activity
20072026
most citedApproximation of Stochastic Partial Differential Equations by a Kernel-based Collocation Method

59 citations · 199 across the 36 of their papers we have counts for

collaborators

42 papers

q-fin.PM2026

Robo-Advising in Motion: A Model Predictive Control Approach

Tomasz R. Bielecki, Igor Cialenco

Robo-advisors (RAs) are automated portfolio management systems that complement traditional financial advisors by offering lower fees and smaller initial investment requirements. Wh…

q-fin.RM2025

Coherent estimation of risk measures

Martin Aichele, Igor Cialenco, Damian Jelito +1

We develop a statistical framework for risk estimation, inspired by the axiomatic theory of risk measures. Coherent risk estimators -- functionals of P\&L samples inheriting the ec…

q-fin.RM2023★ 1 cited

Time consistency of dynamic risk measures and dynamic performance measures generated by distortion functions

Tomasz R. Bielecki, Igor Cialenco, Hao Liu

The aim of this work is to study risk measures generated by distortion functions in a dynamic discrete time setup, and to investigate the corresponding dynamic coherent acceptabili…

math.OC2022★ 1 cited

Risk Filtering and Risk-Averse Control of Markovian Systems Subject to Model Uncertainty

Tomasz R. Bielecki, Igor Cialenco, Andrzej Ruszczyński

We consider a Markov decision process subject to model uncertainty in a Bayesian framework, where we assume that the state process is observed but its law is unknown to the observe…

math.OC2021

Risk-sensitive Markov decision problems under model uncertainty: finite time horizon case

Tomasz R. Bielecki, Tao Chen, Igor Cialenco

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 wi…

math.PR2021

Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach

Igor Cialenco, Hyun-Jung Kim, Gregor Pasemann

Motivated by problems from statistical analysis for discretely sampled SPDEs, first we derive central limit theorems for higher order finite differences applied to stochastic proce…