Union Allowances Research Articles

Spillover effect between carbon spot and futures market: evidence from EU ETS.

The spillover effects ofEuropean Union Allowances (EUA) spot and futuresmarkets are important for investors in order to understand the relevance and risk management of product prices. This paper uses non-linear methods of Granger causality to test the mean spillover relationship between the two markets and then analyzes volatility spillovers between the two by the non-linear TVP-VAR spillover index. The results show that (1) the non-linear Granger causality test better reflects the mean spillover relationship between EUA spot and futures; (2) there is a bidirectional non-linear mean spillover effect between EUA spot and futures prices for the European Union Emissions Trading Scheme (EU ETS) phases II and III; and (3) volatility spillovers, appearing in EUA spot and future markets in both phases, work increasingly strong over time and are vulnerable to financial crises and extreme events.

The impact of the global stock and energy market on EU ETS: A structural equation modelling approach

Abstract The industrial revolution has brought about great development in the economy, but it has also increased the dependence on fossil energy. The emissions of CO2 and other greenhouse gases have contradicted economic development and the ecological environment. The establishment of the EU Emission Trading System (EU ETS) has improved the global carbon emission price mechanism, but as a new commodity, its price trend will affect buyers’ risk evaluation. Therefore, it is influential to master the driving factors behind carbon emission prices and make effective predictions. First, the paper points out that the driving factors are divided into macroeconomic risk factors and energy factors. Second, the Bayesian Network is used to select variables and make prediction of carbon prices. The results show that its accuracy exceeds other machine learning algorithms. Third, a structural equation model is used to study the impact of the selected markets on the carbon market. Finally, from the perspective of global carbon emission reduction, the relationship between driving factors and the carbon futures market is explained. The empirical results show that Cotation Assistee en Continu 40, natural gas and Brent crude oil will directly affect the yield of European Union Allowances and Certified Emission Reduction futures, and the Standard Poor 500 and Global Clean Energy Index will indirectly affect the yield of European Union Allowances and Certified Emission Reduction futures. The energy market will affect the carbon market through the intermediary effect of the stock market, in which the clean energy index is the most relevant factor. From the perspective of how to improve the carbon trading system, this paper proposes suggestions for the sustainable development of the world to promote the virtuous cycle of the global carbon emission market and the high-quality development of the global economy.

Estimation of a term structure model of carbon prices through state space methods: The European Union emissions trading scheme

AbstractThis study models the term structure of the European Union Emissions Trading Scheme. The one‐factor geometric Brownian motion model of Abadie and Chamorro is replicated using the data now available and then compared with a two‐factor short‐term/long‐term (STLT) stochastic model. The STLT model has the better statistical fit to the term structure of European Union Allowances (EUAs). A real options analysis of the value of the option to retrofit carbon capture and storage shows that forecasting phase four EUAs with the STLT model almost triples the estimated project net present value and lowers investment trigger prices by approximately 24 percent.

Short-term European Union Allowance price forecasting with artificial neural networks

The European Union Emissions Trading Scheme (EU ETS) was created to reduce greenhouse gas emissions. Companies producing carbon emissions have to manage associated cash flows by buying or selling carbon allowances. Moreover, future carbon prices could affect company decision making on decarbonization technology investments. In this paper, we forecasted short-term future carbon allowance prices using an artificial intelligence tool: a neural network. The resulting mean error was 1.7617 %. This is indicative of very good performance for a time series whose evolution is influenced by subjective economic and political decisions. The inclusion in the forecasting model of variables possibly directly related to the evolution of the price of CO2 emission allowances did not improve prediction accuracy. Therefore, we can assume that emission allowances evolve following a random path. The neural network provided reliable predictions which agents selling or buying allowances can use to make their decisions.

The volatility spillover effect of the European Union (EU) carbon financial market

This paper modifies the BEKK-GARCH model based on the empirical results of the VAR model to analyze the dynamic volatility spillover effect between the European Union allowance (EUA) and certified emissions reduction (CER) markets during the second and third phases of the European Union Emission Trading System (EU ETS). The empirical results show that (1) an asymmetric volatility spillover effect exists between the EUA and CER markets and that the EUA market has a more significant volatility spillover effect on the CER market, and (2) the volatility spillover effect between the EUA and CER becomes weaker in phase III since the European Commission has limited the substitution of CER for EUA more strictly and the global carbon reduction requirements have become less demanding. Our study can help investors and managers of carbon market to have a more comprehensive understanding of the information and risk transmission mechanism between the EUA and CER markets, thus, providing them with a basis to make investment decisions and formulate policies.

