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The theory of Optimal Transport (OT) and Martingale Optimal Transport (MOT) were inspired by problems in economics and finance and have flourished over the past decades, making significant advances in theory and practice. MOT considers the…

Probability · Mathematics 2023-04-25 Tongseok Lim

Martingale optimal transport (MOT) often yields broad price bounds for options, constraining their practical applicability. In this study, we extend MOT by incorporating causality constraints among assets, inspired by the nonanticipativity…

Mathematical Finance · Quantitative Finance 2026-02-26 Erhan Bayraktar , Bingyan Han , Dominykas Norgilas

We establish dual attainment for the multimarginal, multi-asset martingale optimal transport (MOT) problem, a fundamental question in the mathematical theory of model-independent pricing and hedging in quantitative finance. Our main result…

Mathematical Finance · Quantitative Finance 2026-02-04 Charlie Che , Tongseok Lim , Yue Sun

We study the problem of bounding path-dependent expectations (within any finite time horizon $d$) over the class of discrete-time martingales whose marginal distributions lie within a prescribed tolerance of a given collection of benchmark…

Probability · Mathematics 2021-12-01 Zhengqing Zhou , Jose Blanchet , Peter W. Glynn

We consider an investor who wants to hedge a path-dependent option with maturity $T$ using a static hedging portfolio using cash, the underlying, and vanilla put/call options on the same underlying with maturity $ t_1$, where $0 < t_1 < T$.…

Mathematical Finance · Quantitative Finance 2025-11-04 Purba Banerjee , Srikanth Iyer , Shashi Jain

The duality between the robust (or equivalently, model independent) hedging of path dependent European options and a martingale optimal transport problem is proved. The financial market is modeled through a risky asset whose price is only…

Probability · Mathematics 2013-06-19 Yan Dolinsky , H. Mete Soner

We study the influence of additional intermediate marginal distributions on the value of the martingale optimal transport problem. From a financial point of view, this corresponds to taking into account call option prices not only, as…

Mathematical Finance · Quantitative Finance 2023-11-03 Julian Sester

We study a variant of the martingale optimal transport problem in a multi-period setting to derive robust price bounds of a financial derivative. On top of marginal and martingale constraints, we introduce a time-homogeneity assumption,…

Mathematical Finance · Quantitative Finance 2021-05-07 Stephan Eckstein , Michael Kupper

We establish numerical methods for solving the martingale optimal transport problem (MOT) - a version of the classical optimal transport with an additional martingale constraint on transport's dynamics. We prove that the MOT value can be…

Probability · Mathematics 2019-04-08 Gaoyue Guo , Jan Obloj

We study the martingale optimal transport problem with state-dependent trading frictions and develop a geometric and duality framework extending from the one time-step to the multi-marginal setting. Building on the left-monotone structure…

Optimization and Control · Mathematics 2025-10-14 Pratik Rai

The Black-Scholes-Merton model is a mathematical model for the dynamics of a financial market that includes derivative investment instruments, and its formula provides a theoretical price estimate of European-style options. The model's…

Mathematical Finance · Quantitative Finance 2023-07-04 Tongseok Lim

We consider the pricing of derivatives in a setting with trading restrictions, but without any probabilistic assumptions on the underlying model, in discrete and continuous time. In particular, we assume that European put or call options…

Mathematical Finance · Quantitative Finance 2015-06-09 Alexander M. G. Cox , Zhaoxu Hou , Jan Obloj

The Fundamental Review of the Trading Book (FRTB) poses a significant challenge for exotic derivatives pricing, particularly for non-modelable risk factors (NMRF) where sparse market data leads to infinite audit bounds under classical…

Risk Management · Quantitative Finance 2026-02-03 Sri Sairam Gautam B. , Isha

In a discrete-time market, we study model-independent superhedging, while the semi-static superhedging portfolio consists of {\it three} parts: static positions in liquidly traded vanilla calls, static positions in other tradable, yet…

Pricing of Securities · Quantitative Finance 2015-06-16 Arash Fahim , Yu-Jui Huang

We consider robust pricing and hedging for options written on multiple assets given market option prices for the individual assets. The resulting problem is called the multi-marginal martingale optimal transport problem. We propose two…

Probability · Mathematics 2020-10-08 Stephan Eckstein , Gaoyue Guo , Tongseok Lim , Jan Obloj

We pursue robust approach to pricing and hedging in mathematical finance. We consider a continuous time setting in which some underlying assets and options, with continuous paths, are available for dynamic trading and a further set of…

Mathematical Finance · Quantitative Finance 2015-07-07 Zhaoxu Hou , Jan Obloj

In this article we discuss the problem of calculating optimal model-independent (robust) bounds for the price of Asian options with discrete and continuous averaging. We will give geometric characterisations of the maximising and the…

Probability · Mathematics 2014-12-04 Florian Stebegg

The calibration of volatility models from observable option prices is a fundamental problem in quantitative finance. The most common approach among industry practitioners is based on the celebrated Dupire's formula [6], which requires the…

Mathematical Finance · Quantitative Finance 2019-06-25 Ivan Guo , Grégoire Loeper , Shiyi Wang

While many questions in (robust) finance can be posed in the martingale optimal transport (MOT) framework, others require to consider also non-linear cost functionals. Following the terminology of Gozlan, Roberto, Samson and Tetali this…

Probability · Mathematics 2022-04-05 Mathias Beiglböck , Benjamin Jourdain , William Margheriti , Gudmund Pammer

Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning applications, and its multi-marginal extension opens up for…

Machine Learning · Computer Science 2021-12-07 Jiaojiao Fan , Isabel Haasler , Johan Karlsson , Yongxin Chen
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