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Stochastic volatility of the futures prices of emission allowances: A Bayesian approach

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  • Kim, Jungmu
  • Park, Yuen Jung
  • Ryu, Doojin

Abstract

Understanding the stochastic nature of the spot volatility of emission allowances is crucial for risk management in emissions markets. In this study, by adopting a stochastic volatility model with or without jumps to represent the dynamics of European Union Allowances (EUA) futures prices, we estimate the daily volatilities and model parameters by using the Markov Chain Monte Carlo method for stochastic volatility (SV), stochastic volatility with return jumps (SVJ) and stochastic volatility with correlated jumps (SVCJ) models. Our empirical results reveal three important features of emissions markets. First, the data presented herein suggest that EUA futures prices exhibit significant stochastic volatility. Second, the leverage effect is noticeable regardless of whether or not jumps are included. Third, the inclusion of jumps has a significant impact on the estimation of the volatility dynamics. Finally, the market becomes very volatile and large jumps occur at the beginning of a new phase. These findings are important for policy makers and regulators.

Suggested Citation

  • Kim, Jungmu & Park, Yuen Jung & Ryu, Doojin, 2017. "Stochastic volatility of the futures prices of emission allowances: A Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 714-724.
  • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:714-724
    DOI: 10.1016/j.physa.2016.08.036
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    5. Pan, Di & Zhang, Chen & Zhu, Dandan & Ji, Yuanpu & Cao, Wei, 2022. "A novel method of detecting carbon asset price jump characteristics based on significant information shocks," Finance Research Letters, Elsevier, vol. 47(PA).
    6. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    7. Park, Sung Y. & Ryu, Doojin & Song, Jeongseok, 2017. "The dynamic conditional relationship between stock market returns and implied volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 638-648.
    8. Wugan Cai & Jiafeng Pan, 2017. "Stochastic Differential Equation Models for the Price of European CO 2 Emissions Allowances," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    9. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    10. Hao Chen & Zhixin Liu & Yinpeng Zhang & You Wu, 2020. "The Linkages of Carbon Spot-Futures: Evidence from EU-ETS in the Third Phase," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    11. Daehyeon Park & Jiyeon Park & Doojin Ryu, 2020. "Volatility Spillovers between Equity and Green Bond Markets," Sustainability, MDPI, vol. 12(9), pages 1-12, May.

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