The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market

China, taking the concept of sustainable development as the premise, puts forward Intended Nationally Determined Contributions (INDC) to reduce the greenhouse gas emissions in response to climate change. In this context, with the purpose of seeking solutions to a carbon financial market pricing mechanism to build China’s carbon finance market actively and thus achieving the goal of sustainable development, this paper, based on the autoregressive integrated moving average (ARIMA) model, established a carbon price prediction model for the carbon financial market, and studied the relationship between Certified Emission Reduction (CER) futures prices and spot prices, as well as the relationship between European Union allowances (EUA) futures prices and CER futures prices in an empirical manner. In this paper, EUA and CER futures prices of the European Climate Exchange (ECX) and EUA and CER spot prices of the BlueNext Environmental Exchange were selected as research objects. Granger causality test, co-integration test, and ECM were used to form a progressive econometric analysis framework. The results show that firstly, the ARIMA model can effectively predict carbon futures prices; secondly, CER futures prices cannot guide spot price, and the futures pricing function does not play a role in this market; thirdly, EUA futures price can, in the short term, effectively guide the trend of CER futures prices, with the futures pricing function between the two markets. In the long run, however, the future pricing function of the two markets is not obvious. Therefore, great differences among maturity of the two markets, degree of policy influence, and market share lead to the failure of long-run futures pricing functions.

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation

Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.

The Long-Term Trend Analysis and Scenario Simulation of the Carbon Price Based on the Energy-Economic Regulation

Purpose China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market. Design/methodology/approach To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends. Findings Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios. Originality/value On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Coal phase-outs and carbon prices: Interactions between EU emission trading and national carbon mitigation policies

The European Union Emission Trading System (EU ETS) constitutes the core instrument of the European Union climate protection policy. It limits greenhouse gas emissions of its member states and aims at facilitating an efficient allocation of emission reduction across national borders. Accompanying this policy at the European level, individual member states have introduced national mitigation policies, including renewable energy (RES) expansion measures or coal phase-outs.This study examines to what extent national policies affect the effectiveness of the EU ETS and to what degree the impact is reflected in prices for European Union Allowances (EUA). To investigate this question, a fundamental optimization model of the European electricity markets is deployed and model endogenous EUA prices are derived with a set of future market scenarios. Overall findings indicate that fundamental market forces strongly affect EUA prices. Furthermore, national policies play a critical role: The expansion of RES does not affect the capacity of the EU ETS to provide sufficient price signals for the desired level of decarbonization but a coal phase-out has a strong price-suppressing effect. A withdrawal of certificates can re-establish the effectiveness of the EU ETS but prices can rise drastically when overestimating the necessary amount.

Carbon and inflation

This paper investigates whether European Union Allowances (EUAs) can serve as an inflation hedge for two economic areas, four euro countries and two non-euro countries. The Extended Fisher Hypothesis is tested and the evidence shows a strong positive relationship between EUA returns and the unexpected inflation component in all the economic areas or countries analysed, except for the US. Therefore, EUAs are able to provide a hedge against unanticipated inflation rates.

What Happened to the CER Market? A Dynamic Linkage Effect Analysis

The certified emission reduction (CER) carbon trading market promoted by the clean development mechanism (CDM) has become an important platform for the development of the international carbon market. However, the CER carbon market has shown unsteady development with the present phenomena of price decrease, transaction inactivity, and recession. Against this backdrop, this study aims to explore the intuition behind CER price volatility from the new perspective of internal and external market dynamic linkages. By introducing three homogeneous carbon products of CER futures, namely, the daily dataset of CER spot, EUA (European Union Allowance) spot and EUA futures, and taking five heterogeneous market drivers comprising stock, exchange rates, coal, crude oil, and natural gas into account, we analyze the dynamic correlations and volatility spillovers between CER futures returns and these influencing factors using the DGC-MSV model. With sample data from January 2013 to May 2019, our empirical results show a persistent dynamic dependence between CER futures price and its factors. The homogeneous and heterogeneous markets have significant positive and negative spillover effects, respectively, on the CER futures market. The decline of CER futures price in the post-Kyoto era is due to two aspects: fluctuation of the exchange rate market, which is closely connected to the settlement of currency, and coal price volatility in energy markets. However, the CER futures market has no obvious spillover effect on other markets, except for its strong impact on the CER spot market and weak information spillover to the exchange rate market. Overall, this finding indicates the feeblest financial property of CER carbon futures market.

