European Union Allowances Futures Research Articles

The asymmetric nexus between the cryptocurrency market and the carbon market: Evidence from the quantile-on-quantile method

We employ a novel framework to measure the asymmetric nexus between the cryptocurrency market and the carbon futures market based on different market conditions. Specifically, we use a quantile-on-quantile regression (QQR) approach to explore the correlation between cryptocurrencies (Bitcoin, Ethereum, and Ripple) and European Union Allowance (EUA) futures. We find that there is an asymmetric relationship between markets that is affected by different cryptocurrencies and market conditions. Overall, Bitcoin or Ethereum are positively correlated with the carbon market, while the results of Ripple are more complex. Under certain conditions, EUA futures can be a better hedge against cryptocurrency risk.

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Time-varying risk aversion and dynamic dependence between crude oil futures and European Union allowance futures markets

In this paper, we propose the copula-mixed frequency data sampling model incorporating time-varying risk aversion (RA) (copula-MIDAS-RA model) to investigate the impact of time-varying RA on the dynamic dependence between crude oil futures and European Union allowance (EUA) futures markets. An empirical analysis based on the daily data on the Brent crude oil futures and EUA futures returns and the monthly data on the RA index shows that the Student-t copula-MIDAS-RA model has better goodness-of-fit than other copulas, suggesting that the tail dependence between crude oil futures and EUA futures markets is symmetric and time-varying. More importantly, we observe that the RA has a significantly positive impact on the dynamic dependence between crude oil futures and EUA futures markets. That is, the dynamic dependence between crude oil futures and EUA futures markets is expected to increase with the level of RA increases. Moreover, we observe that the Student-t copula-MIDAS-RA model improves the accuracy in risk management relative to other copula models. Our findings have implication for hedging strategies and asset pricing.

Forecasting the volatility of European Union allowance futures with time-varying higher moments and time-varying risk aversion

There is increasing evidence that European Union allowance (EUA) futures return distributions exhibit features of time-varying higher moments (skewness and kurtosis), which plays an important role in modeling and forecasting EUA futures volatility. Moreover, a number of studies have shown that time-varying risk aversion (RA) contains useful information for forecasting EUA futures volatility. In light of this, this paper proposes the GARCH-MIDAS with skewness and kurtosis (hereafter GARCH-MIDAS-SK) to empirically investigate the impact and predictive role of RA on EUA futures volatility. Our empirical results show that RA has a significantly negative impact on the long-term volatility of EUA futures. The EUA futures return distributions exhibit obvious features of time-varying higher moments. Incorporating RA and time-varying higher moments improves the in-sample fitting of the model. Furthermore, out-of-sample results suggest that incorporating RA and time-varying higher moments leads to significantly more accurate volatility forecasts. This finding is robust to alternative out-of-sample forecasting windows.

Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method

This paper develops an open-high-low-close (OHLC) data forecasting framework to forecast EUA futures price based on EU ETS data and extended exogenous variables from 2013 to 2020. The challenge of forecasting such an OHLC structure lies in handling its three intrinsic constraints, i.e., the positive constraint, interval constraint, and boundary constraint. This paper proposes a novel unconstrained transformation method for OHLC data and combines it with various forecasting models. Out-of-sample modelings identify the extraordinary performance of the convolutional neural network (CNN) in terms of MAPE (1.371%), MAE (0.274), RMSE (0.370), and AR (0.621), better than that of multiple linear regression (MLR), vector auto-regression (VAR) and vector error correction model (VECM), support vector regression (SVR), and multi-layer perceptron (MLP). The proposed transformation-based forecasting framework demonstrates the considerable potential for OHLC data forecasting in the energy finance field, e.g., crude and natural gas. Practicable and concrete suggestions are provided to ensure the profitability of trading EUA futures.

