Advertisement

Advanced Topics: Time-to-Maturity and Modeling the Volatility of Carbon Prices

  • Julien Chevallier

Abstract

This chapter examines the maturity effects of the CO2 futures contracts traded on the European Climate Exchange, in conjunction with the CO2 spot prices traded on the BlueNext market. It investigates the ‘Samuelson hypothesis’ that the volatility of futures price changes increases as a contract’s delivery date nears. While the literature on commodities usually finds strong empirical support of this hypothesis (in agricultural markets for instance), this chapter provides a weak support for the CO2 market. Volatility is found to increase near the expiration of the contract only with realized volatility measures (constructed as the sum of intraday squared returns). The net cost-of-carry and GARCH modeling approaches fail to detect such time-to-maturity effects. This chapter illustrates the superiority of realized volatility in carbon pricing, as the data is observed and modeled at the highest possible frequency. An Appendix completes this chapter by dealing with statistical tests to detect instability in the volatility of carbon prices.

Keywords

Price Volatility Future Price GARCH Modeling Future Contract Spot Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Adrangi B, Chatrath A (2003) Non-linear dynamics in futures prices: evidence from the coffee, sugar and cocoa exchange. Appl Financ Econ 13:245–256 CrossRefGoogle Scholar
  2. 2.
    Alberola E, Chevallier J (2009) European carbon prices and banking restrictions: evidence from Phase I (2005–2007). Energy J 30:51–80 Google Scholar
  3. 3.
    Andersen TG, Bollerslev T, Diebold FX, Ebens H (2001) The distribution of stock return volatility. J Financ Econ 61:43–76 CrossRefGoogle Scholar
  4. 4.
    Andersen TG, Bollerslev T, Diebold FX, Labys P (2003) Modeling and forecasting realized volatility. Econometrica 71:579–625 CrossRefGoogle Scholar
  5. 5.
    Anderson RW, Danthine J (1983) The time pattern of hedging and the volatility of futures prices. Rev Econ Stud 50:249–266 CrossRefGoogle Scholar
  6. 6.
    Andrews DWK (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61:821–856 CrossRefGoogle Scholar
  7. 7.
    Andrews DWK, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62:1383–1414 CrossRefGoogle Scholar
  8. 8.
    Barndorff-Nielsen O, Shephard N (2002) Econometric analysis of realized volatility and its use in estimating stochastic volatility models. J R Stat Soc Ser B 64:253–280 CrossRefGoogle Scholar
  9. 9.
    Benz E, Trück S (2009) Modeling the price dynamics of CO2 emission allowances. Energy Econ 31:4–15 CrossRefGoogle Scholar
  10. 10.
    Bessembinder H, Coughenour JF, Seguin PJ, Smoller MM (1995) Mean reversion in equilibrium asset prices: evidence from the futures term structure. J Finance 50:361–375 CrossRefGoogle Scholar
  11. 11.
    Bessembinder H, Coughenour JF, Seguin PJ, Smoller MM (1996) Is there a term structure of future volatilities? Reevaluating the Samuelson hypothesis. J Deriv 4:45–58 CrossRefGoogle Scholar
  12. 12.
    Berndt E, Hall B, Hall R, Hausman J (1974) Estimation and inference in nonlinear structural models. Ann Econ Soc Meas 3:653–665 Google Scholar
  13. 13.
    Board J, Sutcliffe C (1990) Information volatility, volume, and maturity: an investigation of stock index futures. Rev Futures Mark 9:532–549 Google Scholar
  14. 14.
    Brennan M (1958) The supply of storage. Am Econ Rev 48:50–72 Google Scholar
  15. 15.
    Chen YJ, Duan JC, Hung MW (1999) Volatility and maturity effects in the Nikkei Index Futures. J Futures Mark 19:895–909 CrossRefGoogle Scholar
  16. 16.
    Chevallier J (2009) Carbon futures and macroeconomic risk factors: a view from the EU ETS. Energy Econ 31:614–625 CrossRefGoogle Scholar
  17. 17.
