A Model of Price Correlations between Clean Energy Indices and Energy Commodities

Abstract

This paper theoretically and empirically examines the relationship between environmental value embedded in clean energy indices and energy value obtained from energy prices by focusing on the influence of energy risk on clean energy business including renewables. We propose a supply and demand-based correlation (CR) model of clean energy indices and energy prices that takes into account the influence of energy on clean energy business including renewables. We also propose a market risk model based on CR model to conduct the risk management for stocks of clean energy firms appropriately. Empirical studies estimate the model parameters using the stock indices and energy prices including S&P Global Clean Energy Index (GCE), Wilderhill Clean Energy Index (ECO), S&P/TSX Renewable Energy and Clean Technology Index (TXCT), S&P 500, WTI crude oil prices, and Henry Hub (HH) natural gas prices. It is shown by using the model that the correlations between GCE or ECO and WTI crude oil or HH natural gas prices be positive and be an increasing function of the corresponding energy prices. Results seem reasonable because the values of renewable energy businesses, which sell electricity in the spot market, are enhanced by the increase in energy prices, considering that electricity spot prices tend to increase in line with energy prices. In contrast, it is also shown that the correlations between S&P 500 and WTI or HH prices be still positive but be a decreasing function of the energy prices. This sharp contrast may come from the fact that the S&P 500 listed companies’ businesses can be damaged by high energy prices while not applicable to GCE and ECO companies. Regarding TXCT, the correlations with WTI are positive and are a decreasing function of WTI while those with HH tend to be positive and are an increasing function of HH. It may suggest that TXCT is not fully functioning but still developing as a clean energy index, taking into account the results of GCE and ECO. Regarding market risk, CR model demonstrates different VaR from ordinary normal distribution (OND) model because CR model includes more upward or downward sloping demand curve shape reflecting the reality of the markets than the exponential in OND model, resulting in positive or negative impacts of prices on the volatilities in high clean energy index regions, respectively. We compare CR model with existing dynamic conditional correlation (DCC) model. Since CR model demonstrates the same level of the correlations from DCC model, CR model can work well as the correlation model.