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Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency

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  • Freudenberg & Co. KG
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Abstract

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well captured by a fractionally integrated asymmetric power GARCH process. The decisions of the European Commission on second National Allocation Plans have a strong and immediate impact on EUA prices. Further, EUA prices increase in response to better than expected news on the future economic development as well as the current economic activity in Germany and the U.S.

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... The introduction of carbon markets has resulted in the emergence of a new class of investors who want to have financial exposure to GHGs [30,31]. There is rising interest in the risk management of carbon assets, which can be utilized for a variety of investment objectives, including portfolio diversification, arbitrage, hedging, and speculation [30,32,33]. Liu et al. [31] study the mean and volatility spillovers by non-linear methods of Granger causality, showing there is a bidirectional spillover effect between European Union Allowance (EUA) spot and future prices for Phase II and III of the EU ETS. ...
... Most studies in the field of carbon markets focus on the relationship between the carbon market and other energy and/or financial markets [40,41,75], and [81]. Other studies concentrate on local/domestic carbon markets [24,32], and [45]. These studies did not account for return and/or volatility spillovers across the carbon markets. ...
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Emission Trading Schemes (ETSs) have become vital for meeting global emission reduction targets. They are gaining momentum, as witnessed by increasing market size and improving information mechanisms. Examining key emission markets — European Union, New Zealand, California, and Hubei (China) — from April 2014 to December 2021, a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is applied to discern the markets' connectedness. In a novel approach to global carbon market research, this study uniquely combines the TVP-VAR with the connectedness approach, overcoming fixed parameters estimation and ensuring precise parameter estimates. The approach sheds light on patterns of total, directional, and net return/volatility spillovers, striving to identify which markets act as transmitters and which are receivers. Linking market spillovers to market characteristics, events, and policies offers insights for investors and policymakers. The total connectedness index of 10–12 % suggests a relatively low level of spillover, when compared to other market integration studies. The dynamic nature of return and volatility spillovers is evident, especially during the energy crisis and Covid-19 outbreak. The EU's ETS consistently acts as a net transmitter, predominantly in return connectedness, while New Zealand's ETS emerges as a major shock receiver in both return and volatility systems. Global climate negotiations and carbon market events have only a minor impact on the level of connectedness, in contrast to energy or financial crises and the Covid-19 outbreak. By highlighting the intricacies of carbon price volatility and market transmissions, the findings equip stakeholders with invaluable, actionable insights.
... But these factors differ in factor selection, time, or spatial dimension and are not necessarily leading to consistent conclusions [9]. Much more literature focuses on the European carbon market or individual pilot market in China [4,[10][11][12][13][14][15][16], while there are few literature studies for the 2017-2021 period of the China markets. Factors vary according to single or mixed types, and the corresponding impacts empirically differ in their correlation direction and degree across pilot markets [1,4,15]. ...
... Macroeconomic activities affect the production and operation of emission-control enterprises and the supply and demand of the carbon market, as well as cause fluctuations in the CTP. Empirically, Conrard et al. modeled the adjustment process of EUA prices in accordance with the European Commission's second national allocation plan and demonstrated that the rise in EUA prices was inseparable from future and current economic activities [12]. ...
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The Carbon trading price (CTP) can best reflect the fluctuations of the carbon trading market. This paper comprehensively analyzes the CTP mechanism of China’s carbon trading market, discusses the main factors affecting China’s CTP, which include macroeconomic factors, energy price factors, policy factors, and environmental factors, and provides three hypotheses. In order to highlight and test the three hypotheses about the CTP, five representative carbon trading pilot markets were included: Beijing, Shenzhen, Shanghai, Hubei, and Guangdong, and the daily average price data (over years) were adapted from January 2017 to December 2021, using a dynamic heterogeneous panel PMG model. The current paper selects the China air quality index (AQI), Bohai-Rim steam-coal price index (BSPI), Liquefied natural gas index (LNGI), and the Shanghai stock exchange industrial index (SSEII) as the explanatory variables. The empirical results show that there is a long-term equilibrium relationship between the CTP, AQI, energy price, and macroeconomics. Strengthening environmental governance, optimizing the energy structure, and expanding the carbon trading market coverage should be adopted to improve the China carbon emission trade exchange (CCETE) and stabilize the CTP.
... Vallier (2011) [14] studied the instability in carbon prices during 2005-2008 based on retrospective [9,[15][16][17] and forward-looking tests [18,19]. Conrad et al. (2012) [20] modeled the price adjustment process of the EUA and found that fractionally integrated asymmetric power GARCH can well capture highfrequency EUA price dynamics. Wu et al. (2015) [21] used the Bai-Perron method to test the number of structural changes and corresponding time-varying points of the EUA price. ...
