10 citations · 23 across the 7 of their papers we have counts for
7 papers
Deep Semi-Martingale Optimal Transport
Ivan Guo, Nicolas Langrené, Grégoire Loeper +1
We propose two deep neural network-based methods for solving semi-martingale optimal transport problems. The first method is based on a relaxation/penalization of the terminal cons…
Portfolio optimization with a prescribed terminal wealth distribution
Ivan Guo, Nicolas Langrené, Grégoire Loeper +1
This paper studies a portfolio allocation problem, where the goal is to prescribe the wealth distribution at the final time. We study this problem with the tools of optimal mass tr…
Robust utility maximization under model uncertainty via a penalization approach
Ivan Guo, Nicolas Langrené, Grégoire Loeper +1
This paper addresses the problem of utility maximization under uncertain parameters. In contrast with the classical approach, where the parameters of the model evolve freely within…
Empirical Likelihood for Change Point Detection in Autoregressive Models
Ramadha D. Piyadi Gamage, Wei Ning
Change point analysis has become an important research topic in many fields of applications. Several research work has been carried out to detect changes and its locations in time…
Adjusted Empirical Likelihood for Long-memory Time Series Models
Ramadha D. Piyadi Gamage, Wei Ning, Arjun K. Gupta
Empirical likelihood method has been applied to short-memory time series models by Monti (1997) through the Whittle's estimation method. Yau (2012) extended this idea to long-memor…
Adjusted Jackknife Empirical Likelihood
Ying-Ju Chen, Wei Ning
Jackknife empirical likelihood (JEL) is an effective modified version of empirical likelihood method (EL). Through the construction of the jackknife pseudo-values, JEL overcomes th…
Adjusted Empirical Likelihood for Time Series Models
Ramadha D. Piyadi Gamage, Wei Ning, Arjun K. Gupta
Empirical likelihood method has been applied to dependent observations by Monti (1997) through the Whittle's estimation method. Similar asymptotic distribution of the empirical lik…