Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)

TL;DR

This paper explores using machine learning, specifically denoising autoencoders, to extract market-implied features from illiquid corporate bond yields, comparing its performance to traditional interpolation methods.

Contribution

It introduces a novel application of denoising autoencoders for yield feature extraction in illiquid bond markets, demonstrating its effectiveness over conventional interpolation.

Findings

Autoencoder-based method outperforms Thin Plate Spline in feature extraction accuracy.

Machine learning provides a promising approach for analyzing illiquid bond markets.

The approach can be adapted to other illiquid financial instruments.

Abstract

This paper studies the application of machine learning in extracting the market implied features from historical risk neutral corporate bond yields. We consider the example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder algorithm from the field of computer vision and pattern recognition to learn the features of the missing yield parameters from the historically implied data of the instruments traded in the chosen liquid market. The results of the trained machine learning algorithm are compared with the outputs of a point in- time 2 dimensional interpolation algorithm known as the Thin Plate Spline. Finally, the performances of the two algorithms are compared.