... Vallier (2011) [14] studied the instability in carbon prices during 2005-2008 based on retrospective [9,[15][16][17] and forward-looking tests [18,19]. Conrad et al. (2012) [20] modeled the price adjustment process of the EUA and found that fractionally integrated asymmetric power GARCH can well capture highfrequency EUA price dynamics. Wu et al. (2015) [21] used the Bai-Perron method to test the number of structural changes and corresponding time-varying points of the EUA price. ...
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The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a determin-istic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events.
... Research on carbon emission credit predictions has been conducted since the 2010s. These studies are meaningful early studies attempting to predict carbon em credits in the EUA (European Union Allowance) [13,14,[23][24][25][26][27]. In these studies, sp indicators within one particular temporal range were set as independent variables the relationship with carbon emission credits was presented. ...
... Research on carbon emission credit predictions has been conducted since the early 2010s. These studies are meaningful early studies attempting to predict carbon emission credits in the EUA (European Union Allowance) [13,14,[23][24][25][26][27]. In these studies, specific indicators within one particular temporal range were set as independent variables, and the relationship with carbon emission credits was presented. ...
Article
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Research related to the carbon-emission credit-price prediction model has only considered the effects of specific indicators, such as coal and oil prices, and only long-term prediction studies have been conducted. Recently, carbon emission credits have been recognized as investment assets, such as stocks and real estate. Accordingly, a carbon-emission credit prediction method is needed to establish an industrial strategy with low risk. In this study, an attempt was made to model the behavior of market participants in the time series model by analyzing the correlation between the search query volume data and the Korean Allowance Unit (KAU). Multiple Linear Regression Analysis (MRA) and Auto-Regressive Integrated Moving Average models were developed. In all price prediction models, the error of the prediction model at the 4th time was low. In the case of MRA, the error in the predicted near future price was small, but the error rate increased with increasing analysis period and prediction time. The error rate of ARIMA was lower than that of MRA, but it did not show a rapid change. These research findings will be beneficial to investigating and finding more rigid and reliable methodologies that can be used to predict various important values in similar fields in the future.
... Prior research lines analyzing emission allowances price, have focused on applying univariate or multivariate procedures. Univariate models, mainly apply ARIMA models and, in some cases, volatility models (8). Multivariate methods intend to justify the behaviour of emission allowances price through different variables, (2,11,12,15). ...
... Some economic variables focus on stock indices (11), macroeconomic and financial indicators (6) or business indices such as the Industrial production index (1,7,8). Their positive influence on allowances prices has been widely demonstrated, although depending on the variable itself and the study period. ...
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Analysis of emission allowances prices has important environmental and political connotations. This article aimed to identifying the possible variables that may influence their behaviour and studied their relationship with fundamental factors: energy (Brent petroleum, Gas, Coal) and economy (Industrial Production Index, Baltic Dry Index, Purchasing Managers Index). With the objective of analyzing possible mutual interactions, Multivariate VAR or Error Correction Models (VECM), were applied. The information analysed derived from different sources (World Bank, Sendeco2 and various financial websites). The results obtained showed, not only the influence of past prices on the emission allowances actual price, but also the interaction with energetic and economic variables. Highlights Estimation of time series interrelations through VAR models. There is relationship of the emission allowances price with their past values. The energy variables are factors important to also explain the behavior of the emission allowances price. The economic variables are hardly significant except for the Dry Baltic Index.
... First, we contribute to the literature on price determination within the EU emissions trading system (ETS). See Hintermann (2010), Creti et al. (2012), Conrad et al. (2012), Aatola et al. (2013), Hammoudeh et al. (2014), Koch et al. (2014), and Zhang et al. (2024) for various empirical models trying to explain EUA price dynamics. Relatedly, Dhamija et al. (2018), Adekoya et al. (2021), Zeng et al. (2021), Ding et al. (2022), andVellachami et al. (2023) empirically assess the role of volatility spillovers for the EU ETS. ...
... Second, we check the robustness of the time-frequency spillovers by controlling for the impact of intraday periodicity in return volatility, because some recent literature suggests that not considering intraday periodicity might result in misleading volatility spillovers when using high-frequency data (Dette et al., forthcoming;Nishimura and Sun, 2018;Alemany et al., 2019). In this regard, following Conrad et al. (2012), the intraday periodicity is removed by standardizing r t,k as follows: R t,k = r t,k /f k , where r t,k stands for the returns for each crude oil market at the end of the kth interval at day t, and k is the number of equidistant intervals during a trading day. The standardization scales each return r t,k by the average absolute return of the interval k. ...
