Time-varying correlations in oil, gas and CO<sub>2</sub>prices: an application using BEKK, CCC and DCC-MGARCH models

Chevallier, Julien

doi:10.1080/00036846.2011.589809

Cited by 82 publications

(43 citation statements)

References 22 publications

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“…It is the most popular extension of the CCC GARCH model to-date and is used in prior studies on correlation dynamics in the EU ETS by Koenig (2011) andChevallier (2012). The fundamental difference between the rather parsimonious DCC model and (D)STCC model class is that the former uses only past returns of series in order to model conditional correlations, while the latter makes use of exogenous transition variables.…”

Section: Model Comparisonmentioning

confidence: 98%

“…Finally, Koenig (2011) and Chevallier (2012) study the dynamics of correlations, using the Dynamic Conditional Correlation (DCC) GARCH model of Engle (2002), in order to evaluate carbon-energy market linkages. Koenig finds that pairwise correlations among EUAs, gas and electricity are not constant over time; Chevallier also finds time-varying EUA-oil and EUA-gas correlations.…”

Section: Related Literaturementioning

confidence: 99%

“…The way they incorporate institutional and macrofinancial changes of the EU ETS environment (samplesplitting and/or dummy variables) is mostly ad hoc (see also Alberola et al (2008) and ). However, in a related paper, Chevallier (2012) has shown that the correlation between carbon, oil and gas exhibits considerable time variation. My approach seeks to formulate a coherent model of the data-generating process that allows me to trace temporal patterns in price linkages and their economic sources.…”

Section: Introductionmentioning

confidence: 97%

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Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

Koch

2014

Applied Economics

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This article explores how price linkages between carbon allowances and market fundamentals in the EU Emissions Trading Scheme (EU ETS) vary over time. I adopt a multivariate GARCH model that allows the conditional correlation between carbon, energy and financial prices to change smoothly across regimes governed by functions of two transition variables that explain why price linkages vary. I use (i) time as transition variable to allow for structural changes associated with institutional advances in the EU ETS and (ii) implied volatility to account for heterogeneity in the behaviour of correlations in times of distress compared to calm periods. The results point to a new pricing regime with much closer carbonenergy price linkages in the second phase of the EU ETS. Furthermore, I find that correlations depend on market uncertainty conditions, which exposes the link between carbon and financial markets due to common macroeconomic shocks during the current financial crisis.

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Section: Model Comparisonmentioning

confidence: 98%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

Koch

2014

Applied Economics

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“…Energy market integration and comovements in energy prices play a central role in energy systemic risk as they neutralize the substitution effects that are supposed to bring energy prices to a new viable equilibrium for the rest of the economy. Our methodology therefore relates to the literature on comovements and the modeling of the joint distribution of energy prices (2): comovements in the mean with cointegration and causality models (Bunn and Fezzi (2008);Escribano et al (2011); Haldrup and Nielsen (2006)), comovements in the volatility with multivariate volatility models Chevallier (2012)) and comovements in the tails with copulae (Benth and Kettler (2010); Boerger et al (2009);Gronwald et al (2011)). Dependence in energy markets is complex.…”

Section: Systemic Risk Comovements and Energy Crisesmentioning

confidence: 99%

The Systemic Risk of Energy Markets

Pierret

2013

SSRN Journal

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This paper investigates the meaning of systemic risk in energy markets and proposes a methodology to measure it. Energy Systemic Risk is defined by the risk of an energy crisis raising the prices of all energy commodities with negative consequences for the real economy. Measures of the total cost (EnSysRISK) and the net impact (ΔMES) of an energy crisis on the rest of the economy are proposed. The measures are derived from the Marginal Expected Shortfall (MES) capturing the tail dependence between the asset and the energy market factor. The adapted MES accounts for causality and dynamic exposure to common latent factors. The methodology is applied to the European Energy Exchange and the DAX industrial index, where a minor decline in industrial productivity is observed from recent energy shocks. AbstractThis paper investigates the meaning of systemic risk in energy markets and proposes a methodology to measure it. Energy Systemic Risk is defined by the risk of an energy crisis raising the prices of all energy commodities with negative consequences for the real economy. Measures of the total cost (EnSysRISK) and the net impact (ΔMES) of an energy crisis on the rest of the economy are proposed. The measures are derived from the Marginal Expected Shortfall (MES) capturing the tail dependence between the asset and the energy market factor. The adapted MES accounts for causality and dynamic exposure to common latent factors. The methodology is applied to the European Energy Exchange and the DAX industrial index, where a minor decline in industrial productivity is observed from recent energy shocks.

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“…There has been considerable research regarding impact factors for carbon emission allowance prices since the emergence of CCX and EU-ETS. Numerous studies have identified that energy prices are the price determinants of allowances [8][9][10][11][12]. For example, Bunn and Fezzi [8] applied a cointegrated vector error correction model to highlight the interactions and dynamic pass-through among electricity, gas and carbon prices.…”

Section: Introductionmentioning

confidence: 99%

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

Zhang

2016

Sustainability

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A pilot regional carbon emission trading scheme (ETS) has been implemented in China for more than two years. An investigation into the impacts of different factors on carbon dioxide (CO 2 ) emission allowance prices provides guidance for price-making in 2017 when the nation-wide ETS of China will be established. This paper adopts a quantile regression approach to estimate the impacts of different factors in Shanghai emission trading scheme (SH-ETS), namely, economic growth, energy prices and temperature. The empirical analysis shows that: (i) the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii) the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS) or quantile regression method; (iii) a long-run negative relationship exists between the coal price and the Shanghai emission allowances (SHEA) prices, but a positive interaction under different quantiles, especially the 25%-50% quantiles; (iv) temperature has a significantly positive effect at the 20%-30% quantiles and a conspicuous negative impact at the right tail of the allowances prices.

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Time-varying correlations in oil, gas and CO₂prices: an application using BEKK, CCC and DCC-MGARCH models

Cited by 82 publications

References 22 publications

Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

The Systemic Risk of Energy Markets

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

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Time-varying correlations in oil, gas and CO2prices: an application using BEKK, CCC and DCC-MGARCH models

Cited by 82 publications

References 22 publications

Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

Dynamic linkages among carbon, energy and financial markets: a smooth transition approach

The Systemic Risk of Energy Markets

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

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Time-varying correlations in oil, gas and CO₂prices: an application using BEKK, CCC and DCC-MGARCH models