Carbon price volatility: Evidence from EU ETS

By Z. Feng, L. Zou, Yi-Ming Wei. 2011

226 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/6a853eff3a853f29605fef7305350ff284b5b03e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Yi-Ming Wei
2. Peng Zhou
3. Dequn Zhou
4. Bangzhu Zhu
5. Ping Wang
6. H. Cho
7. Suk Joon Byun
8. Zhanchi Wu

Citations in the corpus

• Forecasting carbon futures volatility using GARCH models with energy volatilities (2013, methodology): “Although Chevallier (2011d), Feng et al. (2011), and Chevallier and Sévi (2011) donot useGARCHmodels, instead using non-parametric modeling, nonlinear dynamics, and the HAR-RV model, respectively, several studies have employed GARCH-type models to estimate carbon volatility.”
• Modeling economic performance of interprovincial CO2 emission reduction quota trading in China (2013, background): “[5] examined the volatility of carbon prices in the EU ETS from a perspective of nonlinear dynamics.”
• How China's current carbon trading policy affects carbon price? An investigation of the Shanghai Emission Trading Scheme pilot (2018, background): “Prior studies on carbon market mainly focused on the determinant of carbon price (Kim and Koo, 2010; Koch, 2014), the relationship between CEA and carbon price (Feng et al, 2011), and the prediction for future carbon market scale (World Bank Group, 2016).”
• Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature (2014, background): “The partially stochastic behavior of allowance prices in the presence of volatility clustering has also been investigated and confirmed by Benz and 12 Trück (2009), Chevallier (2011), Feng et al. (2011), and Conrad et al. (2012).”
• Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression (2017, methodology): “Although the existing methods can obtain good 4 results when they are applied for stationary time series forecasting, they are not robust for forecasting accurately carbon 5 price due to its highly non-stationary and nonlinear characteristics [12].”