• Energy Research

Abstract We study systemic risk and dependence between oil and renewable energy markets using copulas to characterize the dependence structure and to compute the conditional value-at-risk as a measure of systemic risk. We found significant time-varying average and symmetric tail dependence between oil returns and several global and sectoral renewable energy indices. Our evidence on systemic risk indicates that oil price dynamics significantly contributes around 30% to downside and upside risk of renewable energy companies. These results have important implications for risk management and renewable energy policies.

Abstract We examine the link between oil prices and market-based inflation expectations in the United States. Using a Gaussian affine term structure model, we decompose the breakeven inflation into three components: the market-based inflation expectations, the inflation risk premium and the liquidity risk premium. We show that oil prices have a nonlinear impact on the 5- and 10-year market-based inflation expectation components. Specifically, we find that the impact of oil price changes on inflation expectations is more intense when oil prices are above a threshold of 67 USD per barrel and is more pervasive for the intermediate term than for the longer term. Furthermore, we show that oil prices have a nonlinear impact on the inflation risk premium. These results have implications for the management of inflation expectations.

Abstract We studied the financial performance of alternative energy mutual funds using multifactor models and propensity score matching techniques. For a sample of alternative energy mutual funds quoted in EUR and in USD for the period 2010–2016, we found that alternative energy funds underperformed corporate and socially responsible mutual funds in terms of returns and downside risk protection. Our results are consistent with the idea that investors are paying a premium for going green via renewable energies.

In the transition to a low-carbon economy, climate-resilient investors may be inclined to buy renewable-energy or other low-carbon assets. As the diversification benefits of investment positions in those assets depend on interdependence between their market prices, we explore that interdependence in the European and USA stock markets. We model the dependence structure using bivariate copula functions and evaluate price spillovers between those markets using a conditional quantile dependence approach that accounts for the reciprocal effects of price movements in those markets under normal and extreme market scenarios. Our empirical evidence for the period 2010–2019 indicates that European renewable-energy and low-carbon stocks co-move; upward and downward movements in low-carbon asset prices have sizeable effects on renewable-energy asset prices, and vice versa, although effects are smaller. In contrast, for the USA we find evidence of non-interdependence, with no significant upward or downward price spillover effects between renewable-energy and low-carbon stocks. Our empirical findings provide useful insights for the design of carbon-resilient portfolios and risk management strategies, and also for implementation of public funding policies to support the transition to a low-carbon economy.

We studied co-movement and causality between oil and renewable energy stock prices using continuous and discrete wavelets, firstly, to obtain information on dynamic correlations over time and for different time scales from wavelet coherence and, secondly, to obtain information on linear and non-linear Granger causality in the time-frequency domain. For general and sectoral renewable energy indices for the period 2006–2015, our findings indicate that dependence between oil and renewable energy returns in the short run was weak but gradually strengthened towards the long run, mainly for the period 2008–2012. Our causality tests provide evidence against linear causality at higher frequencies and in favour of unidirectional and bidirectional linear causality at lower frequencies. In contrast, we found consistent evidence of non-linear causality running from renewable energy indices to oil prices at different time horizons and mixed evidence of causality running from oil to renewable energy prices. These results have potential implications for investors in terms of hedging and for policymakers in terms of policy support decisions regarding the development of renewable energy.

Abstract We study the impact of Twitter sentiment and sentiment divergence on returns, volatility and trading volumes for renewable energy stocks. Based on daily time series for Twitter sentiment and Twitter sentiment divergence, we estimate VAR models and evaluate spillovers between sentiment and renewable energy stock pricing and trading. We find that whereas Twitter sentiment has no sizeable impact on returns, volatility or trading volumes, Twitter sentiment divergence generates feedback effects on volatility and trading volumes. Our evidence would indicate that the wisdom of the Twitter crowd is not substantial in shaping prices and trading for renewable energy companies.

Abstract Renewable energy deployment is arguably the best way to address the challenges of climate change. This paper investigates convergence in the contribution of renewable energies to the energy supply for a broad set of countries for the period 1990 to 2010. Using a nonlinear time-varying coefficients factor model and a pooled mean group estimator for dynamic heterogeneous panels, our empirical results mainly suggest divergence and dissimilar temporal patterns in the contribution of renewable energies to the energy supply. Only a small number of countries with significant and growing renewable energy sectors display convergence. Our results point to uneven efforts to develop sustainable energies across countries, indicating that greater international cooperation is required in order to push more sustainable development.

Abstract This paper studies co-movements between world oil prices and global prices for corn, soybean and wheat using copulas. Several copula models with different conditional dependence structures and time-varying dependence parameters were considered. Empirical results for weekly data from January 1998 to April 2011 showed weak oil-food dependence and no extreme market dependence between oil and food prices. These results support the neutrality of agricultural commodity markets to the effects of changes in oil prices and non-contagion between the crude oil and agricultural markets. However, dependence increased significantly in the last three years of the sampling period, even though upper tail dependence remained insignificant, indicating that food price spikes are not caused by positive extreme oil price changes. These results have implications for policy design, risk management and hedging strategies.

Abstract We identify network connectedness between green bonds and different asset classes over different investment horizons in the EU and US asset markets. We first focus on dynamic correlations between green bond and asset class price changes in different time scales on the basis of wavelet coherence. We then identify network connectedness by decomposing time series into different frequency components, in each of which we evaluate connectedness on the basis of the error variance decomposition of a multivariate vector autoregressive model. Our empirical evidence reveals strong connectedness between green bonds and treasury and corporate bonds in the short and long run and in both the EU and the USA, with green bonds receiving sizeable spillovers from treasury and corporate bond prices and transmitting negligible effects. Likewise, we find that green bonds are weakly connected with high-yield corporate bond, stock and energy assets over different time scales. These findings have implications for green bond investors regarding portfolio design and hedging decisions, and for the channelling of financial flows to economic activities that are consistent with a decarbonized economy.