Forecasting carbon futures volatility using GARCH models with energy volatilities

Byun, Suk Joon; Cho, Hangjun

doi:10.1016/j.eneco.2013.06.017

Cited by 242 publications

(95 citation statements)

References 40 publications

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“…However, many researchers have highlighted that a few extreme observations may have an excessively large impact on the outcomes of forecast evaluation and comparison tests and have suggested to use loss functions that are less sensitive to large observations (Bollerslev and Ghysels, 1994;Andersen et al, 1999;Poon and Granger, 2003). Therefore, instead of using one particular statistical loss function, we here adopt different statistical loss functions, namely Root Mean Square Error (RMSE), RMSE-LOG, Mean Absolute Error (MAE), MAE-LOG and QLIKE (Patton, 2011;Byun and Cho, 2013). With the forecasted volatility and the actual volatility at hand, RMSE, RMSE-LOG, MAE, MAE-LOG and QLIKE are defined as:…”

Section: Forecast Evaluationmentioning

confidence: 99%

“…The daily volatility of stock price is calculated by summing squared intraday returns. As a model-free estimator, realized volatility has often been used as an ex post proxy to evaluate the volatility forecast models in financial and energy markets (Marcucci, 2005;Brownlees et al, 2011;Byun and Cho, 2013). To our knowledge, realized volatility has not been exploited in the wind power production analysis so far.…”

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confidence: 99%

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Forecasting volatility of wind power production

Shen

Ritter

2016

Applied Energy

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Abstract:The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. The realized volatility, which is derived from lower-scale data, serves as a benchmark for the latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models.

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Section: Forecast Evaluationmentioning

confidence: 99%

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confidence: 99%

Forecasting volatility of wind power production

Shen

Ritter

2016

Applied Energy

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“…It was found that the prices of carbon futures did not respond to macroeconomic changes immediately, but were significantly affected by the quota allocation and power demand (Julien, 2009). Byun and Cho (2013) investigated the abilities of three methods to predict carbon price fluctuation: Generalized Auto Regressive Conditional Heteroskedasticity (GARCH); hidden fluctuation rates of carbon futures prices and option prices; K nearest neighbor (Byun, Cho, 2013). Carbon option did not show any information spillover effect on carbon futures, because of too small trading volume (Byun, Cho, 2013).…”

Section: 21worldwide Research On Carbon Futures Marketsmentioning

confidence: 99%

“…Byun and Cho (2013) investigated the abilities of three methods to predict carbon price fluctuation: Generalized Auto Regressive Conditional Heteroskedasticity (GARCH); hidden fluctuation rates of carbon futures prices and option prices; K nearest neighbor (Byun, Cho, 2013). Carbon option did not show any information spillover effect on carbon futures, because of too small trading volume (Byun, Cho, 2013). They also studied the effects of energy market fluctuation on the carbon futures market and found the fluctuation of carbon futures could be predicted by the crude oil, coal and power market prices (Byun, Cho, 2013).…”

Section: 21worldwide Research On Carbon Futures Marketsmentioning

confidence: 99%

Dynamic Simulation Analysis on EUA and CER Futures Prices at Two Phases of European Union Emission Trading Scheme

2016

Rev. Téc. Ing. Univ. Zulia

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Based on data from European Union Emission Trading Scheme (EU ETS) 2nd phase, 3rd phase, and outlier-free 3rd phase, we used Johansen test, vector error correction, pulse response function and variance decomposition to empirically compare the long-term equilibrium relationships as well as interactions of futures prices of two major EU ETS commodities: European Union Allowance (EUA) and Certified Emission Reduction (CER). We find long-term stable positive equilibrium relationship for EUA and CER futures prices at the 2nd phase, but at the 3rd phase, co-integration of full-sample data disappears, while a positive equilibrium relationship occurs after outlier exclusion, though weaker compared with the 2nd phase. The EUA futures prices at the 3rd phase after outlier exclusion are more sensitive to market changes, but due to the complication of uncertainty factors at the 3rd phase, the correction direction of CER futures prices is changed. EUA versus CER futures prices do not show strong linkage at the 2nd phase, and the guiding role of a certain price disappears.

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“…After an extensive review of the extant literature, we found that in recent years great research efforts have been expended in two areas: (1) understanding the underlying mechanisms that determine carbon futures prices [1][2][3] and (2) the development of various models suitable for forecasting carbon futures prices [4][5][6][7][8][9][10][11][12][13][14][15]. A slight significant progress in forecasting carbon futures prices is notable.…”

Section: Introductionmentioning

confidence: 99%