A Machine Learning-based Recommendation System for Swaptions Strategies
Abstract
Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work aims to develop a trading recommendation system, and apply this system to the so-called Mid-Curve Calendar Spread (MCCS), an exotic swaption-based derivatives package. In summary, our trading recommendation system follows this pipeline: (i) on a certain trade date, we compute metrics and sensitivities related to an MCCS; (ii) these metrics are feed in a model that can predict its expected return for a given holding period; and after repeating (i) and (ii) for all trades we (iii) rank the trades using some dominance criteria. To suggest that such approach is feasible, we used a list of 35 different types of MCCS; a total of 11 predictive models; and 4 benchmark models. Our results suggest that in general linear regression with lasso regularisation compared favourably to other approaches from a predictive and interpretability perspective.
Keywords
Cite
@article{arxiv.1810.02125,
title = {A Machine Learning-based Recommendation System for Swaptions Strategies},
author = {Adriano Soares Koshiyama and Nick Firoozye and Philip Treleaven},
journal= {arXiv preprint arXiv:1810.02125},
year = {2018}
}