Article
This paper tries to examine asymmetric and time-frequency volatility connectedness between the Chinese crude oil futures market and international oil benchmarks. To this end, we separate the realized volatility as bad or good volatility and decompose the aggregate volatility connectedness among these oil markets into the short-, medium-, and long-term components. Our results first show that these crude oil markets are highly connected, whereas the Chinese crude oil futures market is a net receiver in the volatility system. Second, the spillover effect caused by bad volatility is significantly different from that of good volatility, revealing significant asymmetry in the volatility spillovers. Third, the volatility spillovers in the short-term frequency band account for the most of total volatility spillovers. Finally, our results prove that the volatility connectedness information among different oil markets helps design trading strategies and shed light on the arbitrage opportunities in the Chinese new crude oil futures market.
... For instance, Paolella and Taschini (2008) use a mixed-normal generalized autoregressive conditional heteroskedasticity (GARCH) model to forecast carbon prices in the EU. Conrad et al. (2012) use fractional integration and asymmetric power GARCH models (FIAPGARCH) and indicating the dynamics of European Union Allowance prices at high-frequency. ...
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This study explores the predictive effect of climate change attention on carbon futures returns. Using climate‐related Google Trends and news, we construct five dimensions of the public climate attention index and media climate attention index. After feature selection, we incorporate the optimized combination with lagged order into the machine learning model to predict EU Emission Allowance futures returns. Our empirical results show that the forecasting models with climate attention outperform the corresponding benchmark models, indicating that climate attention does provide predictive information for carbon futures returns. In addition, we carry out trading simulations to investigate the economic performance of the forecast results. It turns out that the market strategies based on the prediction models with climate attention can deliver more benefits than the counterpart market strategies. More specifically, the cumulative returns reach 140% during the out‐of‐sample period, much higher than 79% of the cumulative returns of the buy‐and‐hold strategy.
... Such uncertainties could restrain the efficiency of the emissions abatement process, hamper the market's sustainable development, and lead to systemic financial shocks (Zhu et al., 2020). A growing strand of literature indicates that carbon market uncertainties originate from two sources, one being its own-market volatility (Charles, Darné, & Fouilloux, 2013;Chevallier, 2010;Kamdem, Nsouadi, & Terraza, 2016;Tang, Gong, & Shen, 2017) and the other being cross-market volatility with markets such as the energy market (Boubaker & Raza, 2017;Tan, Sirichand, Vivian, & Wang, 2020;Uddin, Hernandez, Shahzad, & Hedström, 2018;Wu, Wang, & Tian, 2020), the capital market (Ma, Wang, & Zhang, 2020;Yuan & Yang, 2020), the electricity market (Balcilar, Demirer, Hammoudeh, & Nguyen, 2016;Green, Larsson, Lunina, & Nilsson, 2018;Ji, Xia, Liu, & Xu, 2019), and others, as indicated by macroeconomic variables (Conrad, Rittler, & Rotfuß, 2012;Liu & Chen, 2013). In recent years, biofuel has been incorporated into the energy market as an alternative to fossil fuels to meet the "20-20-20 climate change and energy sustainability" target of the European Union (EU): a 20% reduction in the emissions of greenhouse gases compared to 1990, a 20% improvement in energy efficiency, and 20% of energy production from the renewable energy sources (European Environment Agency, 2017). ...
... Byun and Cho 4 used a GARCH-type model to predict European carbon price volatility and found that it outperforms other models. Conrad et al. 5 employed the FIAPGARCH model to predict carbon prices in the first and second stages of EU-ETS and indicated that FIAPGARCH can well capture the heteroscedasticity and long memory of carbon price fluctuations. However, econometrics is based on the assumption of linearity and stationarity. ...
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The accurate forecasts of carbon prices can help policymakers and enterprises further understand the laws of carbon price fluctuations and formulate related policies and investment strategies. Nowadays, many carbon price prediction models have been proposed. However, some models ignore the time–frequency relationship when considering exogenous variables and fail to measure their importance to the forecasting results, leading to unsatisfactory results. Therefore, this study proposes a novel hybrid model for carbon price forecasting on the basis of advanced multidimensional time series decomposition techniques and interpretable multifactor models. In the proposed model, multivariate fast iterative filtering is used to decompose carbon price and its exogenous variable sequence into several intrinsic mode functions, which can overcome the nonlinearity and nonstationarity of carbon prices and obtain their intrinsic characteristics. Meanwhile, temporal fusion transform (TFT) is used to interpret predictions for multivariate time series. TFT is a new attention‐based deep learning model combining high‐performance multihorizon prediction and interpretability and can adaptively select the optimal features for carbon price prediction. Five carbon markets in Guangdong, Beijing, Shanghai, Hubei, and Shenzhen are selected for experimental studies. Empirical results indicate that the proposed model outperforms the compared benchmark models in all performance metrics. In the interpretable output of TFT, the prediction of the high‐frequency part requires the participation of exogenous variables and has a long time dependence; for the middle and low‐frequency part, only using the carbon price itself and a short time step can lead to good results. This finding can inform future research on carbon price forecasting and help policymakers.
