Static and dynamic connectedness between NFTs,Defi and other assets: Portfolio implication

The paper examines the return and volatility transmission between NFTs, Defi assets, and other assets (oil, gold, Bitcoin, and S&P 500) using the TVP-VAR framework. The results report weak static return and volatility spillovers between NFTs and Defi assets and selected markets, showing that these new digital assets are still relatively decoupled from traditional asset classes. Bitcoin, oil, and half of the NFTs and Defi assets are net transmitters of return and volatility spillovers, whereas rest of the markets are net recipients of spillovers. Our findings show that the dynamic return and volatility connectedness become higher during the initial phase of the COVID-19 pandemic and the cryptocurrency bubble of 2021. We also compute the static and dynamic optimal weights, hedge ratios, and hedging effectiveness for the portfolios of NFTs/other asset and Defi asset/other asset and show that investors and portfolio managers should consider adding NFTs and Defi assets in their portfolios of gold, oil, and stock markets to achieve diversification benefits.     

Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties

This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude Oil, Gold, Stocks, Bonds, and the United States Dollar-USD), and major global uncertainty measures (the Economic Policy Uncertainty-EPU, the Twitter-based Economic Uncertainty-TEU, and the Volatility Index-VIX) from April 29, 2013, to June 30, 2020. To this end, the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, dynamic connectedness approaches, and network analyses are used. The results indicate that total spillover indices reached unprecedented levels during COVID-19 and have remained high since then. The evidence also confirms the high return and volatility spillovers across markets during the COVID-19 era. Regarding the return spillovers, Gold is the centre of the system and demonstrates the safe heaven properties. Bitcoin is a net transmitter of volatility spillovers to other markets, particularly during the COVID-19 period. Furthermore, the causality-in-variance Lagrange Multiplier (LM) and the Fourier LM tests' results confirm a unidirectional volatility transmission from Bitcoin to Gold, Stocks, Bonds, the VIX and Crude Oil. Interestingly the EPU is the only global factor that causes higher volatility in Bitcoin. Several potential implications of the results are also discussed.     

Directional spillover effects and time-frequency nexus between oil,gold and stock markets: Evidence from pre and during COVID-19 outbreak

The Covid-19 crisis has been spread rapidly throughout the world so far. However, how deep and long the turbulence would depend on the success of solutions taken to deter the spread of Covid-19, the impacts of government policies may be prominent to alleviate the current crisis. In this article, we investigate the spillover effects and time-frequency connectedness between S&P 500, crude oil prices, and gold asset using both the spillover index of Diebold and Yilmaz (2012) and the wavelet coherence to evaluate whether the time-varying dynamic return spillover index exhibited the intensity and direction of transmission during the Covid-19 outbreak. Overall, the present results shed light on that in comparison with the pre-Covid-19 period, and the return transmissions are more apparent during the Covid-19 crisis. More importantly, there exist significant dependent patterns about the information spillovers among the crude oil, S&P 500, and gold markets might provide significant implications for portfolio managers, investors, and government agencies.     

The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model

This study employs a non-linear framework to investigate the impacts of central bank digital currency (CBDC) news on the financial and cryptocurrency markets. The time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005) is estimated based on weekly data from the first week of January 2015 to the last week of December 2021. The vector of endogenous variables in the VAR estimation contains the Central Bank Digital Currency uncertainty index (CBDCU), cryptocurrency policy uncertainty index, S&P 500 index, VIX, and Bitcoin price. The TVP-VAR model’s time-varying responses demonstrated that the reactions of the cryptocurrency market to central bank digital currency announcements vary remarkably over time. The impacts of the CBDC shocks on the financial market have been increasingly visible during the COVID-19 pandemic. According to the time-varying forecast error decompositions, CBDCU and VIX shocks have accounted for most of the variance in cryptocurrency uncertainty and Bitcoin return shocks, notably during the COVID-19 period.     

