Hedging Method of Carbon Market Based on Fluctuation Characteristics of Carbon Price

Dandan Zhu

doi:10.18686/ahe.v9i8.14445

Hedging Method of Carbon Market Based on Fluctuation Characteristics of Carbon Price

Dandan Zhu

Abstract

This paper innovatively constructs a hedging model for carbon market based on Markov state transition Copula model(MRS-Copula). Firstly, considering that carbon spot’s return and carbon futures’ return have the characteristics of high peak, fat tail, skewness and multifractal, this study integrates the Skewed-t distribution which is capable of capturing peak, tail and skewness features into the Markov Regime Switching Multifractal Model(MSM). This combination is employed to fit the marginal distribution characteristics of carbon spot and futures returns. Secondly, we use MRS-Copula model which can depict time-varying correlation and state transition characteristics of correlation degree between return series to depict the correlation between carbon spot’s return and carbon futures’ return. Based on the above model, this paper calculates the optimal hedging ratio of EUA market. To test the hedging eff ectiveness of our model, we compare variance reduction rate of our model with that of MRS-Copula-SV-t, MRS-Copula-GARCH, Copula-MSM-Skewed-t, Copula-SV-t and Copula-GARCH. The empirical result shows that our model has better hedging effectiveness.

Keywords

Carbon market; Hedging; Multifractal; Skewness; State transition characteristics of correlation degree

Full Text:

PDF

Included Database

References

[1] Ivan Darma Wangsa, Iwan Vanany, Nurhadi Siswanto. An optimization model for fresh-food electronic commerce supply chain with carbon emissions and food waste[J]. Journal of Industrial and Production Engineering, 2022, 40(1):1-21.

[2] Sousa R, Aguiar-Conraria L. Energy and carbon prices: a comparison of interactions in the European Union Emissions Trading Scheme and the Western Climate Initiative market[J]. Carbon Management, 2015, 6(3-4):129-140.

[3] Koch N, Fuss S, Grosjean G, et al. Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of every_xfffe_thing?—New evidence[J]. Energy Policy, 2014, 73:676-685.

[4] Keppler J H, Mansanet-Bataller M. Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS[J]. Energy Policy, 2010, 38(7):3329-3341.

[5] Sousa R, Aguiar-Conraria, Luís, Soares M J. Carbon financial markets: A time–frequency analysis of CO2 prices[J]. Physica A: Statistical Mechanics and its Applications, 2014(15):118-127.

[6] BalciLar M, Demirer R, Hammoudeh S, et al. Risk Spillovers across the Energy and Carbon Markets and Hedging Strategies for Carbon Risk[J]. Energy Economics, 2016(54):159-172.

[7] Tu Q, Mo J L. Coordinating carbon pricing policy and renewable energy policy with a case study in China[J]. Computers & In_xfffe_dustrial Engineering, 2017(113):294-304.

[8] Blyth W, Bunn D. Coevolution of policy, market and technical price risks in the EU ETS[J]. Energy Policy, 2011, 39(8):4578-4593.

[9] Xie C et al. Research on optimal hedging ratios of gold market based on M-Copula-GJR-VaR model[J]. Journal of Management Science,2013,26(2):90-99.

[10] Xie C, Yu C, Luo C Q, et al. Hedging of stock index futures based on Markov regime-switching Copula-GJR-Skewed-t model[J]. Journal of Systems Engineering. 2013,28(01):83-93.

[11] Basher S A, Sadorsky P. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH[J]. Energy Economics, 2016, 54:235-247.

[12] Paolella M, Taschini L. An econometric analysis of emission trading allowances [J]. Swiss Finance Institute, 2006(3): 06-26.

[13] Yang X, Liang J L. Analysis and test of fractal and chaotic behavior characteristics of the international carbon emissions market[J]. Systems Engineering-Theory & Practice, 2017,37(06):1420-1431.

