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An empirical investigation on the risk-return relationship of carbon future market

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Abstract

This paper examines the risk-return relationship for the carbon future market during Phases I, II and III of the European Union Emission Trading Scheme (EU ETS). The risk factors derived from the newly developed LSW model, are embedded into a GARCH framework. This new specification is compared with several GARCH-M type models analyzing the risk-return relationship in the carbon market. The results show that the new specification consistently achieves a good fit and possesses superior explanatory power for the European Union Allowance (EUA) data. Some policy suggestions regarding market efficiency are also provided.

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References

  1. Chevallier J, Ielpo F, and Mercier L, Risk aversion and institutional information disclosure on the European carbon market: A case-study of the 2006 compliance event, Energy Policy, 2009, 37: 15–28.

    Article  Google Scholar 

  2. Alberola E, Chevallier J, and Chèze B, Price drivers and structural breaks in European carbon prices 2005–2007, Energy Policy, 2008, 36: 787–797.

    Article  Google Scholar 

  3. Benz E and Trück S, CO2 emission allowances trading in Europe-specifying a new class of assets, Problems and Perspectives in Management, 2006, 3: 30–40.

    Google Scholar 

  4. Charles A, Darné O, and Fouilloux J, Testing the martingale difference hypothesis in CO2 emission allowances, Economic Modeling, 2011, 28: 27–35.

    Article  Google Scholar 

  5. Benz E and Trück S, Modeling the price dynamics of CO2 emission allowances, Energy Policy, 2009, 31: 4–15.

    Google Scholar 

  6. Fusaro P C, Energy and environmental hedge funds, Commodities Now, 2007, 11: 61–63.

    Google Scholar 

  7. Creti A, Jouvet P, and Mignon V, Carbon price drivers: Phase I versus Phase II equilibrium? Energy Economics, 2012, 34: 327–334.

    Article  Google Scholar 

  8. Chevallier J, Modelling risk premia in CO2 allowances spot and futures prices, Economic Modelling, 2010, 27: 717–729.

    Article  Google Scholar 

  9. Merton R C, An intertemporal asset pricing model, Econometrica, 1973, 41: 867–887.

    Article  MathSciNet  MATH  Google Scholar 

  10. Cifarelli G and Paladino G, Oil price dynamics and speculation, A multivariate financial approach, Energy Economics, 2010, 32: 363–372.

    Article  Google Scholar 

  11. Engle R, Lilien D, and Robins R, Estimating time-varying risk premia in the term structure: The ARCH-M model, Econometrica, 1987, 55: 391–407.

    Article  Google Scholar 

  12. Campbell J Y and Hentchel L, No news is good news: An asymmetric model of changing volatility in stock returns, Journal of Financial Economics, 1992, 31: 281–318.

    Article  Google Scholar 

  13. Glosten L R, Jagannathan R, and Runkle D E, On the relation between the expected value and the volatility of the nominal excess returns on stocks, Journal of Finance, 1993, 48: 1779–1801.

    Article  Google Scholar 

  14. Lee C F, Chen G, and Rui O, Stock returns and volatility on China’s stock markets, Journal of Financial Research, 2001, 26: 523–543.

    Article  Google Scholar 

  15. Lundblad C, The risk-return trade-off in the long run 1836–2003, Journal of Financial Economics, 2007, 85: 123–150.

    Article  Google Scholar 

  16. Ghysels E, Santa-Clara P, and Valkanov R, There is a risk-return trade-off after all, Journal of Financial Economics, 2005, 76: 509–548.

    Article  Google Scholar 

  17. Li Q, Yang J, Hsiao C, and Chang Y J, The relationship between stock returns and volatility in international stock markets, Journal of Empirical Finance, 2005, 12: 650–665.

    Article  Google Scholar 

  18. Chiang T C and Shuh-chyi D, Empirical analysis of stock returns and volatility: Evidence from seven Asian stock markets based on TAR-GARCH model, Review of Quantitative Finance and Accounting, 2001, 17: 301–318.

    Article  Google Scholar 

  19. Mohammadi H and Su L, International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models, Energy Economics, 2010, 32: 1001–1008.

    Article  Google Scholar 

  20. Chevallier J, Carbon futures and macroeconomic risk factors: A view from the EU ETS, Energy Economics, 2009, 31: 614–625.

    Article  Google Scholar 

  21. Conrad C, Rittler D, and Rotfuß W, Modeling and explaining the dynamics of European Union Allowance prices at high-frequency, Energy Economics, 2012, 34: 316–326.

