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1
Introduction
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Motivation
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Outline
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Notations
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Problems
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Theta
7
Bayesian Approach
8
Robust Approach
9
Adaptive Approach
10
Confidence Regions
11
Description
12
Construction of estimators
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Defining probability measures
14
Clarifying question
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Time Consistent Problem
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Subgame Perfect Strategies
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Confidence Region
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Interest Rate
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Nature
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Time Consistency
Description:
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.

Adaptive Robust Stochastic Control with Applications to Finance

New York University (NYU)
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