Self-exciting jumps in the oil market: Bayesian estimation and dynamic hedging

In this paper we propose a novel self-exciting jump-diffusion model for oil price dynamics based on a Hawkestype process. In particular, the jump intensity is stochastic and path dependent, implying that the occurrence of a jump will increase the probability of observing a new jump and this feature of the model aims at explaining the jumps clustering effect. Moreover, volatility is described by a stochastic process, which can jump simultaneously with prices. The model specification is completed by a stochastic convenience yield. In order to estimate the model we apply the two-stage Sequential Monte Carlo (SMC) sampler (Fulop and Li, 2019) to both spot and futures quotations. From the estimation results we find evidence of self-excitation in the oil market, which leads to an improved fit and a better out of sample futures forecasting performance with respect to jump-diffusion models with constant intensity. Furthermore, we compute and discuss two optimal hedging strategies based on futures trading. The optimality of the first hedging strategy proposed is based on the variance minimization, while the second strategy takes into account also the third-order moment contribution in considering the investors attitudes. A comparison between the two strategies in terms of hedging effectiveness is provided.