A Regularized Vector Autoregressive Hidden Semi-Markov Model, with Application to Multivariate Financial Data

TL;DR

This paper introduces a regularized vector autoregressive hidden semi-Markov model tailored for analyzing multivariate financial time series with regime switching, employing an augmented EM algorithm for improved parameter estimation.

Contribution

It develops a novel regularized model with an augmented EM algorithm for better capturing regime switches in financial data.

Findings

Regularized estimators improve model performance in simulations.

Model effectively captures regime changes in NYSE data.

Enhanced parameter estimation accuracy with the proposed method.

Abstract

A regularized vector autoregressive hidden semi-Markov model is developed to analyze multivariate financial time series with switching data generating regimes. Furthermore, an augmented EM algorithm is proposed for parameter estimation by embedding regularized estimators for the state-dependent covariance matrices and autoregression matrices in the M-step. The performance of the proposed regularized estimators is evaluated both in the simulation experiments and on the New York Stock Exchange financial portfolio data.