This paper focuses on a dynamic multi‐asset mean‐variance portfolio selection problem
under model uncertainty. We develop a continuous time framework for taking into account …

In this paper we propose a new methodology for solving an uncertain stochastic Markovian
control problem in discrete time. We call the proposed methodology the adaptive robust …

The supplementary material in [2] contains three sections. The first section proposes an
alternative SGDCT estimator that employs the continuous part of the process X to estimate …

We consider a multi-period stochastic control problem where the multivariate driving
stochastic factor of the system has known marginal distributions but uncertain dependence …

We investigate the adaptive robust control framework for portfolio optimization and loss-
based hedging under drift and volatility uncertainty. Adaptive robust problems offer many …

We consider a discrete time stochastic Markovian control problem under model uncertainty.
Such uncertainty comes not only from the fact that the true probability law of the underlying …

We propose a continuous-time version of the adaptive robust methodology introduced in TR
Bielecki et al.[SIAM J. Control Optim., 57 (2019), pp. 925--946]. An agent solves a stochastic …

We consider the problem of filtering an unseen Markov chain from noisy observations, in the
presence of uncertainty regarding the parameters of the processes involved. Using the …

We study dynamic allocation problems for discrete time multi-armed bandits under
uncertainty, based on the the theory of nonlinear expectations. We show that, under …

In this paper we study a class of risk-sensitive Markovian control problems in discrete time
subject to model uncertainty. We consider a risk-sensitive discounted cost criterion with finite …