Initial Margin Simulation with Deep Learning

22 Pages Posted: 26 Mar 2019

Date Written: March 21, 2019

Abstract

Regulatory initial margin (IM) for non-cleared OTC derivatives is currently being implemented in the financial industry per BCBS-IOSCO requirements. To incorporate IM into counterparty credit risk (CCR) measurements and xVA calculations (especially MVA), it is necessary to simulate future IM requirements inside a CCR/xVA system. However, this is an extremely challenging task because the two readily available approaches – brute force simulation and regression-based approximation – are either prohibitively expensive in terms of computational cost, or extremely difficult for large and diverse portfolios due to the high dimensionality of the problem. In this paper, a practical deep learning approach to IM simulation is proposed, with a proof-of-concept implementation and test results demonstrating fast and accurate portfolio-level simulation of scenario-dependent IM through multiple time steps. Model training is shown to converge quickly, and model performance is robust under practical conditions. This approach separates offline training from online simulation, so that it can be implemented in production without significant system overhaul. Conceptually, since training data are generated by a deterministic function (in this case the sensitivity-based SIMM model), data noise is not a concern and overfitting can be avoided, assuming portfolio turnovers occur gradually during periods of time much longer than model training cycles. Model output can also be explained or validated by the underlying data-generating function for transparency. Other potential applications of this deep learning approach are also discussed, including collateral optimization.

Keywords: Initial Margin, Regulation, OTC Derivatives, SIMM, xVA, MVA, PFE, CCR, Monte-Carlo, Machine Learning, Artificial Intelligence, Neural Networks, Deep Learning

Suggested Citation

Ma, Xun and Spinner, Sogee and Venditti, Alex and Li, Zhao and Tang, Strong, Initial Margin Simulation with Deep Learning (March 21, 2019). Available at SSRN: https://ssrn.com/abstract=3357626 or http://dx.doi.org/10.2139/ssrn.3357626

Xun Ma (Contact Author)

TD Bank ( email )

66 Welington St W
Toronto, ON M5K 1A2
Canada

Sogee Spinner

TD Bank ( email )

66 Welington St W
Toronto, ON M5K 1A2
Canada

Alex Venditti

TD Bank ( email )

66 Welington St W
Toronto, ON M5K 1A2
Canada

Zhao Li

TD Bank ( email )

66 Welington St W
Toronto, ON M5K 1A2
Canada

Strong Tang

TD Bank ( email )

66 Welington St W
Toronto, ON M5K 1A2
Canada

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