Forecasting the Volatility of European Union Allowance Futures with Climate Policy Uncertainty Using the EGARCH-MIDAS Model

We propose the EGARCH-MIDAS-CPU model, which incorporates the leverage effect and climate policy uncertainty (CPU) to model and forecast European Union allowance futures’ (EUAF) volatility. An empirical analysis based on the daily data of the EUAF price index and the monthly data of the CPU index using the EGARCH-MIDAS-CPU model shows that the EUAF’s volatility exhibits a leverage effect, and the CPU has a significantly negative impact on the EUAF’s volatility. Furthermore, out-of-sample analysis based on three loss functions and the Model Confidence Set (MCS) test suggests that EGARCH-MIDAS-CPU model yields more accurate out-of-sample volatility forecasting results than various competing models. There is room for further application of the model, such as this model could be applied to price carbon futures, so as to improve the liquidity of the carbon market and achieve carbon peak and carbon neutrality as soon as possible.

Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices

This research uses country-specific economic policy uncertainty (EPU) indices to predict the volatility of European Union Allowance (EUA) futures and compares dimension reduction techniques and combination forecasting methods to improve the accuracy of EUA futures volatility. We use the classic autoregressive (AR) model as a benchmark and construct some extensions of this model. The empirical results show that the economic policy uncertainty indices mainly have long-term predictive ability for the volatility of carbon futures. Both dimension reduction techniques and combination forecasting methods can produce robust long-term out-of-sample forecasting effects. However, these effects depend on the overall forecasting ability of the variables participating in dimension reduction and combination to a certain extent. We have also made a model extension to consider the out-of-sample forecast performance during periods of high volatility and low volatility. We find that country-specific EPU indices are more effective in predicting low volatility of the EUA futures. Finally, we confirm that our results are robust based on the recursive forecasting method.

Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model

This study investigates the impact of economic policy uncertainty (EPU) on the volatility of European Union (EU) carbon futures prices and whether it has predictive power for the volatility of carbon futures prices. The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance (EUA) futures. We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models. Our empirical results show that the GARCH-MIDAS models, which exhibit superior out-of-sample predictive ability, outperform GARCH-type models. The results also indicate that EPU has noticeable effect on the volatility of EUA futures. Specifically, the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index. Robustness checks further confirm that the EPU index (especially the EPU index of the EU) has strong predictive power for EUA futures prices. Additionally, using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index, investors can construct their portfolios to realize economic returns.

A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction

Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.

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.

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.

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.

Stochastic volatility of the futures prices of emission allowances: A Bayesian approach

Abstract Understanding the stochastic nature of the spot volatility of emission allowances is crucial for risk management in emissions markets. In this study, by adopting a stochastic volatility model with or without jumps to represent the dynamics of European Union Allowances (EUA) futures prices, we estimate the daily volatilities and model parameters by using the Markov Chain Monte Carlo method for stochastic volatility (SV), stochastic volatility with return jumps (SVJ) and stochastic volatility with correlated jumps (SVCJ) models. Our empirical results reveal three important features of emissions markets. First, the data presented herein suggest that EUA futures prices exhibit significant stochastic volatility. Second, the leverage effect is noticeable regardless of whether or not jumps are included. Third, the inclusion of jumps has a significant impact on the estimation of the volatility dynamics. Finally, the market becomes very volatile and large jumps occur at the beginning of a new phase. These findings are important for policy makers and regulators.

An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms

Climate change is a big challenge facing global community in 21st century. The carbon emission futures markets has been treated as a key tool to combat climate change cost-effectively. Making profits from futures trading is the fundamental incentive mechanism to keep this market run sustainably and effectively, while few technique analysis research on this topic has been done in the energy finance field. This paper contributes to the literature by proposing an integrated moving average rule for the European Union Allowance (EUA) futures market and designing an approach to optimize the weights of rules based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The similarity of trading rules designed here is used to select base rules. An integrated approach based on PSO and GAs is proposed to identify the optimal weights group for the selected base rules. A group of Adaptive Moving Average trading rules with different weights constitutes an integrated trading rule. Experiments using the EUA futures market price were conducted. The results show that: (1) our model is profitable in the EUA future market with the proper parameter except the case that prices fluctuate significantly; (2) the adjustment cycle of 5days is more useful than 20days or 50days; (3) the algorithm achieves the best performance at the 0.78 similarity threshold; (4) the rule with the short period of 150days and the long period of 200days is a useful building block for a successive rule set. This approach is a useful reference to the practical investments in EUA futures market.

