Relative Performance of Semi-Parametric Nonlinear Models in Forecasting Basis

Many risk management strategies, including hedging the price risk using forward or futures contracts require accurate forecasts of basis, i.e., spot price minus the futures price. Recent literature in this area has applied nonlinear time-series models, which are refinements of the linear autoregressive models that allow the parameters to transition from one regime to another. These parametric nonlinear models, however, involve complex estimation problems, and may diminish forecasting accuracy, especially in longer horizons. We propose using a semi-parametric, generalized additive model (GAM) that may improve the forecasting performance with its simplicity and flexibility while still accounting for nonlinearities in local prices and basis. Empirical results based on weekly futures and spot prices for North Carolina soybean and corn markets support evidence of nonlinear effects in basis. In general, generalized additive models seem to yield better forecasts of basis.