... Some research focuses on the impact of high-frequency information on carbon markets. Conrad et al. [18] modeled the EUA price-adjustment process with the release of high-frequency information and found that the release of NAP in Phase 2 and the news that the future economy is better than expected have an obvious impact on EUA prices. Chen et al. [19] studied the impact of high-frequency information related to macro-economic and verified emission announcements on prices, volatility, trading volumes and illiquidity in the European carbon futures market, defending the belief that some announcements can affect carbon prices within five minutes. ...
Article
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Using the event study methodology, the paper studies the effects of 22 key events in countries’ process of entering and exiting the European Union on returns of European Union Allowance (EUA) future prices in the EU Emissions Trading System (EU ETS). The events include 17 entry events concerning the signing of relevant agreements, becoming a candidate or potential candidate country, the process of a negotiation and formally entering the EU, and five exit events including the process of Brexit and the suspension of Iceland. The results show that two entry events involving Albania and Ukraine, respectively, have a significant positive impact, and five entry events have a significant negative impact. Among the exit events, the announcement of the Brexit referendum results causes significant negative market reaction. Most events regarding small carbon emitters entering the EU lead to negative cumulative abnormal returns (CAR) of EUA prices, and a significant negative correlation between the countries’ annual average carbon emissions and CAR is found, while the change of national allocation plans does not affect the market reaction notably. In the process of establishing a unified carbon market, regulators should carry out appropriate policy arrangements of emission allowances allocation when new members join, in order to guide market expectations and enhance market stability.
... The inverted U-shaped relationship was found by Holtz-Eakin and Selden [23]. Conrad [24] modeled the adjustment process of EU allowance prices and found that EU carbon allowance futures prices are not only influenced by the current economy, but also closely linked to the future economic conditions of the US and Germany. Using a long-and short-term causality test, Yasheng Zou and Wei Wei [25] used the industrial production index as a proxy for the macroeconomy and concluded that there is a significant positive relationship between the two. ...
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Currently, global warming became a world focus as its potential impacts on all human beings. To prevent the damages of greenhouse gas emissions, the carbon market established and operated for several years. In this paper, due to an important financial reality that the carbon market should be regarded as a common financial market, this paper briefly reviews the pricing factors of the novel carbon asset to benefit the market when it is time to price the carbon asset. And finally, this paper points out the potential future investigations.
... A potential explanation is that the Covid-19 policies in China are unique in terms of the strict lockdown: due to closing the boarder, the movement at HB ETS could hardly be impacted by other ETSs.Most studies in the field of carbon markets focus on the relationship between the carbon market and other energy and/or financial markets73,79]. Other studies concentrate on local/domestic carbon markets[29,44,48]. These previous studies did not deal with the directional return and/or volatility spillovers (from/to a particular market) across the four markets. ...
... Further, biofuels can be used to manage and mitigate the adverse effect the price volatility of crude oil has on energy and non-energy portfolios, and to make the issue of energy security less uncertain in countries not endowed with natural resources for energy generation. Lastly, fossil fuel substitutes, such as those considered in our study, and their price dynamics do provide greater flexibility in energy policy implementation and help counter energy market monopolization [1][2][3][4][5][6][7][8][9]. As biomass markets are less developed than traditional fossil fuel markets, being smaller and having lower market liquidity than the traditional crude oil market for instance, they can be more susceptible to downward trends in the traditional and long-established energy markets. ...
... On the one hand, the GARCH model could not capture the asymmetry of volatility. Conrad et al. [16] used the asymmetric GARCH-type models to capture the dynamic characteristics of the EUAF's volatility, and demonstrated that the EUAF's volatility exists as asymmetric characteristic (leverage effect). Dutta et al. [17] explored the relationship of EUAF and the implied volatility of crude oil by using the EGARCH model, which contains a dynamic jump component. ...
Article
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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.
... In addition, Conrad et al. (2012) ...
