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

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 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 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