Zobrazeno 1 - 10
of 407
pro vyhledávání: '"Loeper, P."'
We propose a discrete time formulation of the semi-martingale optimal transport problem based on multi-marginal entropic transport. This approach offers a new way to formulate and solve numerically the calibration problem proposed by [17], using a mu
Externí odkaz:
http://arxiv.org/abs/2412.00030
Autor:
Benamou, Jean-David, Chazareix, Guillaume, Hoffmann, Marc, Loeper, Grégoire, Vialard, François-Xavier
Entropic Optimal Transport (EOT), also referred to as the Schr\"odinger problem, seeks to find a random processes with prescribed initial/final marginals and with minimal relative entropy with respect to a reference measure. The relative entropy forc
Externí odkaz:
http://arxiv.org/abs/2408.09361
We introduce and study geometric Bass martingales. Bass martingales were introduced in \cite{Ba83} and studied recently in a series of works, including \cite{BaBeHuKa20,BaBeScTs23}, where they appear as solutions to the martingale version of the Bena
Externí odkaz:
http://arxiv.org/abs/2406.04016
We contribute to the recent studies of the so-called Bass martingale. Backhoff-Veraguas et al. (2020) showed it is the solution to the martingale Benamou-Brenier (mBB) problem, i.e., among all martingales with prescribed initial and terminal distribu
Externí odkaz:
http://arxiv.org/abs/2310.13797
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-W8-2024, Pp 311-318 (2024)
Reliable pose information is essential for many applications, such as for navigation or surveying tasks. Though GNSS is a well-established technique to retrieve that information, it often fails in urban environments due to signal occlusion or multi-p
Externí odkaz:
https://doaj.org/article/03b6adb57f4a48c1b9cf37037c9aadbe
We develop and implement a non-parametric method for joint exact calibration of a local volatility model and a correlated stochastic short rate model using semimartingale optimal transport. The method relies on the duality results established in Jose
Externí odkaz:
http://arxiv.org/abs/2308.14473
We study robust mean-variance optimization in multiperiod portfolio selection by allowing the true probability measure to be inside a Wasserstein ball centered at the empirical probability measure. Given the confidence level, the radius of the Wasser
Externí odkaz:
http://arxiv.org/abs/2306.16681
We develop a non-parametric, optimal transport driven, calibration methodology for local volatility models with stochastic interest rate. The method finds a fully calibrated model which is the closest to a given reference model. We establish a genera
Externí odkaz:
http://arxiv.org/abs/2305.00200
We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First the PDE is rewritten in its dual stochastic control representation form, a
Externí odkaz:
http://arxiv.org/abs/2205.09815
We propose a data-driven way to reduce the noise of covariance matrices of nonstationary systems. In the case of stationary systems, asymptotic approaches were proved to converge to the optimal solutions. Such methods produce eigenvalues that are hig
Externí odkaz:
http://arxiv.org/abs/2111.13109