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems

Effective hedging strategies are important in reducing price volatility risk for business investors and companies participating into carbon markets. In this paper, we investigate the risk triggered by price fluctuation of European Union Allowance (EUA). A semi-parametric approach with Cornish-Fisher expansion, which approximates the quantile using the higher moments of the distribution, is provided to estimate hedging ratios using CVaR as risk objective function. The approach can successfully capture the features of higher moments with the heavy-tailed and higher kurtosis distribution of the EUA returns which tends to be neglected. The hedging performances of Minimum-CVaR model we proposed and the conventional Minimum-Variance model are evaluated and compared. Our empirical results show that Minimum-CVaR hedging strategy generally outperforms the other in-sample using all the effectiveness criteria while it is not consistent out-of-sample. The proposed semi-parametric Minimum-CVaR strategy with Cornish-Fisher expansion is advisable in carbon market hedging problems.

Flexibility in the market for international carbon credits and price dynamics difference with European allowances

The Paris Agreement establishes a mechanism which allows a Party to benefit from greenhouse gas emissions reductions conducted in a host Party to fulfil its nationally determined contribution. In this context, the objective of this paper is to improve the understanding of carbon offset price dynamics in comparison with regular carbon market allowances. We combine a cointegration approach with risk premium considerations to compare the price dynamics of European Union Allowances (EUA) and Certified Emission Reductions (CER) in the second phase of the European carbon market. By taking account of breaks identified in the series, we find that, while the EUA and CER returns present comparable dynamics mainly driven by fuel switching, the long-term relationships between the price of these two types of permits and their drivers differ significantly. Whereas the price of EUA is well explained by a demand effect, the impact of energy prices on the CER price suggests the existence of a supply-side effect for credits. We find that the price elasticity of allowances with regard to the coal and gas prices is negative in time periods of low economic activity and positive during the remaining time. We explain the former with the fact that the market is not tight and the latter with the effect of the economic activity on the price of commodities and energy.

Return and volatility linkages between CO2 emission and clean energy stock prices

The emission of CO2 has emerged as one of the key factors behind the significant growth of clean energy sources that are less carbon-intensive than conventional energy bases. However, investigating the link between the carbon emission market and the market of clean energy stocks remains extremely understudied. Accordingly, this study examines daily return and volatility linkages between the European Union Allowance (EUA) prices and clean energy stock returns. Employing the bivariate VAR-GARCH approach, we document that variations in the EUA prices affect the renewable energy stock returns positively, though the association is usually found to be statistically insignificant. Our findings further indicate a significant volatility linkage between emission and European clean energy price indexes. Such findings, however, do not hold for the US market, suggesting that emission return and volatility shocks are country- or region-specific. Further results show that the average correlations between emission and stock prices is very low and hence portfolio diversification benefits seem to be attainable if investors hold assets in the EUA and clean energy equity markets.

The erratic behaviour of the EU ETS on the path towards consolidation and price stability

The Kyoto Protocol envisages the use of various instruments to achieve emission reduction targets, one of which is the European Union Emission Trading Scheme (EU ETS), the most important market worldwide for CO2 emission allowances. The volume of European Union Allowances traded represents over 45% of all the carbon dioxide generated by human activity within the continent. In its first two phases (2005–2012), the behaviour of the EU ETS was erratic, as a result of discretionary policies, an oversupply of allowances and reduced economic activity due to the global crisis. These factors caused excessively low prices that distorted the initial goals of achieving low-carbon solutions. From 2013, changes were made to the market regulation mechanisms in order to correct these structural deficiencies. Empirical analysis of daily prices in the two central phases of the market, following the pattern of ARCH and GARCH models, shows that the measures taken within the EU generated greater confidence and stability in the market and thus reduced volatility. Subsequent price behaviour, following a bullish path, has confirmed the success of the measures taken and their contribution to fulfilling emission reduction targets.

