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A wide range of statistical and econometric models have been applied in the extant literature to compute and assess the volatility spillovers among renewable stock prices. This research adds to the body of knowledge by analyzing the dynamic asymmetric volatility spillover between major NASDAQ OMX Green Economy Indices, including solar, wind, geothermal, fuel cell, and developer/operator. The novelty of the research is that it distinguishes between positive and negative volatility spillovers in a time-varying fashion and conducts a connectedness network analysis. To do so, the study implements the Time-Varying Parameter Vector Autoregression (TVP-VAR) approach, as well as the connectedness network. The empirical investigation is based on high-frequency data between 18 October 2010, and 2 April 2022. The main findings may be summarized as follows. First, the analysis reveals a shift in the dominance of positive and negative volatility transmission during the study period, which represents compelling evidence of dynamic asymmetric spillover in the volatility transmission between renewable energy stocks. Second, the connectedness analysis indicates that the operator/developer and solar sectors are the net transmitters of both positive and negative volatility to the system. In contrast, the wind, geothermal and fuel cell sectors receive shocks from other renewable energy stocks. The asymmetric spillovers between the renewable energy stocks are confirmed using the block bootstrapping technique. Finally, the dynamic analysis reveals a substantial impact of the COVID-19 outbreak on the interdependence between renewable energy stocks. The findings above are robust to different lag orders and prediction ranges. [ABSTRACT FROM AUTHOR]

The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into good and bad volatility emphasizes an asymmetric pattern, which becomes more pronounced during periods of major events. On average, the oil market and the Asian renewable energy stock market are net receivers of volatility shocks. In contrast, the US and European renewable energy stock markets are net transmitters of shocks. Our findings provide investors with valuable insights for portfolio design and risk management decisions. [ABSTRACT FROM AUTHOR]

The past few decades have been marked by a gradual but steady increase in the reliance on renewable energy. In this study, we examined whether the prices of fossil fuels, namely, oil, coal, and natural gas, have affected renewable energy consumption in China during the period 1980–2018. To this end, we employed the novel dynamic Autoregressive Distributed Lag simulations approach. In the light of the empirical investigation, some intriguing conclusions have been drawn. We found strong evidence of the cointegrating relationship between the prices of all fossil fuels and renewable energy consumption. Furthermore, rising oil, coal, and natural gas prices resulted in increased renewable energy consumption in the long run, confirming that renewable energy sources can substitute fossil fuel energy only in the long run. Nevertheless, there is no evidence of significant effects in the short run. When considering the presence of structural breaks, the findings confirm the robustness of the dynamic ARDL simulations, as we conclude that fossil fuel prices positively affect renewable energy consumption only in the long run. [ABSTRACT FROM AUTHOR]

The current study investigates the causal relationship between economic growth and renewable energy consumption in the BRICS countries over the period 1971–2010 within a multivariate framework. The ARDL bounds testing approach to cointegration and vector error correction model (VECM) are used to examine the long-run and causal relationships between economic growth, renewable energy consumption, trade openness and carbon dioxide emissions. Empirical evidence shows that, based on the ARDL estimates, there exist long-run equilibrium relationships among the competing variables. Regarding the VECM results, bi-directional Granger causality exists between economic growth and renewable energy consumption, suggesting the feedback hypothesis, which can explain the role of renewable energy in stimulating economic growth in BRICS countries. [ABSTRACT FROM AUTHOR]