Preprint
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Price formation in the EU Emission Trading System (EU ETS) has persistently puzzled economists and policy makers. In recent years, the empirical literature investigating this topic has expanded considerably, but a synthesis of what could be learned about price formation as a whole including the last wave of research is still missing. To fill this gap, we review the empirical literature structured along three categories of price drivers and related econometric methods. For better guidance of the reader, we draw on a simple theoretical model of price formation that we subsequently extend to connect the three different strands of literature: demand-side fundamentals, regulatory intervention and finance. In particular the insights from the second and third strand challenge the widespread view that allowance markets primarily reflect marginal abatement costs. Accordingly, the next wave of research should focus on shedding light on the complex interplay of compliance, regulatory uncertainty and financial trading motives. JEL classifications: Q48, Q50, Q56, Q58
... After the EU ETS system began operations, researchers were in the position to use actual EUA prices for modeling purposes. The results have shown that models that capture asymmetries, excess kurtosis, or fat tails, and that consider different phases of carbon prices' return volatility, are better suited for the modeling of carbon prices (see, in this regard, [30][31][32][33][34][35][36]. Benz and Trück [30] focused on the short-term EUA price behavior in the EU, given its importance for determining the production costs of companies. ...
Article
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The latest European Union measures for combating climate adopted in the “Fit for 55 package” envisage the extension of the Emissions Trading System, the first “cap-and-trade” system in the world created for achieving climate targets, which limits the amount of greenhouse gas emissions by imposing a price on carbon. In this context, our study provides an integrated assessment of carbon price risk exposure of all economic sectors in the European Union Member States, thus supporting decision making in determining the energy transition risk. We propose a novel approach in assessing carbon risk exposure using the Value at Risk methodology to compute the carbon price under the EU ETS, based on historical price simulation for January–August 2021 and ARMA-GARCH models for the October 2012–August 2021 period. We further built a value erosion metric, which allowed us to establish each sector’s exposure to risk and to identify differences between Eastern and Western EU countries. We find that the refining sector appears to be highly vulnerable, whereas there is higher potential for large losses in the energy supply and chemical sectors in Eastern EU Member States, given a different pace of industry restructuring.
... The ARCH and Generalized ARCH (GARCH) models have proved very useful for financial time series analysis. Thus, they have been also used to forecast CO 2 allowance prices, sometimes without any other tool [16], where a modification of its basic structure (fractionally integrated asymmetric power GARCH) is used, integrated into another forecasting model such as Markov chains [4,13] or by forming a hybrid model with other forecasting tool such as ARIMA [15]. ...
Article
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European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.
... Further, biofuels can be used to manage and mitigate the adverse effect the price volatility of crude oil has on energy and non-energy portfolios, and to make the issue of energy security less uncertain in countries not endowed with natural resources for energy generation. Lastly, fossil fuel substitutes, such as those considered in our study, and their price dynamics do provide greater flexibility in energy policy implementation and help counter energy market monopolization [1][2][3][4][5][6][7][8][9]. As biomass markets are less developed than traditional fossil fuel markets, being smaller and having lower market liquidity than the traditional crude oil market for instance, they can be more susceptible to downward trends in the traditional and long-established energy markets. ...
Article
We investigate the impact of geopolitical risk, U.S. economic policy uncertainty, financial stress, and market volatility on prices of U.S. and Brazilian ethanol and Malaysian palm oil. We use quantile autoregressive and quantile causality methods and provide evidence of ethanol and palm oil prices being asymmetrically influenced in the downside and upside by each of the uncertainty measures considered. Malaysian palm oil prices are more attuned to increases in uncertainty measures. Increases rather than decreases in uncertainty more strongly impact ethanol and palm oil prices. Uncertainty causes large negative price fluctuations in the biofuel commodities, while moderate uncertainty changes only moderately influence prices. Large uncertainty increases cause large or extreme positive changes in ethanol and palm oil prices. Implications of the results are discussed.
... Relationship between spot and futures prices returns from various financial investments financialization of the market, e.g., Nordpool and APX-UK spot prices Benz and Trück [4] Lovcha et al. [35] Gorenflo [52] Dasgupta et al. [53] Niblock and Harrison [54] Ozturk and Acaravci [55] Shahbaz et al. [56] Zhang [57] Institutional issues (policy and regulatory issues) State subsidy schemes for power-law system regulations, transaction costs, certified emission reduction, emission of CO2 rate, uncertainties in international agreements, market stability reserve, decisions of the European Commission (e.g., on National Allocation Plans), explicit trading rules (e.g., intertemporal trading), the linkage of the EU ETS with the market of project-based mechanisms Ellerman et al. [26] Chung et al. [38] Seifert et al. [21] Krawiec [36] Boersen and Scholtens [24] Conrad et al. [58] Benz and Trück [4] De Perthuis and Trotignon [59] Kim et al. [60] Weather Atmospheric conditions: Temperature, precipitation, windiness (wind force) Rickels et al. [33] Alberola et al. [34] Hintermann [52] Seifert et al. [21] Source: Own study. ...
