Robustification of Elliott's on-line EM algorithm for HMMs

TL;DR

This paper enhances an online EM algorithm for hidden Markov models used in asset price modeling by making it robust against outliers, improving its reliability in real-world financial data with anomalies.

Contribution

It introduces a step-by-step robustification of Elliott's online EM algorithm for HMMs, specifically targeting outlier resistance in asset return modeling.

Findings

Improved robustness against additive outliers in asset prices

Enhanced stability of parameter estimates in the presence of data anomalies

Better handling of peaks and missing data in financial time series

Abstract

In this paper, we establish a robustification of an on-line algorithm for modelling asset prices within a hidden Markov model (HMM). In this HMM framework, parameters of the model are guided by a Markov chain in discrete time, parameters of the asset returns are therefore able to switch between different regimes. The parameters are estimated through an on-line algorithm, which utilizes incoming information from the market and leads to adaptive optimal estimates. We robustify this algorithm step by step against additive outliers appearing in the observed asset prices with the rationale to better handle possible peaks or missings in asset returns.