Investigating volatility spillover of energy commodities in the context of the Chinese and European stock markets

This paper investigates spillover from energy commodities to Shanghai stock exchange and European Stock market, and identifies possible risks transmission and portfolio diversification opportunities. The study is conducted on daily spot prices of carbon (CO2) emission, natural gas and crude oil from 16 December 2010 to 29 December 2022, employing Granger causality test, dynamic conditional correlation (DCC), Diebold-Yilmaz (2012) and Barunik-Krehlic (2017) models. Results identify higher volatility and imply greater connectedness in the longer run. Additionally, natural gas is witnessed as the highest contributor of the shocks and crude oil as the highest receiver of the shocks from the network connection. Further results suggest for investment in energy commodities in shorter run rather than long run for efficient portfolio diversification. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, given the suggestion of this study to incorporate energy stocks for efficient diversification of risk.     

Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index

We examine the quantile connectedness of returns between the recently developed S&P 500 Twitter Sentiment Index and various asset classes. Rather than a mean-based connectedness measure, we apply quantile-connectedness to explore connectedness of means and, especially, extreme left and right tails of distributions. Using mean-based connectedness measures, the level of return connectedness between the twitter sentiment index and all financial markets is a modest 46%. However, when applying a novel quantile-based connectedness approach, we find that levels of tail-connectedness are much stronger, up to 82%, at extreme upper and lower tails. This suggests that the impact of sentiment on financial markets is much stronger during extreme positive/negative sentiment shocks. Moreover, return connectedness measures are less volatile during extreme events. Net connectedness analysis shows that the Twitter sentiment index acts as a net transmitter of return spillovers, highlighting the leading role of investor sentiment on predicting other financial markets.     

Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

This paper investigates the higher-order moment risk connectedness between West Texas Intermediate (WTI) oil futures, Brent oil futures, Chinese oil futures and commodity futures (agricultural, industrial metals, and precious metals) before and during the COVID-19 pandemic and following the outbreak of the Russia-Ukraine conflict, by combining ex-post moment measures and the novel time-varying parameter (TVP)-vector auto-regression (VAR)-based connectedness approach. Further, this paper depicts the dynamic overall and pairwise correlations between oil and commodity futures and constructs the hedging and optimal-weighted portfolio strategies using the DCC-GARCH t-Copula model. This paper also constructs the multivariate oil-commodity portfolio based on the newly proposed minimum connectedness portfolio approach and takes into account the higher-order moment risk connectedness. The empirical results demonstrate that the dynamic linkages between international oil and commodity futures are positive, time-varying, and have been greatly intensified by the outbreak of the 2018 China-US trade war, the 2020 COVID-19 pandemic, and the 2022 Russia-Ukraine conflict. The risk connectedness results are moment-dependent. The averaged total skewness and kurtosis spillovers are lower than the return and volatility connectedness. Brent (WTI) oil is the largest net transmitter of the return and volatility (skewness and kurtosis) risk spillovers. The dynamic total, net, and net-pairwise spillovers are all time-varying and highly reactive to major crises, especially the COVID-19 pandemic and the Russia-Ukraine conflict. Furthermore, the optimal-weighted portfolio shows a higher risk reduction than the hedging strategy. Finally, the minimum skewness connectedness portfolio shows relatively higher hedging effectiveness, while the minimum kurtosis connectedness portfolio offers the highest cumulative returns.     

Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors

Integration between international energy prices and stock market returns is critical for global economics and politics. In this study, we employ a TVP-VAR (time-varying parameter vector autoregression) connectedness decomposition approach to investigate the time-varying linkages between a diversified energy portfolio comprising oil, coal, natural gas, and stock returns in G7 countries and China. This approach allows us to show the dynamic spillovers and explore the driving factors underlying the dynamic patterns. We find that geopolitical risks, global economic policy uncertainties, and equity market volatility can influence cross-market spillovers. This study expounds the effect of energy financialization.     

