Multiresolution analysis of information flows from international carbon trading market to the clean energy stock market

Abstract

This paper introduced a novel method to investigate the information flows from the international carbon trading market to the market for renewable energy stocks. The method combined the advantages of wavelet decomposition, transfer entropy, and complex network, which defined multiscale, directional, and dynamic flows of information. This analysis selected the daily futures prices of the EUA futures and the daily spot prices of the S&P Global Clean Energy Index (S&P GCE) as sample data and decomposed the bivariate time series into seven sequences for various time-frequency domains by maximal overlap discrete wavelet transform. Transfer entropy has been used to measure the process of transmission of information from the carbon trading market to the renewable energy stock market. The transfer entropies were transformed into four symbols and constructed directed and weighted complex networks of the transfer entropy fluctuation mode sequence in different time-frequency domains according to the concept of coarse graining. The findings showed that the carbon market information flows to the renewable energy stock market varied at different scales. In the short to medium term (2-16 days), there were a lot of information flows from EUA to S&P GCE. The information flow of EUA to S&P GCE is the most stable at scale d2 (4-8 days), but the volatility of the carbon market has not affected the clean energy market at scales d5-d6 (32-128 days). These findings provided the requisite reference for investors in carbon finance and policy-makers who support clean energy production.