no code implementations • 1 Mar 2023 • Vedant Choudhary, Sebastian Jaimungal, Maxime Bergeron
We introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices.
1 code implementation • 10 Aug 2021 • Brian Ning, Sebastian Jaimungal, Xiaorong Zhang, Maxime Bergeron
We propose a hybrid method for generating arbitrage-free implied volatility (IV) surfaces consistent with historical data by combining model-free Variational Autoencoders (VAEs) with continuous time stochastic differential equation (SDE) driven models.
no code implementations • 28 Jul 2021 • Xi Li, George Kesidis, David J. Miller, Maxime Bergeron, Ryan Ferguson, Vladimir Lucic
We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an "oracle" capable of providing real-valued supervision (a regression target) for samples.
no code implementations • 7 Feb 2021 • Maxime Bergeron, Nicholas Fung, John Hull, Zissis Poulos
As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options.