Search Results for author: Maxime Bergeron

Found 4 papers, 1 papers with code

FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs

no code implementations1 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.

Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders

1 code implementation10 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.

Robust and Active Learning for Deep Neural Network Regression

no code implementations28 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.

Active Learning regression

Variational Autoencoders: A Hands-Off Approach to Volatility

no code implementations7 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.

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