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Pricing and Hedging Illiquid Energy Derivatives:an Application to the JCC Index

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  • Matteo Manera

    (University of Milan-Bicocca and Fondazione Eni Enrico Mattei)

  • Elisa Scarpa

    (Banca Intesa)

Abstract

In this paper we discuss a simple econometric strategy for pricing and hedging illiquid financial products, such as the Japanese crude oil cocktail (JCC) index, the most popular OTC energy derivative in Japan. First, we review the existing literature for computing optimal hedge ratios (OHR) and we propose a critical classification of the existing approaches. Second, we compare the empirical performance of different econometric models (namely, regression models in price-levels, price first differences, price returns, as well as error correction and autoregressive distributed lag models) in terms of their computed OHR using monthly data on the JCC over the period January 2000-January 2006. Third, we illustrate and implement a procedure to cross-hedge and price two different swaps on the JCC: a one-month swap and a three-month swap with a variable oil volume. We explain how to compute a bid/ask spread and to construct the hedging position for the JCC swap. Fourth, we evaluate our swap pricing scheme with backtesting and rolling regression techniques. Our empirical findings show that it is not necessary to use sophisticated econometric techniques, since the price level regression model permits to compute a more reliable optimal hedge ratio relative to its competing alternatives.

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Bibliographic Info

Paper provided by Fondazione Eni Enrico Mattei in its series Working Papers with number 2006.130.

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Date of creation: Oct 2006
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Handle: RePEc:fem:femwpa:2006.130

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Keywords: Hedging Models; Cross-Hedging; Energy Derivatives; Illiquid Financial Products; Commodity Markets; JCC Price Index;

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  1. Moschini, GianCarlo & Myers, Robert J., 2002. "Testing for constant hedge ratios in commodity markets: a multivariate GARCH approach," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 589-603, December.
  2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  4. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-70, March.
  5. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
  6. Dimitris Kenourgios & Aristeidis Samitas & Panagiotis Drosos, 2005. "Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract," Finance 0512018, EconWPA.
  7. Anderson, Ronald W & Danthine, Jean-Pierre, 1981. "Cross Hedging," Journal of Political Economy, University of Chicago Press, vol. 89(6), pages 1182-96, December.
  8. Lien, Donald & Tse, Y K, 2002. " Some Recent Developments in Futures Hedging," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 357-96, July.
  9. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  10. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
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