Multifractal features of EUA and CER futures markets by using multifractal detrended fluctuation analysis based on empirical model decomposition

Basing on daily price data of carbon emission rights in futures markets of Certified Emission Reduction (CER) and European Union Allowances (EUA), we analyze the multiscale characteristics of the markets by using empirical mode decomposition (EMD) and multifractal detrended fluctuation analysis (MFDFA) based on EMD. The complexity of the daily returns of CER and EUA futures markets changes with multiple time scales and multilayered features. The two markets also exhibit clear multifractal characteristics and long-range correlation. We employ shuffle and surrogate approaches to analyze the origins of multifractality. The long-range correlations and fat-tail distributions significantly contribute to multifractality. Furthermore, we analyze the influence of high returns on multifractality by using threshold method. The multifractality of the two futures markets is related to the presence of high values of returns in the price series.

THE MULTI-FREQUENCY CORRELATION BETWEEN EUA AND sCER FUTURES PRICES: EVIDENCE FROM THE EMD APPROACH

Currently European Union Allowances (EUA) and secondary Certified Emission Reduction (sCER) have become two dominant carbon trading assets for investors and their linkage attracts much attention from academia and practitioners in recent years. Under this circumstance, we use the empirical mode decomposition (EMD) approach to decompose the two carbon futures contract prices and discuss their correlation from the multi-frequency perspective. The empirical results indicate that, first, the EUA and sCER futures price movements can be divided into those triggered by the long-term, medium-term and short-term market impacts. Second, the price movements in the EUA and sCER futures markets are primarily caused by the long-term impact, while the short-term impact can only explain a small fraction. Finally, the long-term (short-term) effect on EUA prices is statistically uncorrelated with the short-term (long-term) effect of sCER prices, and there is a medium or strong lead-and-lag correlation between the EUA and sCER price components with the same time scales. These results may provide some important insights of price forecast and arbitraging activities for carbon futures market investors, analysts and regulators.

Nonlinear price dynamics between CO2 futures and Brent

The growing expansion of European Union Emission Trading futures market and the parallel increase in the tendency of using such products also as financial instruments in the capital market outline the interest in the analysis of European Union Allowances (EUA) futures prices drivers. We applied recent developments in the threshold cointegration approach to investigate the presence of asymmetric dynamic adjusting processes between CO2 futures and spot Brent prices. We found evidence of the existences of long-run nonlinear relationships, with Brent prices driving EUA futures prices to a long-run equilibrium up to a point where the gap between the two prices exceeds a critical threshold. Hence, EUA futures market currently do not act only as a tool for compliance within the environmental regulation (i.e. to contain CO2 emissions), but has become a financial instrument to support firms' profit-maximizing behaviour.

An investigation into the evolved relationship between spot and futures in the European Union Emission Trading Scheme

This paper fills the gap in the literature that the functions of the European Union Allowance (EUA) futures have only been studied in the pilot phase of the European Union Emission Trading Scheme (EU ETS) and no market evolvement has been explored. The cost-of-carry pricing efficiency of the EUA futures and information spillover between the EUA spot market and futures market are examined in Phase I (2005–2007) and Phase II (2008–2012), respectively. First, it was found that only the December 2007 futures price was in long-run equilibrium with the spot price, but the relationship was not given by a cost-of-carry model. Second, the information spillover pattern also changed from Phase I to Phase II. The leading role of futures contract in price discovery can only be seen in Phase I, but not in Phase II. We conclude that, in the second phase of EU ETS, the market fundamentals are imposing stronger impacts on EUA price formation while the impact from speculation has weakened significantly.