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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 identification of factors influencing the renewable energy stock market has recently received much attention from scholars. While the implications of economic policy uncertainty on renewable energy stock markets have been extensively studied in recent literature, less attention has been paid to climate policy uncertainty. This study adds to the existing body of knowledge by investigating whether the U.S. climate policy uncertainty predicts the return and volatility of five NASDAQ OMX Green Economy indices (fuel cell, wind, solar, developer/operator, and geothermal). The analysis is based on the nonparametric causality-in-quantile test and employs data from November 2010 to August 2022. The findings provide strong evidence of heterogeneous distributional predictability of climate policy uncertainty on the return and volatility of renewable energy stocks, which depends on the renewable energy sector and the market condition. More specifically, climate policy uncertainty does not cause stock returns during extreme downward and/or upward renewable energy market conditions. Additionally, uncertainty cannot predict volatility during the turmoil episodes that occurred in the renewable energy stock market. Further in-depth analysis considering the asymmetry reveals that a decrease (increase) in climate policy uncertainty improves the prediction of renewable energy stock returns (volatility) instead of a decrease (increase). These findings still hold when using a parametric causality-in-quantile test. The findings underscore the critical role of climate policy transparency and sector-specific strategies in reducing uncertainty and encouraging long-term investments in the renewable energy sector. • We study the (a)symmetric impact of US climate policy uncertainty on the return and volatility of renewable energy stocks. • The nonparametric causality-in-quantiles test is performed. • Evidence of heterogeneous distributional predictability of climate policy uncertainty to sectoral renewable energy stocks. • Climate policy uncertainty does not cause stock returns during extreme downward/upward swings. • A decrease (increase) in climate policy uncertainty improves the prediction of renewable energy stock returns (volatility). [ABSTRACT FROM AUTHOR]

Research background: The effects of oil price fluctuations on the macroeconomic performance in oil-importing and oil-exporting countries have stimulated considerable research activity. However, the debate is far from being closed. Purpose of the article: This paper revisits the impact of crude oil price on economic activity in the Gulf Cooperation Council oil-exporting countries. The study covers a relatively long period spanning from 1960 to 2018. Methods: The empirical investigation accounts for structural breaks, nonlinearity, and nonnormal distribution of data. The Kapetanios (2005) structural breaks unit root test and Saikkonen- Lütkepohl (2000a, b, c) cointegration test with structural shifts are implemented to examine the stationary properties of data and the presence of cointegration between variables, respectively. Moreover, the quantile regression is employed to assess whether the impact of oil price on real GDP differs across different states of the economy. Findings & Value added: Empirical results suggest the absence of long-run cointegrating relationships between oil price and GDP in all countries. The quantile regression reveals that oil price does not affect real GDP in the same way across countries and for different business cycle phases. More specifically, the symmetric quantile regression findings reveal that oil price exerts a positive impact on GDP in all countries and that the effect is higher during the recession than expansion states. The asymmetric quantile regression shows that GDP reacts to positive oil price changes in all countries. However, only the Emirati and Omani GDPs are affected by negative oil price changes. [ABSTRACT FROM AUTHOR]

The macroeconomic outcomes of oil price fluctuations have been at the forefront of the debate among economists, financial analysts and policymakers over the last decades. Among others, the oil price–food price nexus has particularly received a great deal of attention. While an abundant body of literature has focused on the linear relationship between oil price and food price, little is known regarding the nonlinear interactions between them. The aim of this paper is to conduct aggregated and disaggregated analyses of the impact of the Brent and West Texas Intermediate (WTI) oil prices on international food prices between January 1990 and October 2017. The empirical investigation is based on the estimation of linear and nonlinear autoregressive distributed lag (ARDL) models. The findings confirm the presence of asymmetries since the overall food price is only affected by positive shocks on oil price in the long-run. While the dairy price index reacts to both positive and negative changes of oil price, the impact of oil price increases is found to be greater. Finally, the asymmetry is present for some other agricultural commodity prices in the short-run, since they respond only to oil price decreases. All in all, the study concludes that studies assuming the presence of a symmetric impact of oil price on food price might be flawed. The findings are important for the undertaking of future studies and the design of international and national policies in the fight against food insecurity. [ABSTRACT FROM AUTHOR]