LIBOR Prompts Quantile Leap: Machine Learning for Quantile Derivatives

Abstract

Inspired by initially proposed IBOR fallback mechanisms, we show how deep learning can be used to quickly and accurately compute the {expected median} of a time series at future inference dates with varying amounts of observed data. While the IBOR fallback spreads were ultimately fixed, the technique outlined here showcases the ability of neural networks to tackle financial problems over seemingly impossibly large domains.

Keywords: Machine Learning, Deep Learning, Libor, Libor reform, Median, Quantile, Percentile, RFR, OIS, Risk-Free Rates, Sonia, Fallback, Brownian motion with drift, Fallback Spread, Libor Adjustment Spread

JEL Classification: C61, G13, G15, G18, G21, C51