Article
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The aim of this article was to identify challenges of emissions trading that the Polish and CEE Central and Eastern Europe energy industry will face, as well as to indicate key implications for the competitiveness of the companies from the energy sector resulting from that trading. The EU Emissions Trading Scheme (ETS) is the emissions trading system, which results from the EU policy concerning climate change. It is a tool for reducing greenhouse gas emissions (GHG). The system regulates an annual allocation of the allowances. The price of CO2 emission allowances is subject to constant fluctuations because it depends on various macroeconomic factors as well as is an effect of proprietary trading by global investment banks. Polish energy companies have an increasing share in the emission of CO2 in the European market. This is due to the fact that other European countries are rapidly moving away from fossil fuel-fired sources. The cost per MWh related to CO2 price has been growing in the last 10 years from ca. 5 up to 30 EUR/MWh at the beginning of 2021. From an electric power utilities perspective, the ability to set up a proper strategy in trading CO2 will be crucial to be competitive in the wholesale power market. The higher price of CO2 (and electric power) at the domestic market in relation to more green (more renewable energy sources RES in energy mix) surrounding countries translates into a worse competitive position.
... However, much longer data series are sometimes analyzed. For 339 example, in recent empirical work, models of the ARCH(∞) class have been fitted to samples of size 100,032 (Han, 2008), 509,472 (Chortareas et al., 2011), 55,860 (Conrad et al., 2012), and 156,672 (Naeema et al., 2019). ...
Article
This paper provides an exact algorithm for efficient computation of the time series of conditional variances, and hence the likelihood function, of models that have an ARCH(∞) representation. This class of models includes, e.g., the fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model. Our algorithm is a variation of the fast fractional difference algorithm of Jensen, A.N. and M.Ø. Nielsen (2014), Journal of Time Series Analysis 35, 428–436. It takes advantage of the fast Fourier transform (FFT) to achieve an order of magnitude improvement in computational speed. The efficiency of the algorithm allows estimation (and simulation/bootstrapping) of ARCH(∞) models, even with very large data sets and without the truncation of the filter commonly applied in the literature. In Monte Carlo simulations, we show that the elimination of the truncation of the filter reduces the bias of the quasi‐maximum‐likelihood estimators and improves out‐of‐sample forecasting. Our results are illustrated in two empirical examples.
... Established in 2005, the European Union Emissions Trading Scheme (EU ETS) is the world's longest running carbon trading market whose main trading products are European Union allowance (EUA) spot and futures. The prices of the two are heavily influenced by the same factors, such as energy prices, social events, climate conditions, policy changes, and so on (Conrad et al. 2012;Liu et al. 2017a, b). Both fluctuate violently and can present a simultaneous rise and fall. ...
Article
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The spillover effects of European Union Allowances (EUA) spot and futures markets 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.
... Considering data availability, most research focuses on the EU ETS. Overall, the main research objects include the determinants of carbon prices (Alberola et al., 2008;Chevallier, 2009;Creti et al., 2011;Aatola et al., 2013), the volatility and risk measurement of carbon prices (Benz and Trück, 2009;Chevallier et al., 2011;Conrad et al., 2012), and the interaction among different carbon markets as well as the influence of carbon markets on other markets or economic variables (Blyth and Bunn, 2011;Aboura and Chevallier, 2014;Chevallier, 2009;Zhang and Wei, 2010b). ...
... However, there are several ways to address the issue for the intraday periodicity (see e.g., Martens et al., 2002). Following Conrad et al. (2012), we employ a simple but very effective method is to remove the intraday periodicity by standardizing r t,k according to the following rule: ...
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... Lepone, Rahman, and Yang (2011) found that national allocation plans (NAP) and verification announcements related to phase II had a significant impact on EUA returns but no impact on volatility. Conrad, Rittler, and Rotfuß (2012) demonstrated that the release of the economic indicators of Germany and the United States had a strong and immediate impact on EUA prices. Mizrach and Otsubo (2014) showed that the events of verified emissions release caused a decline in realized volatility, bid-ask spreads, and adverse selection costs of EUA prices in the European Climate Exchange (ECX). ...
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The European Union Emissions Trading Scheme (EU ETS) is supposed to be an important mechanism for addressing climate change. Up to now, the theoretical foundation of EU ETS has been widely acknowledged, but empirical research on its current situation has only been published recently or is forthcoming. Therefore, this paper is aimed to summarize the main arguments of empirical studies on the EU ETS, in terms of two aspects, i.e., the operating mechanism and economic effect of the EU ETS, which are two crucial topics and have been attached much attention. Based on the shortcomings of current research and future requirements of the EU ETS evolution, finally, we also present some further directions of the EU ETS research. Overall, the research overview here may be helpful to recognize the features of the EU ETS and its effect on others.
