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 MC simulation or simulation and regression of cash flows by learning a metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian over function space, with the spatial covariance structure imposed only on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. This approach quantifies the uncertainty in portfolio valuation arising from the Gaussian process approximation. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations. This is joint work with Stephane Crepey (Paris Saclay).

Bio

Matthew Dixon is an Assistant Professor of Applied Math at the Illinois Institute of Technology. His research in machine learning and computational finance is funded by Intel. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively. He has published several peer reviewed academic publications on machine learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker on Wall Street. He is the deputy editor of the Journal of Machine Learning in Finance and an associate editor for the AIMS Journal of Dynamic and Games.