[14] Zhuang X Y, Wei Y, Zhang B Z. Multifractal detrended cross-correlation analysis of carbon and crude oil markets[J]. Physica A: Statistical Mechanics and its Applications, 2014, 399:113-125.

[15] Palao F, Pardo A. Assessing price clustering in European carbon markets [J].Applied Energy, 2012, 92(2):51-56.

[16] Feng J F, Xia C X, Wang C X. Carbon emission right price modeling and carbon bond valuation[J]. Journal of Hebei University of Economics and Business, 2018,39(01):66-72.

[17] Chevallier J. A note on cointegrating and vector autoregressive relationships between CO2 allowances spot and futures prices[J]. Economics bulletin, 2010,30:1564-1584.

[18] Zhang C, Liu Y J. Researon on information flow of EU ETS based on DGC-MSV-t model[J]. Soft Science, 2017,31(02):130-135.

[19] Philip D, Shi Y . Optimal hedging in carbon emission markets using Markov regime switching models[J]. Journal of International Financial Markets Institutions and Money, 2016, 43:1-15.

[20] Wu H Y, Hu G H. The Copula-GARCH model of dynamic dependencies and risk measure: an international carbon emission market application[J]. Journal of Applied Statistics and Management, 2014,33(05):892-909.

[21] He X B, Zhang J, Zeng S H. Dynamic hedging considering risk aversion and target return rate[J]. On Economic Problems, 2015(11):79-82.

[22] Chang K. Carbon emissions, dynamic hedging and return on assets risk[J]. Journal of Guizhou University of Finance and Economics,2013(02):21-27.

[23] Luo C Q, Xie C, Yu C, et al. Measuring financial market risk contagion using dynamic MRS-Copula models: The case of Chinese and other international stock markets[J]. Economic Modelling, 2015(51):657-671.

[24] Calvet L E, Fisher A J. How to forecast long-run volatility: Regime switching and the estimation of multifractal processes[J]. Journal of Financial Econometrics, 2004,2(1):49-83.

[25] Hansen B E. Autoregressive Conditional Density Estimation[J]. International Economic Review, 1994,35(3):705-730.

[26] Lambert P, Laurent S. Modelling finanial time series using GARCH-type models with a Skewed student distribution for the innovations[R]. Discussion paper 0125, Institutde Statistique, Universite Catholique de Louvain, Louvain-la-Neuve, Belgirm, 2001.

[27] Gray S F. Modeling the conditional distribution of interest rates as a regime-switching process[J]. Journal of Financial Economics, 1996,42(1):27-62.

[28] Kantelhardt J W , Zschiegner S A , Koscielny-Bunde E , et al. Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A: Statistical Mechanics and its Applications, 2002,316(1-4):87-114.

[29] Cao G , Xu W . Multifractal features of EUA and CER futures markets by using multifractal detrended fluctuation analysis based on empirical model decomposition[J]. Chaos, Solitons & Fractals, 2016, 83:212-222.

[30] Ederington L H . The Hedging Performance of the New Futures Markets[J]. Journal of Finance, 1979, 34(5):157-170.

[31] Zhang C, Yang Y, Zhang T, ea al. The Integrated Measurement about Carbon Finance Market Risk of Commercial Banks Based on Copula Model[J]. Chinese Journal of Management Science, 2015,23(04):61-69.

[32] Zhang C, Wu Y Q. Characteristics of post-Kyoto Era's CER price volatility based on Switching ASV Model[J]. China Population Resources & Environment, 2016,26(S2):16-19.

[33] Zhang Y J, Wei Y M. Interpreting the mean reversion of international carbon futures price:Empirical evidence from the EU ETS[J]. Systems Engineering-Theory & Practice,2011,31(02):214-220.

[34] Zhu D D, Zhang C, Pan D, et al. Multifractal cross-correlation analysis between carbon spot and futures markets considering asymmetric conduction effect[J]. Fractals,2021,7:1-18.

DOI: http://dx.doi.org/10.18686/ahe.v9i8.14445

Refbacks

There are currently no refbacks.