This paper examines return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional financial assets across various market conditions using a Quantile-VAR connectedness technique. It also explores the predictive powers of major global macroeconomic and geopolitical indicators on both connectedness across these market conditions. First, we find that return and volatility connectedness vary across market conditions, with higher levels during extreme events. Except during bullish periods, return connectedness dominates volatility connectedness. Second, NFTs are decoupled from both (un)conventional assets during normal market condition but it is a net return shocks receiver except under bullish market period, where it is a net transmitter. However, it is a net volatility shocks receiver irrespective of the market situation. Lastly, geopolitical risks, business condition and economic policy uncertainty are important predictors of return and volatility connectedness, although the strength and direction are heterogeneous. We discuss the policy implications of these findings.

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This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

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This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.@en

The conventional portfolio design approach assumes Gaussian return distributions, but this is not accurate in practice. Asymmetric and heavy-tailed return distributions necessitate consideration of higher-order moments such as skewness and kurtosis, in addition to mean and variance. This study proposes a multi-objective approach using a mean-variance-skewness-kurtosis model to construct a diversified portfolio. A parametrized polynomial goal programming (PGP) method is used to optimize the portfolio by maximizing returns and skewness while minimizing variance and kurtosis. Empirical data from the S &P ESG index family is used, and PGP generates multiple portfolios reflecting investors’ preferences for the four moments. To compare between the obtained portfolios, we represent the empirical cumulative distribution of the portfolio returns for all studied values of weights and show how this can be used to assist the inverstor in selecting the best set of weights.@en

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.@en

Productive efficiency has far-reaching implications on the direction of economic growth and welfare. While this has led to an expansive literature on the drivers of productive efficiency, this literature has proceeded without considering the role of energy poverty. Yet, energy poverty affects productive efficiency on several fronts. This paper fills this knowledge gap, u. sing a sample comprising 100 developing countries for the period 2000–2017. W e found robust evidence suggesting that energy poverty negatively affects productive efficiency—i.e., energy-poor countries become productively inefficient. Further analysis in the paper revealed that this negative effect persists largely across regions and is not sensitive to cross-country differences in income level. We also found that the negative effect of energy poverty on productive efficiency becomes more pronounced at a higher level of productive efficiency. We discuss the policy implications of our findings.@en

This paper uses the Quantile Vector-Autoregressive (Q-VAR) technique to examine the connectedness between three regional (North America, Europe and Asia-Pacific) sustainability indices and major natural resource commodities including energy commodities (crude oil and natural gas), precious metals (gold, silver, and platinum), and industrial metals (steel, aluminium, and copper). It also uses a linear regression model to investigate the macroeconomic and geopolitical factors that drive the connectedness among these investments. The QVAR results reveal asymmetric connectedness among these investment indices, with the levels of total connectedness during extreme downside and upside market conditions being significantly stronger than the level of connectedness during normal market condition. The results also show that, on average, the amount of shocks the regional sustainable investment indices each received from the studied energy and metal commodity markets are higher (lower) than what they transmit to the commodity market during the extreme upside (downside) market condition. During the normal market condition, however, only the Asian Pacific sustainable investment receives more shock than it transmits to the studied commodity market indices, making a net shock receiver. Finally, geopolitical risks, business environment conditions, gold and fixed income markets and economic policy uncertainty are important predictors of return connectedness, although the predictive strength and direction vary across market conditions. We discuss the implications of our findings.@en

This paper examines the dependence between Artificial Intelligence (AI) and eight energy-focused sectors (including renewable energy and coal) across different market conditions and investment horizons. This paper adopts both linear and non-linear models such as quantile regressions and quantile cross-spectral coherency models. Evidence from the linear model suggests that the performance of energy-focused corporations, especially those in the renewable energy sector depends strongly on the performance of AI. Results from the non-linear model indicate that dependence varies across both energy sectors, market conditions as well as investment horizons. By considering both negative and positive shocks on AI, we demonstrate that the dependence of energy corporations on AI also varies according to the direction of shocks on AI. Interestingly, negative and positive shocks on AI impact differently on the performance of energy corporations across different sectors and market conditions. Besides, we found that the dependence became stronger during the first wave of the COVID-19 pandemic. Our findings hold profound implications for portfolio managers and investors, who may be interested in holding the assets of AI and those of energy corporations.@en

We examine the dependence between volume and returns for the NFT market and three sub-markets (Cryptokitties, Cryptopunks, and Decentraland) using both quantile cross-spectral coherency and quantile regression techniques. Results from both techniques show significant evidence of dependence between NFT return and volume. Dependence between volume and return is weakest in the Cryptopunks market. Similarly, quantile regression results show that during extreme market conditions, equity and gold markets uncertainty, business condition and term-spread are important predictors of Cryptokitties returns, while oil, equity and gold markets uncertainty and geopolitical risks significantly predict Cryptopunks and Decentraland markets returns. In all cases, increase in Bitcoin prices reduces NFT market returns.@en

This paper examines the connectedness among 12 African equity markets and the global commodity, developed equity markets, paying particular attention to their evolution during the COVID-19 pandemic's peak period. We find that whilst African equity markets connect weakly to these markets, the levels of connectedness among these markets improved significantly during the pandemic. In addition, the energy market dominates the transmission of shocks in the system with commodity markets. Regarding the system with equity markets, the French and South African equity markets transmit the highest spillover in the full sample and during the pandemic's peak period, respectively.@en

This paper examines the dynamic short- and long-run asymmetric interactions and causality between real economic activity and stock and gold markets volatility shocks using both the cointegration Nonlinear Autoregressive Distributed Lag and Granger causality tests. In a further analysis, we used both the original and the partial sums decomposition of these variables to examine the level of market integration under different market conditions using the spillover index of Diebold and Yilmaz (2009; 2012; 2014). Our results indicate asymmetries in the short- and long-term relationships among these variables. In the long run, both positive and negative shocks from the energy market increase stock market volatility. However, only positive shocks on the gold market increase stock market volatility, while positive (negative) shocks on economic activity reduce (increase) stock market volatility. Also, an increase in both stock and energy markets volatility shocks are detrimental to real economic activity. We find a feedback effect between real economic activity shocks and these market volatility indexes, except for the gold market which has a unidirectional causality with the real economic activity shocks. Finally, the spillover analysis suggests a stronger integration among the partial sums, with the energy market as the dominant net-transmitter of both positive and negative shocks while the gold market is a net-receiver of shocks. Our results hold crucial implications for both investors and policymakers.@en