Computing MVA via Regression and Principal Component Analysis

MVA is today's price of the future costs generated by future initial margin postings. Computing MVA requires long-term risk neutral simulations of future initial margin amounts. The ISDA SIMM computes initial margin based on the portfolio's sensitivity with respect to a high-dimensional vector of risk factors. In this note, we describe a way to approximate future SIMM based initial margin amounts in terms of regression functions with respect to a small number of explanatory variables. Our method uses principal components analysis, and it fits in naturally with American Monte Carlo techniques

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Year of publication:	2017
Authors:	Kappen, Christian
Publisher:	[2017]: [S.l.] : SSRN
Subject:	Regressionsanalyse \| Regression analysis \| Hauptkomponentenanalyse \| Principal component analysis \| Schätztheorie \| Estimation theory

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