Scheduling Processes and Inference of Scheduled Events From Price Data

Abstract

We introduce 'scheduling processes,' a novel class of processes tailored for modeling scheduled events in financial markets. These processes, driven by a Poisson mechanism, enable the endogenous arrival of events. Their most notable feature is the closed-form characteristic function, facilitating efficient derivative pricing through Fourier inversion methods. We also developed a specific filter method that allows us to draw conclusions about these planned events from derivative prices. An application of this model to VIX options from 2016 to 2020 not only identifies key events but also demonstrates superior performance compared to models without this feature.

Keywords: Option pricing, scheduling processes, particle filtering, event pricing

JEL Classification: G13, G17, C58, C32, C53