The pricing efficiency of exchange-traded commodities

Abstract

Exchange-traded commodities (ETCs) open the commodity markets to both private and institutional investors. This paper is the first to examine the pricing efficiency and potential determinants of price deviations of this new class of derivatives based on daily data of 237 ETCs traded on the German market from 2006 to 2012. Given the unique size of the sample, we employ the premium/discount analysis, quadratic and linear pricing methods, as well as regression models. We find that the ETCs incur, on average, price deviations in their daily trading and are more likely to trade at a premium from their net asset values than at a discount. In addition, we examine the influence of certain factors such as management fees, commodity sectors, issuers, spread, assets under management, investment strategies, replication and collateralization methods on quadratic and linear price deviations.

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Notes

  1. 1.

    The acronym UCITS stands for “Undertakings for Collective Investment in Transferable Securities” directive and is the regulatory framework for an investment vehicle that can be marketed across the European Union.

  2. 2.

    Besides a stock market trading, over-the-counter (OTC) trading may also be possible.

  3. 3.

    Thus, the APs operate as an important link between the secondary and primary market from which retail investors are usually excluded. The settlement by an independent clearing and settlement organization takes place on a normal \(T+2\) or \(T+3\) basis. In summary, a clear distinction between the primary and the secondary market including its market participants is crucial for a correct understanding of the whole ETC structure.

  4. 4.

    The above given information are provided by the German stock exchange “Deutsche Börse AG”.

  5. 5.

    We use log returns instead of simple returns which are also widespread in the context of passive financial instruments in the academic literature and in practice, as the reliance on continuously compounded returns is more valid and suitable in the context of our further statistical computations.

  6. 6.

    In our framework, it would be necessary to aggregate \(\alpha \), \(\beta \) and standard errors into one deviation measure. Since there is no straight forward way to do this, we used the more proven concepts defined above.

  7. 7.

    Brokerage commissions, market fees, clearing and settlement costs as well as taxes and stamp duties are examples of direct or explicit trading costs (see D’Hondt and Giraud 2008).

  8. 8.

    See Ramaswamy (2011) for further information.

  9. 9.

    The independent variable ’Issuer 3’ was excluded from the model for it is identical to the variable ’no collateralization’.

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Correspondence to Gregor Dorfleitner.

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Dorfleitner, G., Gerl, A. & Gerer, J. The pricing efficiency of exchange-traded commodities. Rev Manag Sci 12, 255–284 (2018). https://doi.org/10.1007/s11846-016-0221-0

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Keywords

  • Exchange-traded commodities
  • Commodities
  • Pricing
  • Financial innovation
  • Pricing efficiency

Mathematics Subject Classification

  • 91B24
  • 91B28