A model of price correlations between clean energy indices and energy commodities

ABSTRACT

We propose a new supply and demand-based correlation model of clean energy indices and energy prices with the influence of energy on clean energy business including renewables. 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. Results show the correlations between GCE or ECO and WTI crude oil or HH natural gas prices are positive and an increasing function of the corresponding energy prices. It seems 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 increase in line with energy prices. In contrast, results show the correlations between S&P 500 and WTI or HH prices are still positive but 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.

KEYWORDS:

• Renewables
• fossil fuels
• correlation
• volatility
• leverage effect
• market risk

Acknowledgements

This research is supported by a grant-in-aid from Financial Support Programme for Green Bond Issuance for FY2018: Research on pricing, risk, impact, etc. of Green Bonds and others. Views expressed in this paper are those of the author. All remaining errors are mine. The author thanks John Constable, Toshiki Honda, Matteo Manera, Ryozo Miura, Nobuhiro Nakamura, Kazuhiko Ōhashi, two anonymous referees, and the editor-in-chief Olaf Weber for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Clean energy indices are created from stock prices relevant to clean energy related companies.

2 Leverage effect is a phenomenon in which price and its volatility have a negative relationship. It is often observed in financial markets.

3 Inverse leverage effect is a phenomenon in which price and its volatility have a positive relationship. It is often observed in energy markets.

4 Define $Q_t\equiv \left(\begin{array}{cc}{q}_{11}& q_{12}\\ q_{21}& q_{22}\end{array}\right)$. Then, $Q_t^{\ast }=\left(\begin{array}{cc}\sqrt{q_{11}}& 0\\ 0& \sqrt{q_{22}}\end{array}\right)$.

5 ${\theta }_2$ represents the persistence of the conditional covariance matrix. Since the standardized shock ${\eta }_t$ is used for the calculation, ${\theta }_2$ is approximately considered as the conditional correlation persistence.