Return connectedness across asset classes around the COVID-19 outbreak

In this paper, we show evidence of a dramatic change in the structure and time-varying patterns of return connectedness across various assets (gold, crude oil, world equities, currencies, and bonds) around the COVID-19 outbreak. Using the TVP-VAR connectedness approach, the results show that the dynamic total connectedness across the five assets was moderate and quite stable until early 2020. After that, the total connectedness spikes and the structure of the network of connectedness alters, which concurs with the COVID-19 outbreak. The equity and USD indices are the primary transmitters of shocks before the outbreak, whereas the bond index becomes the main transmitters of shocks during the COVID-19 outbreak. However, the USD index is a net receiver of shocks to other assets during the outbreak period. Furthermore, using a recently developed newspaper-based index of uncertainty in financial markets due to infectious diseases to capture the recent impact of COVID-19, we find that connectedness is positively related to this index, and increases at higher levels (conditional quantiles) of connectedness. Overall, our results reflect the speedy disturbing effects of the COVID-19 outbreak, which matters to the formulations of policies seeking to achieve financial stability. The results also indicate a possibility to threaten investors’ portfolios and fade the benefits of diversification.     

Multilayer information spillover networks between oil shocks and banking sectors: Evidence from oil-rich countries

There is no doubt that oil price shocks significantly affect oil-producing countries' macroeconomic fundamentals and financial stability, mainly in crisis times. The recent oil price shocks, coupled with the COVID-19 pandemic, motivated us to investigate the connectedness and risk transmission among oil shocks and banking sectors in the Gulf Cooperation Council (GCC) economies from June 30, 2006, to September 9, 2021. Thus, we construct multilayer information spillover networks between oil price shocks and GCC banking sectors. The empirical results show that the Bahrain banking sector depicts the highest connectedness and risk transmission with oil price shocks on the extreme risk spillover layer. In addition, Kuwait and the United Arab Emirates are highly connected to oil demand shocks. Furthermore, we find a substantial increase in extreme risk spillover and volatility spillover layers during the COVID-19 period. The results of this paper have some important implications for regional portfolio risk management, alleviating systemic risk, and developing hedging and investment strategies.     

What do we know about the price spillover between green bonds and Islamic stocks and stock market indices?

This paper investigates the risk transmission, linkages, and directional predictability between green bonds, Islamic stocks, and other asset classes. Using daily data from November 2008 to August 2020, we use the Standard & Poor's (S&P) Green Bond Index to represent the green bond market and the Dow Jones Islamic World Index and the S&P Global Shariah Indices to represent Islamic stocks. The other asset classes considered include the S&P 500 Stock Composite, S&P 500 Bond, and S&P 500 Energy indices. This paper uses the novel quantile cross-spectral (coherency), the windowed scalogram difference (WSD), and the cross-quantilogram (CQ) correlation approaches. The results from the quantile coherency analysis reveal a negative spillover effect from green bond price returns to Islamic stocks in the long run, which indicates that the green bond market poses a long-run systemic risk to Islamic stocks. From the WSD analysis, the results show that the integration between green bonds and Islamic stocks, the S&P 500 Stock Composite, and the S&P 500 Bond index is weaker during volatile market conditions. The CQ correlation suggests that the dependency between green bonds and other asset returns is concentrated in the lower quantiles and that this dependency is weaker at longer lags. Our results underscore the significance of green bonds in investor portfolios as a new investment asset class.     

Time-Varying Spillover and the Portfolio Diversification Implications of Clean Energy Equity with Commodities and Financial Assets

This article examines the time-varying spillover and its implications on hedging and portfolio diversification for clean energy equities (WilderHill New Energy Global Innovation Index (NEX)) with technology stocks (PSE), four energy sub-indices of Standard & Poor Goldman Sachs Commodity Index (S&P-GSCI) viz., Crude oil, Brent crude oil, Gasoline and Heating oil and three major global equities indices represented by the USA, Europe, World, Dow-Jones Islamic Market Index (DJIMI) along with USD-Euro exchange rate. We find that in a mixed portfolio set-up, the inclusion of NEX in energy portfolio provides better diversification and risk reduction benefits for hedgers and portfolio managers.     

