Adaptive Robust Stochastic Control with Applications to Finance

Explore adaptive robust stochastic control and its applications in finance through this Brooklyn Quant Experience (BQE) Lecture Series presentation by Dr. Igor Cialenco from Illinois Institute of Technology. Delve into key topics including motivation, notation, Bayesian and robust approaches, adaptive methods, confidence regions, time consistency, and subgame perfect strategies. Gain insights into the construction of estimators, defining probability measures, and the role of interest rates in this comprehensive exploration of advanced quantitative finance concepts.

Introduction
Motivation
Outline
Notations
Problems
Theta
Bayesian Approach
Robust Approach
Adaptive Approach
Confidence Regions
Description
Construction of estimators
Defining probability measures
Clarifying question
Time Consistent Problem
Subgame Perfect Strategies
Confidence Region
Interest Rate
Nature
Time Consistency