  This paper studies an application of machine learning in extracting features
from the historical market implied corporate bond yields. We consider an
example of a hypothetical illiquid fixed income market. After choosing a
surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to
learn the features of the missing yield parameters from the historical data of
the instruments traded in the chosen liquid market. The DAE algorithm is then
challenged by two "point-in-time" inpainting algorithms taken from the image
processing and computer vision domain. It is observed that, when tested on
unobserved rate surfaces, the DAE algorithm exhibits superior performance
thanks to the features it has learned from the historical shapes of yield
curves.
