Forecasting volatility of EUA futures: New evidence

Guo, Xiaozhu; Huang, Yisu; Liang, Chao; Umar, Muhammad

doi:10.1016/j.eneco.2022.106021

Cited by 15 publications

(7 citation statements)

References 58 publications

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“…The statistically significant forecasting power also holds in the long term and under alternative settings. This finding supplements the research fields on the energy‐carbon relationship and carbon volatility forecasting (see, e.g., Byun & Cho, 2013; Dutta, 2018; Guo et al, 2022; Zhang, Luo, et al, 2022). Second, we verify that the informative content in the spillover index can be utilized in practical portfolio constructions to earn higher economic gains than the benchmark and competing models.…”

Section: Introductionsupporting

confidence: 81%

“…Following the convention in the studies on volatility prediction (see, e.g., Gong & Lin, 2018b;Patton & Sheppard, 2009;Wang et al, 2016;, we employ the approach of MCS test proposed by Hansen et al (2011), to identify whether the model of interest has statistically different out-of-sample performance from others. The MCS is a subset of a few models with the best forecasting performance.…”

Section: Mcs Testmentioning

confidence: 99%

“…Specifically, the model with a larger p value is likely to have greater predictive accuracy. We set the confidence level as 75% and exclude the models with p values smaller than 0.25 from the MCS following Hansen et al (2011), Ma et al (2017, and Gong and Lin (2018a).…”

Section: Mcs Testmentioning

confidence: 99%

“…It is thereby essential to forecast and manage the volatility risk of carbon allowance prices for the purpose of stabilizing the carbon trading market and promoting the transition of corporates. Not surprisingly, a host of recent literature focuses on modeling and forecasting the carbon price volatility, which has attracted growing attention among investors, emission industries, and governments (see, e.g., Byun & Cho, 2013; Dutta, 2018; Guo et al, 2022; Ibrahim & Kalaitzoglou, 2016; Liu et al, 2021; Zhang, Luo, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

Zhang,

Wang

et al. 2024

Journal of Futures Markets

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In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in‐sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out‐of‐sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long‐term forecasting and does not derive from other carbon‐related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate.

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Section: Introductionsupporting

confidence: 81%

Section: Mcs Testmentioning

confidence: 99%

Section: Mcs Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

Zhang,

Wang

et al. 2024

Journal of Futures Markets

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“…Wu et al (2022) forecast the EUA futures volatility using EGARCH-MIDAS model, and show that the EUA futures volatility exhibits a leverage effect and the proposed EGARCH-MIDAS model outperforms the traditional competing models. Guo et al (2022) propose the GARCH-MIDAS-JUMP and GARCH-MIDAS-JUMP-LJ models for forecasting volatility of EUA futures, they find that both long-term and short-term asymmetries, extreme observations, and jump information have substantially effect on the EUA volatility.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the volatility of European Union allowance futures with time-varying higher moments and time-varying risk aversion

Mei

Ding

2022

Front. Environ. Sci.

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There is increasing evidence that European Union allowance (EUA) futures return distributions exhibit features of time-varying higher moments (skewness and kurtosis), which plays an important role in modeling and forecasting EUA futures volatility. Moreover, a number of studies have shown that time-varying risk aversion (RA) contains useful information for forecasting EUA futures volatility. In light of this, this paper proposes the GARCH-MIDAS with skewness and kurtosis (hereafter GARCH-MIDAS-SK) to empirically investigate the impact and predictive role of RA on EUA futures volatility. Our empirical results show that RA has a significantly negative impact on the long-term volatility of EUA futures. The EUA futures return distributions exhibit obvious features of time-varying higher moments. Incorporating RA and time-varying higher moments improves the in-sample fitting of the model. Furthermore, out-of-sample results suggest that incorporating RA and time-varying higher moments leads to significantly more accurate volatility forecasts. This finding is robust to alternative out-of-sample forecasting windows.

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Volatility of Volatility and VIX Forecasting: New Evidence Based on Jumps, the Short‐Term and Long‐Term Volatility

Qiao,

Cui,

Zhou

et al. 2024

Journal of Futures Markets

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This study explores VIX forecasting by proposing a novel model to characterize the volatility of volatility based on high‐frequency VIX. Specifically, the decomposed jumps, the short‐ and long‐term volatility of VIX realized volatility obtained through wavelet analysis are considered by integrating the HAR‐DJI‐GARCH with GARCH‐MIDAS model. Empirical results show superior performance over competing models, with enhanced predictive accuracy under four non‐parametric jumps. The model's effectiveness is further validated by adjusting prediction windows, wavelet levels, examining VIX term structure, varying the significance level of jump test, and through the assessment of its economic significance.

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Forecasting volatility of EUA futures: New evidence

Cited by 15 publications

References 58 publications

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

Forecasting the volatility of European Union allowance futures with time-varying higher moments and time-varying risk aversion

Volatility of Volatility and VIX Forecasting: New Evidence Based on Jumps, the Short‐Term and Long‐Term Volatility

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