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• W2913374098 abstract "Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA computation. Our approach avoids nested simulation or simulation and regression of cash flows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian process over function space, with the spatial covariance structure imposed on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. The uncertainty in portfolio valuation arising from the Gaussian process approximation is quantified numerically. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations, including a counterparty portfolio of interest rate swaps." @default.
• W2913374098 created "2019-02-21" @default.
• W2913374098 creator A5036435050 @default.
• W2913374098 creator A5081229081 @default.
• W2913374098 date "2019-01-30" @default.
• W2913374098 modified "2023-10-10" @default.
• W2913374098 title "Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations" @default.
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• W2913374098 hasPublicationYear "2019" @default.
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