Abstract
Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, the computation of such collateral is performed following the Standard Initial Margin Model (SIMM) methodology. Focusing on a portfolio consisting of an interest rate swap, we propose the use of Artificial Neural Networks (ANN) to approximate the Initial Margin value of the portfolio over its lifetime. The goal is to find an optimal configuration of structural hyperparameters, as well as to analyze the robustness of the network to variations in the model parameters and swap features.
Highlights
As a Deep Learning model for the task of computing the Initial Margin (IM), we propose to use a self-normalizing neural network (SNN) [3], adding a single unit output layer
Unlike the usual methodology, where features associated with the scenario ω and time step j tuple are considered as a single input data w for training, xw j, with the corresponding target y j ; we propose to use the entire scenario as w input data, x, with the corresponding target vector yw
We study the optimal choice of structural hyperparameters of our proposed neural network
Summary
We aim to implement a supervised neural network for computing the IM over the considered portfolio’s life, with special attention to its structure’s design
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