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This paper studies the three main markets for emission allowances within the European Union Emissions Trading Scheme (EU ETS): Powernext, Nord Pool and European Climate Exchange (ECX). The analysis suggests that the prohibition of banking of emission allowances between distinct phases of the EU ETS has significant implications in terms of futures pricing. Motivated by these findings, we develop an empirically and theoretically valid framework for the pricing and hedging of intra-phase and inter-phase futures and options on futures, respectively.
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Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH specification of Ding, Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight countries. We find this multivariate specification to be generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, we find that both the optimal fractional differencing parameter and power transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the multivariate FIAPARCH framework is provided in terms of forecast error statistics and tests for equal forecast accuracy of the various models.
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In this paper we analyze the short-term spot price behavior of carbon dioxide (CO2) emission allowances of the new EU-wide CO2 emissions trading system (EU ETS). After reviewing the stylized facts of this new class of assets we investigate several approaches for modeling the returns of emission allowances. Due to different phases of price and volatility behavior in the returns, we suggest the use of Markov switching and AR–GARCH models for stochastic modeling. We examine the approaches by conducting an in-sample and out-of-sample forecasting analysis and by comparing the results to alternative approaches. Our findings strongly support the adequacy of the models capturing characteristics like skewness, excess kurtosis and in particular different phases of volatility behavior in the returns.
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Ding et al. (1993) [Ding, Z., Granger, C.W.J., Engle, R.F., 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance1, 83–106] suggested a model which extends the ARCH family of models for analyzing a wider class of power transformations than simply taking the absolute value or squaring the data as in the conventional conditional heteroscedastic models. This paper analyzes the applicability of these power ARCH (PARCH) models to national stock market returns for 10 countries plus a world index. We find the PARCH model to be generally applicable once GARCH and leverage effects are taken into consideration. In addition, we also find that the optimal power transformation is remarkably similar across countries.
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In this article we derive conditions which ensure the non-negativity of the conditional variance in the Hyperbolic GARCH(p,d,q) (HYGARCH) model of Davidson (2004). The conditions are necessary and sufficient for p=1 and sufficient for p≥2 and emerge as natural extensions of the inequality constraints derived in Nelson and Cao (1992) and Tsai and Chan (2008) for the GARCH model and in Conrad and Haag (2006) for the FIGARCH model. As a by-product we obtain a representation of the ARCH(∞) coefficients which allows computationally efficient multi-step-ahead forecasting of the conditional variance of a HYGARCH process. We also relate the necessary and sufficient parameter set of the HYGARCH to the necessary and sufficient parameter sets of its GARCH and FIGARCH components. Finally, we analyze the effects of erroneously fitting a FIGARCH model to a data sample which was truly generated by a HYGARCH process. Empirical applications of the HYGARCH(1,d,1) model to daily NYSE and DAX30 data illustrate the importance of our results.
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This article investigates the efficacy of a volatility model for three crude oil markets — Brent, Dubai, and West Texas Intermediate (WTI) — with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices.
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This paper provides an introduction to alternative models of uncertain commodity prices. A model of commodity price movements is the engine around which any valuation methodology for commodity production projects is built, whether discounted cash flow (DCF) models or the recently developed modern asset pricing (MAP) methods. The accuracy of the valuation is in part dependent on the quality of the engine employed. This paper provides an overview of several basic commodity price models and explains the essential differences among them. We also show how futures prices can be used to discriminate among the models and to estimate better key parameters of the model chosen.
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In this article we derive necessary and sufficient conditions for the nonnegativity of the conditional variance in the fractionally integrated generalized autoregressive conditional heteroskedastic (p, d, q) (FIGARCH) model of the order p ≤ 2 and sufficient conditions for the general model. These conditions can be seen as being analogous to those derived by Nelson and Cao (1992, Journal of Business & Economic Statistics 10, 229--235) for the GARCH(p, q) model. However, the inequality constraints which we derive for the FIGARCH model illustrate two remarkable properties of the FIGARCH model which are in contrast to the GARCH model: (i) even if all parameters are nonnegative, the conditional variance can become negative and (ii) even if all parameters are negative (apart from d), the conditional variance can be nonnegative almost surely. In particular, the conditions for the (1, d, 1) model substantially enlarge the sufficient parameter set provided by Bollerslev and Mikkelsen (1996, Journal of Econometrics 73, 151--184). The importance of the result is illustrated in an empirical application of the FIGARCH(1, d, 1) model to Japanese yen versus U.S. dollar exchange rate data. Copyright 2006, Oxford University Press.
