Christian Bayer, Peter Friz, John Schoenmakers, Vladimir Spokoiny
Paul Hager (TU), Paolo Pigato (WIAS)
01.01.2019 – 31.12.2021
TU Berlin / WIAS
In this project, we will develop efficient methods for modeling energy price processes and methods for solving related control or decision problems. Followinyg (Bennedsen, 2017), we explore the use of rough pricing models, which have been very successful for modeling equity markets. Due to the lacking Markov property, rough models pose new mathematical challenges for stochastic control. In this area deep learning is playing an increasingly important role. In this respect, a big challenge is the incorporation of deep learning architectures in new methods for optimal stopping, multiple stopping and control problems.
We compute solutions to these control problems by combining new methods from machine learning (reinforcement learning) with classical tools from optimal control (dynamic programming, regression methods, duality formulas).
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