Approximating intractable short ratemodel distribution with neural network
Machine Learning
2024-04-15 v9 Machine Learning
Mathematical Finance
Abstract
We propose an algorithm which predicts each subsequent time step relative to the previous timestep of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and different validation data.
Cite
@article{arxiv.1912.12615,
title = {Approximating intractable short ratemodel distribution with neural network},
author = {Anna Knezevic and Nikolai Dokuchaev},
journal= {arXiv preprint arXiv:1912.12615},
year = {2024}
}
Comments
Incorrect methodology for generation of stochastic scenarios