Forecasting volatility of carbon under EU ETS: a multi-phase study

Dhamija, Ajay Kumar; Yadav, Surendra S.; Jain, Pradeep

doi:10.1007/s10018-016-0155-4

Cited by 24 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such rich analyses add to previous studies modeling the volatility of carbon emissions with GARCH without accounting for structural breaks (e.g. Paolella and Taschini, 2008;Benz and Truck, 2009;Rittler, 2012;Spiesová, 2016;Dhamija et al, 2017).…”

Section: Introductionmentioning

confidence: 74%

Assessing the risk of the European Union carbon allowance market

Dutta

Jalkh

Bouri

et al. 2019

IJMF

View full text Add to dashboard Cite

Purpose The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices. Design/methodology/approach The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH. Findings The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated. Originality/value The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

show abstract

Section: Introductionmentioning

confidence: 74%

Assessing the risk of the European Union carbon allowance market

Dutta

Jalkh

Bouri

et al. 2019

IJMF

View full text Add to dashboard Cite

show abstract

“…Measuring volatility spillover between the carbon market and other financial market has emerged in recent researches (Mansanet-Bataller & Soriano, 2009;Reboredo, 2014;Dhamija, Yadav, & Jain, 2016;Zhang & Sun, 2016;Ji, Zhang, & Geng, 2018). From the financial perspective, if there is a risk transfer between different financial markets, i.e., volatility spillover effects, it can be considered that the activities on financial market are effective, rather than being intervened.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang and Sun (2016) explore the volatility spillover between the EU ETS and the fossil energy market using a threshold DCC. Dhamija, Yadav, and Jain (2016) study the volatility of EU ETS and energy prices, and they found that the asymmetric GARCH model is relatively optimal.…”

Section: Introductionmentioning

confidence: 99%

“…So far, EU ETS covers around 45% of total GHG from 31 countries (all 28 EU countries plus Iceland, Liechtenstein and Norway) and the emission of CO2 from power plants, a wide range of energy-intensive industry sectors and transportation sector. Since its commencement, a number of researches have begun to discuss the allocation process and the mechanism of the EU ETS (Ellerman, 2008;Ellerman, Convery, & De Perthuis, 2010;Parsons, Ellerman, & Feilhauer, 2009;Hu, Crijns-Graus, Long, & Gilbert, 2015;Dhamija, Yadav, & Jain, 2016;Jiménez-Rodríguez, 2019) and most studies takes EUA price as the carbon price.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the different operating time of the EU ETS, the economic conditions and trading rules faced by this scheme (EU ETS) in different periods are also diverse. Since many literatures have used the entire cycle or a certain phase of the EU ETS as a research sample (Alberola, Chevallier, & Chèze, 2008a, 2008bChevallier, 2011;Eugenia Sanin, Violante, & Mansanet-Bataller, 2015;Dhamija, Yadav, & Jain, 2016;Zhang & Sun, 2016;Wang & Guo, 2018), supplementing multi-phase research has practical significance. Secondly, the asymmetric BEKK model is a powerful weapon for the study of volatility spillovers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Volatility Spillover and Dynamic Correlation Between the Carbon Market and Energy Markets

Chen

et al. 2019

Journal of Business Economics and Management

View full text Add to dashboard Cite

This paper studies the volatility spillover and dynamic correlation between EU emission allowance (EUA) prices and energy prices by considering three energy commodities, including oil, gas, and coal. The asymmetric BEKK model is employed for multi-phase analysis of EU ETS, yet only a little empirical evidence backing up the existence of volatility spillover between EU ETS and energy markets, i.e., the establishments of the EU ETS may not effectively limitation and influence energy markets. The time-varying conditional correlation between EUA and each of energy prices is analyzed. The dynamic correlation shows there is a relatively stable, positive correlation between the EUA and Brent oil, natural gas. However, modeling the dynamics correlation also suggests that the correlation between the EUA and the natural gas, coal became weaker and more volatile since second and third phases, especially after the Global Financial Crisis in 2008, which may indicate that the demand reduction in emission allowances caused by the economic slowdown far exceeds the reduction in the annual restraint of EU ETS.

show abstract

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Zhang

2021

Journal of Forecasting

View full text Add to dashboard Cite

In an attempt to combat global warming, many countries have been introducing carbon trading schemes, which, in turn, has led to increased research interest in carbon price forecasting as accurate predictions can provide valuable decision references for governments and investors. Therefore, this paper proposes an optimal hybrid forecasting framework to ensure accurate long‐term carbon price series predictions for which empirical mode decomposition (EMD) was used to decompose the original data and a novel optimal combination developed to predict each decomposed part, which involved the integration of autoregressive moving average‐type (ARMA‐type) models and least squares support vector machines (LSSVMs) optimized by 10‐fold cross validation and particle swarm optimization (PSO) algorithm. The effectiveness of the optimal EMD‐ARMAs‐LSSVMs framework was then verified using an empirical example from the EU emissions trading system (EU ETS). The long‐term forecast results and comparisons with other forecasting methods demonstrated the stability and validity of the proposed framework. The results indicated that ARMA‐type models are more suitable for low frequency and trend components, and LSSVMs were more suitable for high frequency components. It was also found that the proposed optimal framework was able to effectively reduce the error accumulation.

show abstract

Forecasting volatility of carbon under EU ETS: a multi-phase study

Cited by 24 publications

References 24 publications

Assessing the risk of the European Union carbon allowance market

Assessing the risk of the European Union carbon allowance market

Volatility Spillover and Dynamic Correlation Between the Carbon Market and Energy Markets

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Contact Info

Product

Resources

About