Building the future profit and loss distribution of a portfolio holding highly nonlinear and path-dependent derivatives, among other assets, is a challenging task. Giacomo Bormetti, Flavio Cocco and Pietro Rossi provide a simple machinery where an increasing number of assets may be accounted for in a simple and semi-automatic fashion. They resort to a variation of the least squares Monte Carlo algorithm in which the continuation value of the portfolio is interpolated with a feed-forward neural network. They account for the profit and loss distribution of a whole portfolio even when the dependence structure between different assets is very strong, eg, for contingent claims written on the same underlying.

Deep learning profit and loss

Giacomo Bormetti;
2021-01-01

Abstract

Building the future profit and loss distribution of a portfolio holding highly nonlinear and path-dependent derivatives, among other assets, is a challenging task. Giacomo Bormetti, Flavio Cocco and Pietro Rossi provide a simple machinery where an increasing number of assets may be accounted for in a simple and semi-automatic fashion. They resort to a variation of the least squares Monte Carlo algorithm in which the continuation value of the portfolio is interpolated with a feed-forward neural network. They account for the profit and loss distribution of a whole portfolio even when the dependence structure between different assets is very strong, eg, for contingent claims written on the same underlying.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1496998
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