Dynamic hedging strategies and commodity risk management - Final report

In this report we have investigated the experimental performance of a dynamic hedging strategy to secure a physical commitment having a one to five years maturity. In order to implement this strategy a model for the term structure is needed. We selected three models from those available: a simple one-factor model, where the single factor, the spot price, is assumed to be the exponential of a mean reverting process (namely an Ornstein Uhlenbeck process), a two- and a three-factor models, where the spot price is a geometric brownian motion, with a drift defined by one or two Ornstein Uhlenbeck processes. The parameters of these models have been calibrated applying a Kalman filter and maximum likelihood to the data, which consist in the values of the crude oil futures prices up to 5 years, for the period 1997-2002. The one, three, six and nine month maturities have been used for this calibration. The others maturities have been used in order to experimentally test the performances of the dynamic hedging strategies. In addition to the futures prices values, we took into account the effect of the term structure of interest rates, which was available to us for the same period than the futures. The experimental results show that although the model has been fitted on the whole period (1997-2002), the dynamic hedging can be efficient with a hedge portfolio incorporating maturities less than 12 months, provided we limit ourselves to 1.5 years to 3 years ahead. The results we obtain are comparable to those obtained by Brennan and Crew on a different period in time and considering a one and two factor model, but with a constant interest rate. In our case the effect of transaction costs on the futures market are negligible, but the interest rate spread on the hedger has an impact (of several percent) on the hedge performance. To improve the hedging performance we have to improve the adequacy of the model to our data. This can be done by changing the way we calibrate the models. This is the main direction for our future research