Maximum Entropy Evaluation of Asymptotic Hedging Error under a Generalised Jump-Diffusion Model

In this paper we propose a maximum entropy estimator for the asymptotic distribution of hedging error options. Perfect replication financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive options under generalised jump-diffusion model with kernel bias, which nests number very important processes in finance. then obtain an estimation by maximising Shannon’s subject set moment constraints, turn yields value-at-risk expected shortfall error. The significance approach lies fact that allows us consistent estimate error, despite non-normality underlying returns.