Approximating intractable short ratemodel distribution with neural network

TL;DR

This paper introduces a neural network-based algorithm to approximate the intractable short rate model distribution, predicting future values relative to previous steps, and demonstrates its superior accuracy over unbiased estimates on multiple datasets.

Contribution

The paper presents a novel neural network approach for approximating intractable short rate models, improving prediction accuracy over traditional unbiased methods.

Findings

Achieves superior prediction accuracy on trained data

Performs well on validation datasets

Outperforms unbiased estimates in experiments

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.