The determinants of the renewable energy stock returns and associated investments

1. Context of the research

The research would be into the renewable energy (RE) market and what drives the use of RE. The RE sector has been experiencing extraordinary growth over the past few decades. In 2019, the International Energy Agency (IEA, 2019) estimated that the renewable power capacity would be expected to grow by 50% globally between 2019 and 2024 with solar PV making the largest contribution. The current Covid-19 crisis, however, brings several disruptions with worldwide installations of renewable power expected to fall in 2021, which would be the first decline in 20 years (IEA, 2020). Given countries throughout the UN are determined to achieve the Sustainable Development Goals by 2030, the uncertainty and potential risks associated with distribution to meet these commitments may cause severe disruption to national economies and delay decarbonisation advancements (UN, 2018). Therefore, it is important to understand the factors determining the development of the RE sector and associated investment.

2. Aims and objectives

The aim of the project is to investigate the dynamics of the relevant EU variables in the context of VAR approach and apply the results towards policy analysis/design which will potentially prompt an increase in RE production and the reduction of greenhouse gas emissions. This will involve investigating the determinants of the RE industries financial performance. The 5 key variables will be analysed: ERIX (European Renewable Energy Total Return Index), Brent (Brent futures closing oil prices), STOXX (STOXX Europe 600 Technology Net Return Index), EURIBOR (3-months EURo InterBank Offered Rate) and VIX (stock market volatility). The data can be found on their respective websites: Societe Generale, Investing.com, STOXX Digital, Bank of France and Chicago Board Options Exchange. A vector autoregressive (VAR) model is a proposed empirical approach with an aim to apply the recent innovations in econometrics such as the Bayesian VAR. The analysis will also include a Granger causality test, impulse responses statistics, and historical decomposition to partly mitigate the drawbacks of the model and to better understand the dynamics of the data.

This research will add several innovative aspects to the existing literature. It would analyse some important new determinants of the RE sector, including financial risk, such as the VIX. This paper will also adopt new variables into the analysis which have not been used in similar studies before such as prices of silver, copper, and lithium, or prices of other energy sources such as coal or gas. The project will also build on the literature by adding in some new EU countries into the analysis, especially the transition economies that haven't been widely used in these types of models yet. It will also incorporate policy factors, such as the current pandemic and effects of a new president in the US.

3. Potential benefits and applications

This topic is important because to meet the targets for reductions in greenhouse gas emissions, there needs to be a move away from the consumption of oil and towards increased use of RE. To enable greater RE production, there needs to be increased public and private sector resources allocated to it. In the private sector this involves raising funds from the financial markets, so the factors driving the investments in RE needs to be understood thus aiding the allocation of capital in this sector optimally.
The careful ex-post analysis of the underlying factors driving RE deployment may aid researchers and policy makers alike in the design of future policy pathways aiming to decarbonise the energy sector, reduce dependency on finite fossil fuels, decrease carbon emissions and encourage the transition towards a sustainable energy environment. The significance is also recognised by investors when they are faced with the decision to invest into the RE sector, construct portfolio weights or calculate hedge ratios.