Dynamic Asymmetric Volatility Spillover and Connectedness Network Analysis among Sectoral Renewable Energy Stocks

Abstract

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.