Initial Margin Simulation with Deep Learning

Xun, Ma; Spinner, Sogee; Venditti, Alex; Zhao, Li; Tang, Strong

doi:10.2139/ssrn.3357626

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…For the IM calculation, it is standard market practice to follow the Standard Initial Margin Model (SIMM) methodology [1], promoted by International Swaps and Derivatives Association (ISDA), which only requires the sensitivities of the portfolio as input data. When the goal is to know this amount over the whole life of the portfolio, the SIMM simulation becomes challenging due to the heavy computational burden coming from nested Monte Carlo simulations and the high-dimensional nature of the problem [2].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Method for Computing Initial Margin

Villarino

Rodriguez

2021

The 4th XoveTIC Conference

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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.

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Section: Introductionmentioning

confidence: 99%

“…Among the existing alternatives to brute-force simulation, there are approaches based on Deep Learning algorithms, as [2]. 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.…”

Section: Introductionmentioning

confidence: 99%