Most previous empirical studies using the Heath-Jarrow-Morton model (hereafter referred to as the HJM model) have focused on the one-factor model. In contrast, this study implements the Das (1999) two-factor Poisson-Gaussian version of the HJM model that incorporates a jump component as the second-state variable. This study aims at examining the performance of the two-factor model through comparing it with the one-factor model in pricing and hedging the Eurodollar futures option. The degree of impact arising from the jump factor also is examined. In addition, three new volatility specifications are constructed to enhance further the pricing performance of the model. Their performances are compared according to three performance yardsticks-in-sample fitting, out-of-sample pricing, and the hedging test. The result indicates that the two-factor model outperforms the one-factor model in both the 840 Zeto in-sample and out-sample price fitting, but the one-factor model performs better in the hedging test. In addition, the HJM model, coupled with the proposed volatility specification, leads to good fitting results that will be of considerable use to practitioners and academics in guiding model choice for interest-rate derivatives.

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This study develops a new conditional extreme value theory-based model (EVT) combined with the NIG + Jump model to forecast extreme risks. This paper utilizes the NIG + Jump model to asymmetrically feedback the past realization of jump innovation to the future volatility of the return distribution and uses the EVT to model the tail distribution of the NIG + Jump-processed residuals. The model is compared to the GARCH-t model and NIG + Jump model to evaluate its performance in estimating extreme losses in three major market crashes and crises. The results show that the conditional EVT-NIG + Jump model outperforms the GARCH and GARCH-t models in depicting the non-normality and in pro- viding accurate VaR forecasts in the in-sample and out-sample tests. The EVT-NIG + Jump model, which can measure the volatility of extreme price movement in capital markets due to unexpected events, enhances the EVT-based model for measuring the tail risk