Optimal Stochastic Decensoring and Applications to Calibration of Market Models

TL;DR

This paper introduces an optimal stochastic decensoring method for reconstructing missing data generated by diffusion processes, with applications to calibrating market models and recreating historical stock data.

Contribution

It presents a novel optimal stochastic decensoring technique specifically designed for diffusion process data, improving historical data reconstruction for financial modeling.

Findings

Effective reconstruction of missing diffusion process data

Improved calibration of market models using reconstructed data

Demonstrated application to historical stock data recreation

Abstract

Typically flat filling, linear or polynomial interpolation methods to generate missing historical data. We introduce a novel optimal method for recreating data generated by a diffusion process. The results are then applied to recreate historical data for stocks.