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A Model for the Valuation of Carbon Price Risk

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Emissions Trading

Abstract

Modeling the price risk of CO2 emission allowances is an important aspect of integral corporate risk management related to emissions trading. In this paper, a pricing model is developed which may be the basis for evaluating the risk of emission certificate prices. We assume that the certificate price is determined by the expected marginal CO2 abatement costs in the current trade period as well as by the long-term marginal abatement costs. The price risk is modeled on the basis of a mean reversion process.

Due to uncertainties about the future state of the environment, we suppose that within one trade period erratic changes in the expected marginal abatement costs may occur leading to shifts in the price level. In addition to the parameter estimation, it is also an objective of this work to modify the mean reversion process so that such abrupt changes in the expected reversion level can be displayed. Because of the possibility of transferring spare allowances to a subsequent period we take into account the fact that the expected long run marginal abatement costs act as a lower limit for the price in the trading period.

We gratefully acknowledge funding of the project behind this article from the German Federal Ministry for Education and Research (BMBF). The responsibility for the content of this publication rests with the authors.

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Notes

  1. 1.

    Discounting is neglected here for reasons of better illustration.

  2. 2.

    Taking interest rates into account, the spot market price would be equal to the discounted future price.

  3. 3.

    Uhlenbeck and Ornstein (1930, p. 823ff.).

  4. 4.

    Blanco et al. (2001, p. 74).

  5. 5.

    Spangardt and Meyer (2005) also represents this view.

  6. 6.

    See for example Straja (2001, p. 1ff.).

  7. 7.

    Blanco and Soronow (2001b, p. 83).

  8. 8.

    See e.g. Spangardt and Meyer (2005, p. 226).

  9. 9.

    See e.g. Vose (2008, p. 405).

  10. 10.

    See e.g. Yang and Blyth (2007, p. 11).

  11. 11.

    See Blanco and Soronow (2001a, p. 71).

  12. 12.

    The period between 19/02/07 and 28/12/07 will not be examined, because here the oversupply of the first trading period led to a collapse of the market.

  13. 13.

    The relevant price history is still quite short. As in every model calibration based on historical data, the past holds the best information available. The more empirical data that is available, the more reliable the parameter estimations will be.

  14. 14.

    Monte Carlo methods are a class of computational algorithms used when it is unfeasible to compute an exact result using a deterministic algorithm. As opposed to deterministic simulation methods (such as molecular dynamics) Monte Carlo techniques are stochastic. This means that the computation of the results relies on repeated random sampling using random or pseudo-random numbers. Because of the necessity of numerous repeated calculations and (pseudo-)random numbers, these methods are most suited to calculation by computers. In the simulation of physical or mathematical systems with a large number of coupled degrees of freedom such as fluids or cellular structures or with serious uncertainty in inputs such as the quantitative risk evaluation in business, Monte Carlo methods are widely used. For technical details and application see e.g. Vose (2008, p. 56ff.).

  15. 15.

    Basically, for price increases and decreases different maximum change rates can be specified.

  16. 16.

    The current price history suggests that the actual price is even lower.

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Authors and Affiliations

  1. Halle Institute for Economic Research (IWH), Kleine Märkerstr. 8, 06108, Halle, Germany

    Henry Dannenberg & Wilfried Ehrenfeld

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  1. Henry Dannenberg
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  2. Wilfried Ehrenfeld
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Editors and Affiliations

  1. LS Betriebliches Umweltmanagement, Universität Halle-Wittenberg, Große Steinstr. 73, Halle, 06099, Sachsen-Anhalt, Germany

    Ralf Antes

  2. Environmental Research - UFZ, Department of Urban and, Helmholtz Centre for, Permoserstr. 15, Leipzig, 04318, Germany

    Bernd Hansjürgens

  3. FB 05 Wirtschaftswissenschaften, LS für Wertschöpfungsmanagement insb., Universität Siegen, Hölderlinstr. 3, Siegen, 57076, Germany

    Peter Letmathe

  4. Fakultät für Informatik, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, Neubiberg, 85577, Germany

    Stefan Pickl

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Dannenberg, H., Ehrenfeld, W. (2011). A Model for the Valuation of Carbon Price Risk. In: Antes, R., Hansjürgens, B., Letmathe, P., Pickl, S. (eds) Emissions Trading. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20592-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-20592-7_9

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