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

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  • Greg Kirczenow
  • Ali Fathi
  • Matt Davison

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.

Suggested Citation

  • Greg Kirczenow & Ali Fathi & Matt Davison, 2018. "Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)," Papers 1806.01731, arXiv.org.
    • Handle: RePEc:arx:papers:1806.01731
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    File URL: http://arxiv.org/pdf/1806.01731
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    Cited by:

    1. Castro-Iragorri, C & Ramírez, J, 2021. "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo 19431, Universidad del Rosario.

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