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This paper calculates indices of central bank autonomy (CBA) for 163 central banks as of end-2003, and comparable indices for a subgroup of 68 central banks as of the end of the 1980s. The results confirm strong improvements in both economic and political CBA over the past couple of decades, although more progress is needed to boost political autonomy of the central banks in emerging market and developing countries. Our analysis confirms that greater CBA has on average helped to maintain low inflation levels. The paper identifies four broad principles of CBA that have been shared by the majority of countries. Significant differences exist in the area of banking supervision where many central banks have retained a key role. Finally, we discuss the sequencing of reforms to separate the conduct of monetary and fiscal policies. IMF Staff Papers (2009) 56, 263–296. doi:10.1057/imfsp.2008.25; published online 23 September 2008
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This paper examines the conditional heteroscedasticity of the yen-dollar exchange rate. A model is constructed by extending the asymmetric power autoregressive conditional heteroscedasticity model to a process that is fractionally integrated. It is found that, unlike the equity markets, the appreciation and depreciation shocks of the yen against the dollar have similar effects on future volatilities. Although the results reject both the stable and the integrated models, our analysis of the response coefficients of the past shocks and the application of the models to the estimation of the capital requirements for trading the currencies show that there are no substantial differences between the fractionally integrated models and the stable models. © 1998 John Wiley & Sons, Ltd.
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This paper provides a detailed characterization of the volatility in the deutsche mark-dollar foreign exchange market using an annual sample of five-minute returns. The approach captures the intraday activity patterns, the macroeconomic announcements, and the volatility persistence (ARCH) known from daily returns. The different features are separately quantified and shown to account for a substantial fraction of return variability, both at the intraday and daily level. The implications of the results for the interpretation of the fundamental "driving forces" behind the volatility process is also discussed. Copyright The American Finance Association 1998.
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This article aims at characterizing the daily price fundamentals of European Union Allowances (EUAs) traded since 2005 as part of the Emissions Trading Scheme (ETS). The presence of two structural changes on April 2006 following the disclosure of 2005 verified emissions and on October 2006 following the European Commission announcement of stricter Phase II allocation allows to isolate distinct fundamentals evolving overtime. The results extend previous literature by showing that EUA spot prices react not only to energy prices with forecast errors, but also to unanticipated temperatures changes during colder events. Besides, the sub-period decomposition of the pilot phase gives a better grasp of institutional and market events that drive allowance price changes.
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A ‘long memory’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return ¦rt¦d also has quite high autocorrelation for long lags. It is possible to characterize ¦rt¦d to be ‘long memory’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those based on absolute return can produce this property. A new general class of models is proposed which allows the power δ of the heteroskedasticity equation to be estimated from the data.
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This paper discusses and documents G@RCH 2.2, an Ox package dedicated to the estimation and forecast of various univariate ARCH–type models including GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, HYGARCH, FIEGARCH and FIAPARCH specifications of the conditional variance and an AR(FI)MA specification of the conditional mean. These models can be estimated by Approximate (Quasi) Maximum Likelihood under four assumptions: normal, Student–t, GED or skewed Student errors. Explanatory variables can enter both the conditional mean and the conditional variance equations. h–step–ahead forecasts of both the conditional mean and the conditional variance are available as well as many mispecification tests. We first propose an overview of the package’s features, with the presentation of the different specifications of the conditional mean and conditional variance. Then further explanations are given about the estimation methods. Measures of the accuracy of the procedures are also given and the GARCH features provided by G@RCH are compared with those of nine other econometric softwares. Finally, a concrete application of G@RCH 2.2 is provided.
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A test for long-term memory that is robust to short-range dependence is developed. It is a modification of the R/S statistic, and the relevant asymptotic sampling theory is derived via functional central limit theory. Contrary to previous findings, when applied to daily and monthly stock returns indexes over several time periods this test yields no evidence of long-range dependence once short-range dependence is accounted for. Monte Carlo experiments show that the modified R/S test has power against at least two specific models of long-term memory, suggesting that models with short-range dependence may adequately capture the behavior of historical stock returns. Copyright 1991 by The Econometric Society.
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Using a new dataset consisting of six years of real-time exchange rate quotations, macroeconomic expectations, and macroeconomic realizations (announcements), we characterize the conditional means of U.S. dollar spot exchange rates versus German Mark, British Pound, Japanese Yen, Swiss Franc, and the Euro. In particular, we find that announcement surprises (that is, divergences between expectations and realizations, or "news") produce conditional mean jumps; hence high-frequency exchange rate dynamics are linked to fundamentals. The details of the linkage are intriguing and include announcement timing and sign effects. The sign effect refers to the fact that the market reacts to news in an asymmetric fashion: bad news has greater impact than good news, which we relate to recent theoretical work on information processing and price discovery. Key Words: Exchange Rates; Macroeconomic News Announcements; Jumps; Market Microstructure; High-Frequency Data; Expectations Data; Anticipations Data; Order Flow; Asset Return Volatility; Forecasting.