Calibrating a Latent Order Book Model to Market Data

Gant, Michael

Calibrating a Latent Order Book Model to Market Data

Master Thesis

2022

Abstract

We investigate the formulation of the Latent Order Book (LOB) as a reaction diffusion Partial Differential Equation (PDE) and its subsequent numerical solution through an explicit method based on discrete stochastic processes. The numerical solution is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion PDE and certain free-parameters are recoverable with the iterative calibration techniques.

Keywords

Statistical Sciences,

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