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.

Giacomo Bormetti, Flavio Cocco, Pietro Rossi (2021). Deep learning profit and loss. RISK, October, 1-6.

Deep learning profit and loss

Giacomo Bormetti;
2021

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.
2021
Giacomo Bormetti, Flavio Cocco, Pietro Rossi (2021). Deep learning profit and loss. RISK, October, 1-6.
Giacomo Bormetti; Flavio Cocco; Pietro Rossi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/837095
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