Deep learning Profit & Loss

TL;DR

This paper introduces a neural network-based variation of the Least Square Monte Carlo method to efficiently estimate the profit and loss distribution of complex, multi-asset portfolios with non-linear and path-dependent derivatives.

Contribution

It proposes a semi-automatic neural network approach that simplifies P&L distribution modeling for portfolios with complex payoffs and strong asset dependencies.

Findings

Neural networks effectively interpolate multi-asset payoffs.

The method handles non-linear, path-dependent derivatives.

It accounts for strong dependence structures in portfolios.

Abstract

Building the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features not all of them will be fully discussed in the paper. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation. This is an essential feature to account for the P&L distribution of the whole portfolio when the dependence structure between the different assets is very strong like the case where one has contingent claims written on the same underlying.