Approximating intractable short ratemodel distribution with neural network
This work addresses a domain-specific problem in finance for modeling short rates, but appears incremental as it builds on existing methods without introducing a new paradigm.
The authors tackled the problem of approximating intractable short rate model distributions by predicting subsequent time steps relative to previous ones, achieving superior outcomes compared to unbiased estimates on both trained and validation datasets.
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.