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Found 4 papers, 0 papers with code
D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options
With an incomplete market and a more involved objective function, we show that it is beneficial to add options to the portfolio.
Convergence of a robust deep FBSDE method for stochastic control
In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control.
Deep learning for CVA computations of large portfolios of financial derivatives
In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, \textit{e. g.,} a portfolio of a mix of European- and Bermudan-type derivatives.
A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options
Cashflow-paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors.
