Vörös, Miklós (MSCI)
Neural
Network Model of Mortgage-Backed Security Prepayments
We apply deep neural networks, a type of machine
learning method, to model agency mortgage-backed security (MBS) 30-year,
fixed-rate pool prepayment behaviours. The neural networks model (NNM) is able
to produce highly accurate model that fits the historical prepayment patterns
as well as accurate sensitivities to economic and pool-level risk factors.
These results are comparable with model results and intuition 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. We also examine various potential shortcomings of
this approach, including nontransparency/“black-box” issues, model overfitting,
and regime shift issues.
The talk is held in English!
Az előadás nyelve angol!
Date: Nov 5, Tuesday 4:15pm
Place: BME, Building „Q”, Room
QBF13