Paper Title: “Hidden Markov Model for Portfolio Management with Mortgage-Backed Securities Exchange-Traded Fund” was published on the Society of Actuaries website in April. This project was funded by the finance research grants from SOA, from June 2016-June 2017.
The hidden Markov model (HMM) is a regime-shift model that assumes observation data were driven by hidden regimes (or states). The model has been used in many fields, such as speech recognition, handwriting recognition, biomathematics and financial economics. In this paper, we describe HMM and its application in finance and actuarial areas. We then develop a new application of HMM in mortgage-backed securities exchange-traded funds (MBS ETFs). We begin with a primer on the hidden Markov model, covering main concepts, the model’s algorithms and examples to demonstrate the concepts. Next, we introduce some applications of the model in actuarial and financial areas. We then present applications of HMM on MBS ETFs. Finally, we establish a new use of HMM for a portfolio management with MBS ETFs: predicting prices and trading some MBS ETFs. Data, algorithms and codes generated in this paper can be used for future research in actuarial science and finance.
Paper Title: “Using the Hidden Markov Model to Improve the Hull-White Model for Short Rate”, a collaboration work with Thomas Wakefield, YSU, and Dung Nguyen, Ned Davis Research Group, was accepted to publish in the International Journal of Trade, Economics and Finance.