Do affine jump-diffusion models require global calibration? Empirical studies from option markets

This study presents an empirical evaluation of the efficiency and robustness of calibration methods for option pricing models with closed-form expression, specifically by using affine jump-diffusion models. To mitigate the local minima problems inherent in model calibration, we provide a global calibration method with enhanced discrete local search strategy. We compare this global calibration method with local calibration methods by using both model generated and real-market cross-sectional option data. Global calibration is highly essential for obtaining a reliable parameter set for affine jump-diffusion models and yields robust calibration results. By using the 2007 S&P 500 index options, we apply the global calibration method to 12 widely used affine jump-diffusion models. Results show that the calibrated parameters common across the models share similar values. This result verifies the robustness of global calibration. Conversely, local calibration generates different values. We also examine the predictive performance of the models and find that global calibration outperforms local calibration for the considered affine jump-diffusion models with respect to in-sample and out-of-sample errors.