We present an accurate and computationally efficient method , based on Chebyshev Spectral Decomposition, to stochastically compute the Initial Margin of financial products within a Monte Carlo simulation , via sensitivities simulation. This methodology is compared in terms of accuracy, efficiency, and implementation /maintenance costs with common techniques used for the same purpose, such as amortisation-based, regression-based and Adjoint Algorithmic Differentiation. Measured in terms of these criteria, the methodologies based on Chebyshev interpolants offer an optimal solution and set a new benchmark standard for the industry0pt%DIFAUXCMD ) and Deep Neural Nets (\mbox{%DIFAUXCMD DNNs IM}0pt%DIFAUXCMD )} .
We present two methods , based on Chebyshev tensors, to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation . These methods are implemented and run in a Monte Carlo engine to compute Dynamic Initial Margin as defined by ISDA (SIMM). We show that the levels of accuracy, speed and implementation efforts obtained, compared to the benchmark (DIM obtained calling pricing functions such as are found in risk engines), are better than those obtained by alternative methods presented in the literature, such as regressions (\mbox{%DIFAUXCMD Zhu Chan0pt%DIFAUXCMD ) and Deep Neural Nets (\mbox{%DIFAUXCMD DNNs IM}0pt%DIFAUXCMD )} .
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an accurate and computationally efficient method
two methods
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Spectral Decomposition, to stochastically compute the Initial Margin of financial products
tensors, to compute dynamic sensitivities of financial instruments
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, via sensitivities simulation. This methodology is compared in terms
. These methods are implemented and run in a Monte Carlo engine to compute Dynamic Initial Margin as defined by ISDA (SIMM). We show that the levels
R
efficiency, and implementation /maintenance costs with common techniques used for the same purpose, such as amortisation-based, regression-based and Adjoint Algorithmic Differentiation. Measured in terms of these criteria, the methodologies based on Chebyshev interpolants offer an optimal solution and set a new benchmark standard for the industry
speed and implementation efforts obtained, compared to the benchmark (DIM obtained calling pricing functions such as are found in risk engines), are better than those obtained by alternative methods presented in the literature, such as regressions (\mbox{%DIFAUXCMD Zhu Chan