Foreign exchange market return spillovers and connectedness among African countries

This paper examines the foreign exchange return shock spillovers and network connectedness among African countries during crisis periods using (Diebold & Yilmaz, 2012; 2014; 2016) which is based on generalized VAR and network theory between June 2004 and June 2021. Overall, the study found a low system-wide spillover connectedness among African foreign exchange markets. However, the total systemic spillover index increased during the eurozone sovereign debt crisis followed by global financial crisis, indicating evidence of contagion effects. This offers good diversification opportunities in the African currency market during crisis periods. The study also found no significant evidence of spillover effects among African currencies. Nonetheless, the network connectedness analysis found a positive significant pairwise return spillovers from the South African rand, Moroccan dirham and the CFA francs to Botswana pula, and from Moroccan dirham to CFA francs and South African rand. Furthermore, the study found South African rand, Moroccan dirham and CFA francs as the most significant net-transmitter of return shocks to other currencies whiles the Kenyan shilling and Botswana pula are the net-receivers of return shocks from other currencies. These results have implications for African central banks interventions in stabilizing their exchange rates to facilitate intra and inter-African trade and for international portfolio investors in managing their foreign exchange risk exposures.     

Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis

The response of renewable energy stock returns to the dynamics of fossil energy markets is a vital concern of low-carbon transitions. There is still sparse literature documenting the directional dependence of renewable energy stock returns on the connectedness among fossil energy returns, even though previous studies have examined the relationship among renewable energy stocks and fossil energy markets. Additionally, the conclusions of prior studies are quite far from reaching a consensus regarding the relationship between the renewable energy stock and the fossil energy markets. To this end, by using the TVP-VAR based connectedness approach and Cross-Quantilogram techniques, this study does the first attempt to unpack the complicated and controversial directional dependence of renewable energy stock returns on the returns and connectedness of fossil energy markets, considering various market conditions and time horizons. The empirical analysis demonstrates that, first, the directional dependence of renewable energy stock returns on fossil energy returns is pronounced during extreme market conditions, whereas they appear to be decoupled from fossil energy returns during normal market conditions. Second, the total connectedness between fossil energy returns transmits a substantial shock to renewable energy stock returns during most market conditions, which is in stark contrast to the information transmission directly originating from fossil energy markets. The performance of renewable energy stock markets improves with stronger fossil energy return connectedness, whereas weaker fossil energy return connectedness hinders it. Additionally, further study reveals that the directional dependence of renewable energy stock returns on the net connectedness of the crude oil market is dominated by negative dependence when the net connectedness of the crude oil market is low, whereas it displays positive dependence when the net connectedness of the crude oil market is high. This directional dependence pattern on the net connectedness of the crude oil market is opposite to that exhibited in the net connectedness of the coal and natural gas markets. Third, in general, the directional dependence of renewable energy stock returns on fossil energy returns is more pronounced in the short term but diminishes over the medium and long terms. Conversely, the directional dependence of renewable energy stock returns on fossil energy return connectedness persists over the medium and long terms. Final, with the outbreak of the Global Financial Crisis during 2007–2008, we notice an abrupt jump in the directional dependence of renewable energy stock returns on fossil energy returns and their connectedness, particularly during extreme market conditions. Our findings provide noteworthy implications for energy transformation, energy security, and climate mitigation.     

Volatility spillovers across NFTs news attention and financial markets

The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public’s attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories from the LexisNexis News & Business database and applies the historical decomposition to assess the historical variations of the NFTsAI. Then the empirical analysis is performed via a TVP-VAR volatility spillover connectedness model. The empirical results show that NFTsAI indicates NFT markets are dominated by cryptocurrency, DeFi, equity, bond, commodity, F.X. and gold markets. And NFT markets are volatility spillover receivers. In addition, NFT assets could impede financial contagion and have significant diversification benefits. Employing a panel pooled OLS regression model as a supplementary analysis and a GARCH-MIDAS model as a robustness test. This study reveals that NFTsAI has sufficient power to explain the return of NFT assets from a fixed effect perspective, and NFTsAI contains useful forecasting information for both short and long-term volatility of NFT markets, separately. The new NFTsAI and the empirical findings contain useful insights for risk-averse investors, portfolio managers, institutional investors, academics and financial policy regulators.     

The impact of deposit insurance on S&L shareholders' risk/return trade-offs

This article empirically examines how savings and loan associations' (S&Ls') stock returns respond to asset mix changes. When deposit insurance is underpriced, increases in financial leverage and the riskiness of the asset portfolio should lead to increases in expected return on common stock. In particular, changes in asset components which increase the volatility of an institution's portfolio should lead the stock market to upwardly revalue S&L equity. This hypothesis is examined using data for the July 1984–December 1989 period. Increases in commercial mortgage loans, acquisition and development loans, and investments in service corporations appear to cause higher return for shareholders of poorly capitalized, failing S&Ls. Similar increases appear to have little impact on the common stock returns of well-capitalized S&Ls.     

