LIBOR Prompts Quantile Leap: Machine Learning for Quantile Derivatives

5 Pages Posted: 12 Jul 2021

See all articles by Maxime Bergeron

Maxime Bergeron

Riskfuel Analytics

Ryan Ferguson

Riskfuel

Vladimir Lucic

Imperial College London

Ivan Sergienko

Riskfuel Analytics

Date Written: July 7, 2021

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

Suggested Citation

Bergeron, Maxime and Ferguson, Ryan and Lucic, Vladimir and Sergienko, Ivan, LIBOR Prompts Quantile Leap: Machine Learning for Quantile Derivatives (July 7, 2021). Available at SSRN: https://ssrn.com/abstract=3882160 or http://dx.doi.org/10.2139/ssrn.3882160

Maxime Bergeron (Contact Author)

Riskfuel Analytics ( email )

Toronto
Canada

HOME PAGE: http://https://riskfuel.com/

Ryan Ferguson

Riskfuel ( email )

140 Yonge Street, Suite 316
Toronto, Ontario M5C1X6
Canada

HOME PAGE: https://www.riskfuel.com/

Vladimir Lucic

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Ivan Sergienko

Riskfuel Analytics ( email )

Toronto
Canada

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