RT Journal Article
SR Electronic
T1 Agency MBS Prepayment Model Using Neural Networks
JF The Journal of Structured Finance
FD Institutional Investor Journals
SP 17
OP 33
DO 10.3905/jsf.2019.24.4.017
VO 24
IS 4
A1 Jiawei “David” Zhang
A1 Xiaojian “Jan” Zhao
A1 Joy Zhang
A1 Fei Teng
A1 Siyu Lin
A1 Hongyuan “Henry” Li
YR 2019
UL https://pm-research.com/content/24/4/17.abstract
AB The authors apply deep neural networks, a type of machine learning method, to model agency mortgage-backed security (MBS) 30-year, fixed-rate pool prepayment behaviors. The neural networks model (NNM) is able to produce highly accurate model fits to the historical prepayment patterns as well as accurate sensitivities to economic and pool-level risk factors. These results are comparable with model results and intuitions obtained from a traditional agency pool-level prepayment model that is in production and was built via many iterations of trial and error over many months and years. This example shows NNM can process large datasets efficiently, capture very complex prepayment patterns, and model large group of risk factors that are highly nonlinear and interactive. The authors also examine various potential shortcomings of this approach, including nontransparency/“black-box” issues, model overfitting, and regime shift issues.TOPICS: MBS and residential mortgage loans, statistical methods, big data/machine learning