no code implementations • 26 Jul 2023 • Ivan Guo, Shijia Jin, Kihun Nam
We propose the macroscopic market making model \`a la Avellaneda-Stoikov, using continuous processes for orders instead of discrete point processes.
1 code implementation • 24 Feb 2023 • Ivan Guo, Nicolas Langrené, Jiahao Wu
In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks.
no code implementations • 29 Nov 2021 • Anna Aksamit, Ivan Guo, Shidan Liu, Zhou Zhou
We consider the superhedging price of an exotic option under nondominated model uncertainty in discrete time in which the option buyer chooses some action from an (uncountable) action space at each time step.
no code implementations • 5 Jul 2021 • Ivan Guo, Gregoire Loeper, Jan Obloj, Shiyi Wang
We provide a survey of recent results on model calibration by Optimal Transport.
no code implementations • 28 Jun 2021 • Kaustav Das, Ivan Guo, Grégoire Loeper
In a multi-dimensional diffusion framework, the price of a financial derivative can be expressed as an iterated conditional expectation, where the inner conditional expectation conditions on the future of an auxiliary process that enters into the dynamics for the spot.
no code implementations • 5 Apr 2020 • Ivan Guo, Gregoire Loeper, Jan Obloj, Shiyi Wang
This paper addresses the joint calibration problem of SPX options and VIX options or futures.
no code implementations • 11 Oct 2019 • Guiyuan Ma, Song-Ping Zhu, Ivan Guo
This paper studies the valuation of European contingent claims with short selling bans under the equal risk pricing (ERP) framework proposed in Guo and Zhu (2017) where analytical pricing formulae were derived in the case of monotonic payoffs under risk-neutral measures.
no code implementations • 15 Jun 2019 • Ivan Guo, Gregoire Loeper, Shiyi Wang
In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models.