Carbon portfolio management

AbstractThe aim of the European Union's Emissions Trading Scheme (EU ETS) is that by 2020, emissions from sectors covered by the EU ETS will be 21% lower than in 2005. In addition to large CO2 emitting companies covered by the scheme, other participants have entered the market with a view of using emission allowances for the diversification of their investment portfolios. The performance of this asset as a stand alone investment and its portfolio diversification implications will be investigated in this paper. Our results indicate that the market views Phases 1, 2, and 3 European Union allowance futures as unattractive as stand alone investments. In a portfolio context, in Phase 1, once the short‐selling option is added, there are considerable portfolio benefits. However, our results indicate that these benefits only existed briefly during the pilot stage of the EU ETS. There is no evidence to suggest portfolio diversification benefits exist for Phase 2 or the early stages of Phase 3.

Measuring the risk-adjusted performance of CO2 emission markets: Evidence from SENDECO2

This paper analyzes the historical risk-adjusted performance of CO2 emission allowances traded on SENDECO2 (the reference market for Southern Europe) by using the daily spot prices of the European Union Allowances (EUAs) and Certified Emission Reductions (CERs) from 2008 to 2012. We revisit the Sharpe-ratio, taking into account the modified version proposed by Ferruz and Sarto (1997), to propose a new performance indicator, the Sharpe-VaRFS, estimated by Monte Carlo simulation. Due to the existing imbalances between demand and supply for allowances, both the EUA and CER markets underperform when compared with financial stock markets, being unattractive to potential investors.

Environmental versus economic performance in the EU ETS from the point of view of policy makers: A statistical analysis based on copulas

The European Union Emissions Trading System (EU ETS) was created with the aim of promoting reductions of greenhouse gas emissions in a cost-effective and economically efficient manner. In line with this objective, when making their decisions, policy makers should consider not only the CO2 reduction targets but also the influence of these pollution reduction goals on companies' economic performance. This paper analyses the relationship between the economic and environmental performance of a sample of Spanish companies involved in the EU ETS in order to provide more information to the institutions responsible for developing these policies. This relationship is considered from two perspectives: the first examines how a company's production affects the ratio of the level of emissions to allocated allowances (the EA ratio), while the second examines how the EA ratio affects company results. A statistical methodology based on copulas is used, which allows us to analyze the relationship between these variables without requiring the assumptions of joint normality and linearity, thus providing the study with greater flexibility and realism. Our results highlight the existence of three different periods that correspond to Phases I, II and III of the EU ETS. During Phase I (2005–2007), the relationship between the EA ratio and firms' production and profitability was weak and, in the case of production, not significant. In Phase II (2008–2012), the efficiency of the EU ETS was higher, the allocations were better adjusted to firms' activities, and firms with EA values close to 1 were the most productive and profitable. The same trend occurred in Phase III (2013–2015), where a significant reduction of CO2 emissions levels was observed but with higher EA values, especially in the Energy and Other Manufacturing sectors (including the Food, Textile, Leather, Footwear and Clothing, and Rubber and Paper industries). Therefore, although the environmental policy promulgated by the EU ETS is partially achieving its goal of reducing CO2 emissions, it is still necessary to encourage green investments in order to decrease the EA levels, which are too high to satisfy the European Union Allowances allocation policy.

Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk

The objectives of this study are three-fold. First, we aim to assess whether outliers or extreme observations occur in the European Union Allowance (EUA) data. Second, we examine if time-varying jumps are present in the carbon emission market. Third, we use the crude oil volatility index (OVX) to investigate the effect of oil market uncertainty on the emission price volatility. In order to detect the possible outliers in the EUA market, we employ a standard methodology proposed by Ané et al. (2008) and identify several outliers in the emission data. After spotting and then removing those extreme points, we apply the GARCH-jump models to both original and outlier free observations. The results of the jump model show that while time-varying jumps do exist in the uncorrected data, most of the jump parameters, however, become insignificant in case of outlier-free observations. Next, the application of an extended EGARCH model, in which OVX is introduced in the GARCH specification, demonstrates that emission prices are highly sensitive to oil market implied volatility and that the impact of OVX on the EUA market appears to be asymmetric. Additionally, the use of forecast encompassing test documents that considering the outlier-free data and using the information content of OVX would improve the volatility forecasts for the carbon emission market. The results of our research carry important implications for both investors and policymakers.