6 papers
Particle Methods with Deep Learning for Stochastic Control under Partial Observation
Mathieu Laurière, Xiaolu Tan, Jiefei Yang
Numerical computation of stochastic control problems under partial observation is challenging because the dynamic programming formulation is naturally posed on the conditional dist…
Optimal incentive scheme for ESG disclosure
Imen Ben Tahar, Dylan Possamaï, Xiaolu Tan
This paper characterises optimal incentive schemes for ESG disclosure in a continuous-time principal-agent setting. We model a risk-averse principal (e.g., a platform or standard-s…
Limit theory for mean-field control problems with common noise adapted controls
Bruno Bouchard, Xiaolu Tan
We consider a mean-field control problem in which admissible controls are required to be adapted to the common noise filtration. The main objective is to show how the mean-field co…
Exit Incentives for Carbon Emissive Firms
René Aïd, Xiangying Pang, Xiaolu Tan
We develop a continuous-time model of incentives for carbon emissive firms to exit the market based on a compensation payment identical to all firms. In our model, firms enjoy prof…
Unbiased simulation of Asian options
Bruno Bouchard, Xiaolu Tan
We provide an extension of the unbiased simulation method for SDEs developed in Henry-Labordere et al. [Ann Appl Probab. 27:6 (2017) 1-37] to a class of path-dependent dynamics, pe…
On the regularity of solutions of some linear parabolic path-dependent PDEs
Bruno Bouchard, Xiaolu Tan
We study a class of linear parabolic path-dependent PDEs (PPDEs) defined on the space of càdlàg paths , in which the coefficient functions at time depend on $x(…