    Chevallier J (2011) Detecting instability in the volatility of carbon prices. Energy Econ 33:99–110 CrossRefGoogle Scholar
  18. 18.
    Chevallier J, Ielpo F, Mercier L (2009) Risk aversion and institutional information disclosure on the European carbon market: a case-study of the 2006 compliance event. Energy Policy 37:15–28 CrossRefGoogle Scholar
  19. 19.
    Chevallier J, Sévi B (2010) Jump-robust estimation of realized volatility in the EU emissions trading scheme. J Energy Markets 3:49–67 Google Scholar
  20. 20.
    Chevallier J, Sévi B (2011) On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics, and forecasting. Ann Finance 7:1–29 CrossRefGoogle Scholar
  21. 21.
    Chu CSJ, Stinchcombe M, White H (1996) Monitoring structural change. Econometrica 64:1045–1065 CrossRefGoogle Scholar
  22. 22.
    Clark PK (1973) A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41:135–155 CrossRefGoogle Scholar
  23. 23.
    Daskalakis G, Psychoyios D, Markellos RN (2009) Modelling CO2 emission allowance prices and derivatives: evidence from the European trading scheme. J Bank Finance 33:1230–1241 CrossRefGoogle Scholar
  24. 24.
    Duong HN, Kalev PS (2008) The Samuelson hypothesis in futures markets: an analysis using intraday data. J Bank Finance 32:489–500 CrossRefGoogle Scholar
  25. 25.
    Haff I, Lindqvist O, Leiland A (2008) Risk premium in the UK natural gas forward market. Energy Econ 30:2420–2440 CrossRefGoogle Scholar
  26. 26.
    Kalev PS, Duong HN (2008) A test of the Samuelson hypothesis using realized range. J Futures Mark 28:680–696 CrossRefGoogle Scholar
  27. 27.
    Kolb R (1991) Understanding futures markets. Kolb Publishing Company, Miami Google Scholar
  28. 28.
    Leisch F, Hornik K, Kuan CM (2000) Monitoring structural changes with the generalized fluctuation test. Econom Theory 16:835–854 CrossRefGoogle Scholar
  29. 29.
    Martens M, van Dijk D (2007) Measuring volatility with the realized range. J Econom 138:181–207 CrossRefGoogle Scholar
  30. 30.
    McMillan DG, Speight AEH (2004) Intra-day periodicity, temporal aggregation and time-to-maturity in FTSE-100 index futures volatility. Appl Financ Econ 14:253–263 CrossRefGoogle Scholar
  31. 31.
    Movassagh N, Modjtahedi B (2005) Bias and backwardation in natural gas futures prices. J Futures Mark 25:281–308 CrossRefGoogle Scholar
  32. 32.
    Mu X (2007) Weather, storage, and natural gas price dynamics: fundamentals and volatility. Energy Econ 29:46–63 CrossRefGoogle Scholar
  33. 33.
    Nelson DB (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59:347–370 CrossRefGoogle Scholar
  34. 34.
    Oberndorfer U (2009) EU emission allowances and the stock market: evidence from the electricity industry. Ecol Econ 68:1116–1126 CrossRefGoogle Scholar
  35. 35.
    Paolella MS, Taschini L (2008) An econometric analysis of emission allowance prices. J Bank Finance 32:2022–2032 CrossRefGoogle Scholar
  36. 36.
    Ploberger W, Kramer W (1992) The CUSUM test with OLS residuals. Econometrica 60:271–285 CrossRefGoogle Scholar
  37. 37.
    Samuelson PA (1965) Proof that properly anticipated prices fluctuate randomly. Ind Manage Rev 6:41–49 Google Scholar
  38. 38.
    Thomakos DD, Wang T (2003) Realized volatility in the futures markets. J Empir Finance 10:321–353 CrossRefGoogle Scholar
  39. 39.
    Working H (1949) The theory of the price of storage. Am Econ Rev 39:1254–1262 Google Scholar
  40. 40.
    Zeileis A, Kleiber C, Krämer W, Hornik K (2003) Testing and dating of structural changes in practice. Comput Stat Data Anal 44:109–123 CrossRefGoogle Scholar
  41. 41.
    Zeileis A (2006) Implementing a class of structural change tests: an econometric computing approach. Comput Stat Data Anal 50:2987–3008 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.CGEMP/LEDa Department of EconomicsUniversity Paris DauphineParisFrance

Personalised recommendations