the U.S. financial market has more capacity to receive information from various markets than China does. When market returns are in the left tail quantile interval and at the middle quantiles, the Chinese financial markets are more susceptible to shocks than those in the United States, with the Chinese stock and bond markets being more sensitive than the foreign exchange market.

The Impact of the Ukrainian Crisis and Global Shocks on Stock Market Connectedness in Sub-Saharan Africa: Evidence from Diebold and Yilmaz (2012) Spillover Analysis

The Impact of the Ukrainian Crisis and Global Shocks on Stock Market Connectedness in Sub-Saharan Africa: Evidence from Diebold and Yilmaz (2012) Spillover Analysis

How do uncertainties affect the connectedness of global financial markets? Changes during the Russia-Ukraine conflict

ABSTRACT We utilize the spectral decomposition of TVP-VAR connectedness to examine the dynamics of connectedness among six global financial markets. Additionally, we employ dynamic model averaging with retrospective analysis to ascertain the impact of uncertainties on the connectedness network. The findings reveal a shift from a declining trend in both total and directional connectedness to an ascending trend during the Russia-Ukraine conflict. The US dollar has the largest outward and inward spillovers in the short-term, but G7 MSCI and EFM MSCI are the largest outward and inward spillovers in the medium- and long-term, respectively. Among the five uncertainties under study, the financial stress index and VIX consistently hold significant influence throughout the sample period. Meanwhile, geopolitical risk and Twitter-based economic uncertainty demonstrate significance during the conflict period. Nonetheless, the impacts of these uncertainties diverge. The financial stress index and Twitter-based economic uncertainty exhibit positive effects, whereas VIX and geopolitical risk tend to weaken connectedness. Our findings underscore the need for investors to remain cautious of shifts in market connectedness patterns as they manage their assets.

Green Sukuk or Green Bonds: A Comparative Study of Diversification and Hedging Prospects

This study employs the wavelet coherence and the spillover index methodologies to compare time-varying relationship between green sukuk (GSI) and green bonds (SPGB) with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index and Bitcoin between October 2019, and December 2022. While the predominant tonality of the coherence of GSI and SPGB was weak, GSI and SPGB failed to offset spillover during the covid-19 pandemic. Overall, GSI exhibited superior diversification and hedging performance. The spillover index results unveiled the influence of bearish market conditions on the intensity of market connectedness, prompting temporal transmission of risks between markets which can last up to eight days following the emergence of a shock. GSI slightly outperformed SPGB in portfolio diversification amid low total market connectedness, less net spillover, and less net-pairwise spillover as revealed by the findings of the spillover index. A combination of GSI or SPGB with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index, and Bitcoin under the same investment portfolio is likely to offer long-term investors diversification and hedging opportunities. Keywords: green sukuk, green bond, co-movement, spillover, wavelet, risk management.