Quantile connectedness between energy,metal, and carbon markets

This study employs a quantile connectedness approach to examine the dynamic linkages and tail risk connectedness between energy, metal, and carbon markets. Results show that the connectedness between energy, metal, and carbon markets is about 51% at the mean or median and 87% under extreme conditions. This means that the spillover effects of the two tails are much stronger than those under the conditional mean and normal markets, and the spillover effect between markets is heterogeneous under different market conditions. The connectedness between energy, metal, and carbon markets is time-varying, and the volatility is relatively small under extreme positive and negative conditions. Notably, the dynamic connectedness of energy, metal, and carbon markets is different in extreme upward and downward markets, which reflects the asymmetry and tail dependence of spillover effects between markets and indicates that spillover effects are different between the periods of upward and downward markets. In addition, the results of portfolio strategy show that holding short positions in the carbon market is an effective investment choice.     

Dependence and risk spillovers among clean cryptocurrencies prices and media environmental attention

This paper examines the relationships among cryptocurrency environmental attention and clean cryptocurrencies prices using Time-Varying Parameter Vector Auto-Regression (TVP-VAR) and wavelets techniques. Results show strong connectedness among these variables, implying that the prices of clean cryptocurrencies are influenced by attention on cryptocurrency sustainability. Connectedness is stronger with positive shocks on environmental attention than negative shocks. Also, in the short-term, clean cryptocurrencies prices lead environmental attention, especially after 2021. However, there are notable periods when environmental attention led clean cryptocurrency prices before 2021. In the long-term, clean cryptocurrencies such as Hedera, Polygon, Cosmos, IOTA, TRON, Stellar, Tezos and Ripple lead environmental attention. In the presence of bitcoin, the degrees of connectedness increased across both shocks on cryptocurrency environmental attention. In all cases, the bitcoin market is the main destination of shocks from the system. We highlight some crucial implications of these results.     

Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities

The main focus of this research is to investigate the potential spillover effects between AI-based stocks and tokens by using the quantile connectedness approach developed by Ando et al. (2022). The study aims to investigate both static and dynamic spillovers at the lower and upper tails of the return distribution. AI-based stocks and tokens may have relatively low levels of connectedness, which also varies over time and increases during periods of economic turbulence. In addition, in line with previous work analysing other financial markets and assets, this research finds that the system is more sensitive to the tails of the distribution (i.e., the lower and upper quantiles) than to the median (Q = 0.50). This finding is consistent with expectations, and measures of dynamic connectedness change over time, with the intensity of spillovers increasing at the extremes of the distribution. These results have practical implications for portfolio managers, as they can use the results to adjust their investment portfolios according to the evolution of the dynamic spillovers observed in the system. Overall, this study sheds light on the potential tail spillovers in the AI-based stock and token market and provides valuable insights for investment decisions.     

Dynamic connectedness and optimal hedging strategy among commodities and financial indices

This study looks at the best portfolio strategy for mitigating the risk associated with the MSCI ACWI & Frontier Markets Index, as well as the volatility spillovers between commodity markets and certain financial markets. Therefore, we empirically explore the connectedness among three financial indicators and five product groups using the framework of Diebold and Yilmaz (2012), which is based on a vector autoregressive process and variance decomposition of prediction errors, between 31 May 2002 and 30 July 2021. We also investigate the best hedging instrument(s) for the MSCI ACWI & Frontier Markets Global Index by combining the Asymmetric Dynamic Conditional Correlation (ADCC) model with the risk reduction index and the hedging ratios. Our empirical findings highlight the importance of volatility spillover effects across financial markets, which is not the case for commodity markets with low volatility externalities. Furthermore, the first markets appear to be net transmitters of volatility, whereas the second markets appear to be net receivers. Using the approach of Kroner and Sultan (1993), we show that the least risk portfolio is a portfolio that combines the MSCI ACWI & Frontier Markets Global Index with financial indices related to socially responsible and irresponsible investing.