184 citations · 540 across the 36 of their papers we have counts for
45 papers
Continuous-time Optimal Stopping through Deep Reinforcement Learning
Cosmin Borsa, Michael Ludkovski
Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping o…
DeepPAAC: A New Deep Galerkin Method for Principal-Agent Problems
Michael Ludkovski, Changgen Xie, Zimu Zhu
We consider numerical resolution of principal-agent (PA) problems in continuous time. We formulate a generic PA model with continuous and lump payments and a multi-dimensional stra…
Functional Analysis of Loss-development Patterns in P&C Insurance
Arthur Charpentier, Qiheng Guo, Mike Ludkovski
We analyze loss development in NAIC Schedule P loss triangles using functional data analysis methods. Adopting the functional viewpoint, our dataset comprises 3300+ curves of incre…
Expressive Mortality Models through Gaussian Process Kernels
Mike Ludkovski, Jimmy Risk
We develop a flexible Gaussian Process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative…
Large Scale Probabilistic Simulation of Renewables Production
Mike Ludkovski, Glen Swindle, Eric Grannan
We develop a probabilistic framework for joint simulation of short-term electricity generation from renewable assets. In this paper we describe a method for producing hourly day-ah…
On Parametric Optimal Execution and Machine Learning Surrogates
Tao Chen, Mike Ludkovski, Moritz Voß
We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact w…