    Article  Google Scholar 

  22. Paolella M and Taschini L, An econometric analysis of emission allowances prices, Journal of Banking and Finance, 2008, 32: 2022–2032.

    Article  Google Scholar 

  23. Berger D, Chaboud A, and Hjalmarsson E, What drives volatility persistence in the foreign exchange market? Journal of Financial Economics, 2009, 94: 192–213.

  24. Fleming J, Kirby C, and Ostdiek B, Stochastic volatility, trading volume, and the daily flow of information, Journal of Business, 2006, 79: 1551–1590.

    Article  Google Scholar 

  25. Andersen T G, Return volatility and trading volume: An information flow interpretation of stochastic volatility, Journal of Finance, 1996, 51: 169–204.

    Article  Google Scholar 

  26. Andersen T G, Bollerslev T, Diebold F X, and Vega C, Micro effects of macro announcements: Real-time price discovery in foreign exchange, American Economic Review, 2003, 93: 38–62.

    Article  Google Scholar 

  27. Rangel J G, Macroeconomic news, announcements, and stock market jump intensity dynamics, Journal of Banking and Finance, 2011, 35: 1263–1276.

    Article  Google Scholar 

  28. Flannery M J and Protopapadakis A A, Macroeconomic factors do influence aggregate stock returns, Review of Financial Studies, 2002, 15: 751–782.

    Article  Google Scholar 

  29. Foster A J, Volume-volatility relationships for crude oil futures markets, Journal of Futures Markets, 1995, 79: 1551–1590.

    Google Scholar 

  30. Moosa I A and Silvapulle P, The price-volume relationship in the crude oil futures market, International Review of Economics and Finance, 2000, 9: 11–30.

    Article  Google Scholar 

  31. Li Z R, Sun J J, and Wang S Y, An information diffusion-based model of oil futures price, Energy Economics, 2013, 36: 518–525.

    Article  Google Scholar 

  32. Hintermann B, Pricing emission permits in the absence of abatement, Energy Economics, 2012, 34: 1329–1340.

    Article  Google Scholar 

  33. Engle R and Lee G, A permanent and transitory model of stock return volatility, Eds. by Engle R and White H, Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W J Granger, Oxford University Press, Oxford, UK, 1999, 475–497.

  34. Nelson D B, Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 1991, 59: 347–370.

    Article  MathSciNet  MATH  Google Scholar 

  35. French K R, Schwert G W, and Stambaugh R F, Expected stock returns and volatility, Journal of Financial Economics, 1987, 19: 3–30.

    Article  Google Scholar 

  36. Whitelaw R F, Stock market risk and return: An equilibrium approach, Review of Financial Studies, 2000, 13: 521–547.

    Article  Google Scholar 

  37. Rittler D, Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis, Journal of Banking and Finance, 2012, 36: 774–785.

    Article  Google Scholar 

  38. Bollerslev T and Wooldridge J M, Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric Reviews, 1992, 11: 143–172.

    Article  MathSciNet  MATH  Google Scholar 

  39. Chevallier J, Macroeconomics, finance, commodities: Interactions with carbon markets in a datarich model, Journal of Economic Modelling, 2011, 28(1): 557–567.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Collaborative Innovation Center for the Innovation and Regulation of Internet-Based Finance, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Ziran Li

  2. CFFEX Institute for Financial Derivatives, Beijing, 100033, China

    Ziran Li

  3. School of Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Han Qiao

  4. Directors’ Office, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Nan Song

  5. School of Management Sciences and Engineering, Central University of Finance and Economics, Beijing, 100081, China

    Lei Zu

Authors
  1. Ziran Li
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  2. Han Qiao
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  3. Nan Song
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  4. Lei Zu
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Corresponding author

Correspondence to Han Qiao.

Additional information

This research is supported by the National Natural Science Foundation of China under Grant Nos. 71003057, 71003094 and 71373262, and Shanghai Key Laboratory of Intelligent Information Processing under Grant No. IIPL-2014-001.

This paper was recommended for publication by Editor WANG Shouyang.

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Li, Z., Qiao, H., Song, N. et al. An empirical investigation on the risk-return relationship of carbon future market. J Syst Sci Complex 29, 1057–1070 (2016). https://doi.org/10.1007/s11424-015-4141-x

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  • DOI: https://doi.org/10.1007/s11424-015-4141-x

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