Deep kernel hedging
A deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods and derives a generalized representer theorem for the joint hedging problem, reducing the empirical optimization to a finite-dimensional problem is introduced.
Jean-Loup Dupret, Donatien Hainaut, Edouard Motte
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