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Detecting and modelling the jump risk of CO 2 emission allowances and their impact on the valuation of option on futures contracts

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  • Sharon S. Yang
  • Jr-Wei Huang
  • Chuang-Chang Chang

Abstract

Modelling CO 2 emission allowance prices is important for pricing CO 2 emission allowance linked assets in the emissions trading scheme (ETS). Some statistical properties of CO 2 emission allowance prices have been discovered in the literature ignoring price jumps. By employing real data from the ETS, this research first detects the jump risk using a jump test and then verifies jump effects in modelling CO 2 emission allowance prices by comparing the in-sample and out-of-sample model performance. We suggest a model which can capture the statistical properties of autocorrelation, volatility clustering and jump effects is more appropriate for modelling CO 2 emission allowance prices. We establish a general framework for pricing CO 2 emission allowance options on futures contracts with these properties and find that the jump risk significantly affects the value of the CO 2 emission allowance option on futures contracts. More importantly, we demonstrate that the dynamic jump ARMA--GARCH model can provide more accurate valuations of the CO 2 emission allowance options on futures than other models in terms of pricing error.

Suggested Citation

  • Sharon S. Yang & Jr-Wei Huang & Chuang-Chang Chang, 2016. "Detecting and modelling the jump risk of CO 2 emission allowances and their impact on the valuation of option on futures contracts," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 749-762, May.
    • Handle: RePEc:taf:quantf:v:16:y:2016:i:5:p:749-762
    DOI: 10.1080/14697688.2015.1059953
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    1. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    2. Daal, Elton & Naka, Atsuyuki & Yu, Jung-Suk, 2007. "Volatility clustering, leverage effects, and jump dynamics in the US and emerging Asian equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2751-2769, September.
    3. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
    4. Marc Chesney & Luca Taschini, 2012. "The Endogenous Price Dynamics of Emission Allowances and an Application to CO 2 Option Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(5), pages 447-475, November.
    5. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    6. Kara, M. & Syri, S. & Lehtila, A. & Helynen, S. & Kekkonen, V. & Ruska, M. & Forsstrom, J., 2008. "The impacts of EU CO2 emissions trading on electricity markets and electricity consumers in Finland," Energy Economics, Elsevier, vol. 30(2), pages 193-211, March.
    7. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    10. Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 377-389, July.
    11. Chen, Hua & Cox, Samuel H. & Wang, Shaun S., 2010. "Is the Home Equity Conversion Mortgage in the United States sustainable? Evidence from pricing mortgage insurance premiums and non-recourse provisions using the conditional Esscher transform," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 371-384, April.
    12. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    13. Philippe Jorion, 1988. "On Jump Processes in the Foreign Exchange and Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 427-445.
    14. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    15. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    16. John M. Maheu & Thomas H. McCurdy, 2004. "News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 755-793, April.
    17. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    18. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    19. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    20. Montagnoli, Alberto & de Vries, Frans P., 2010. "Carbon trading thickness and market efficiency," Energy Economics, Elsevier, vol. 32(6), pages 1331-1336, November.
    21. Daskalakis, George & Psychoyios, Dimitris & Markellos, Raphael N., 2009. "Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1230-1241, July.
    22. Bredin, Don & Muckley, Cal, 2011. "An emerging equilibrium in the EU emissions trading scheme," Energy Economics, Elsevier, vol. 33(2), pages 353-362, March.
    23. repec:dau:papers:123456789/2267 is not listed on IDEAS
    24. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

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    2. Tang, Ling & Wang, Haohan & Li, Ling & Yang, Kaitong & Mi, Zhifu, 2020. "Quantitative models in emission trading system research: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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