Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

Abstract

Using a fresh empirical approach to time-frequency domain frameworks, this study analyzes the return and volatility spillovers from fossil fuel markets (coal, natural gas, and crude oil) to electricity spot and futures markets in Europe. In the time domain, by an approach developed by Diebold and Yilmaz (2012) which can analyze the directional spillover effect across different markets, we find natural gas has the highest return spillover effect on electricity markets followed by coal and oil. We also find that return spillovers increase with the length of the delivery period of electricity futures. In the frequency domain, using the methodology proposed by Barunik and Krehlik (2018) that can decompose the spillover effect into different frequency bands, we find most of the return spillovers from fossil fuels to electricity are produced in the short term while most of the volatility spillovers are generated in the long term. Additionally, dynamic return spillovers have patterns corresponding to the use of natural gas for electricity generation, while volatility spillovers are sensitive to extreme financial events.

The impact of economic uncertainty caused by COVID-19 on renewable energy stocks

“…Our results indicate that the main dynamic spillover effects during the pandemic are concentrated in the long term, which is consistent with the full-sample results in Table 4 . Furthermore, this result is in line with the related literature (Tiwari et al 2018 ; Trabelsi 2018 ; Liu et al 2020 ), which supports the notion that, based on the Baruník–Křehlík methodology, spillover effects from asset volatilities to asset volatilities are mainly focused on the long term.…”

“…This may be explained by the fact that the ID-EMV is a volatility tracker that could be regarded as a volatility index, and the spillover effects we calculated can be seen as those from asset volatilities to asset returns, while they are from asset returns to asset returns in the previous literature. Additionally, most time-varying spillover effects from uncertainty to volatilities during COVID-19 appeared in the long term, which is in line with the related literature (Tiwari et al 2018 ; Trabelsi 2018 ; Liu et al 2020 ). These findings confirm that shocks caused by COVID-19 in terms of economic uncertainty affect renewable energy stock returns and volatilities and last for a long period of time.…”

“…Fifth, the results of the dynamic frequency-domain analysis revealed that during COVID-19, the main time-varying spillover effects on returns from the resulting economic uncertainty were concentrated in the long term (low frequency), which is contrary to the results of previous studies (Tiwari et al 2018 ; Trabelsi 2018 ; Liu et al 2020 ). This may be explained by the fact that the ID-EMV is a volatility tracker that could be regarded as a volatility index, and the spillover effects we calculated can be seen as those from asset volatilities to asset returns, while they are from asset returns to asset returns in the previous literature.…”

“…This highlights the importance of considering dynamic spillover effect analysis because this phenomenon could not be observed with full-sample analysis. In contrast, some previous studies (Tiwari et al 2018 ; Trabelsi 2018 ; Liu et al 2020 ) analyzed the connectedness among different assets using the Diebold–Yilmaz approach and Baruník–Křehlík methodology and observed that the dynamic spillover effects of returns primarily exist in the short term. This could be because ID-EMV can be regarded as a volatility index, and the spillover effects obtained herein refer to spillovers from asset volatilities to asset returns, while they were from asset returns to asset returns in the previous literature.…”

Spillovers to Renewable Energy Stocks in the US and Europe: Are They Different?

“…Trabels [30] uses the Diebold-Yilmaz approach and Barunik and Krehlik methodology to study the connectedness among cryptocurrency markets, the Bitcoin index, traditional currencies, stock markets, gold, and crude oil. Liu et al [31] examine the spillovers of return and volatility from fossil fuel energies (crude oil, coal, natural gas) to electricity spot and three electricity futures in Europe using the same time-frequency domain frameworks. Tiwari et al [32] examine the volatility spillovers among four global assets, including currency, credit default swaps, sovereign bonds, and stocks, using the Diebold-Yilmaz approach and Barunik and Krehlik method.…”