Boudabsa, LotfiFilipovic, Damir2021-12-042021-12-042021-12-042021-11-2210.1007/s00780-021-00465-4https://infoscience.epfl.ch/handle/20.500.14299/183522WOS:000721391100001We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite-sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.Business, FinanceMathematics, Interdisciplinary ApplicationsSocial Sciences, Mathematical MethodsStatistics & ProbabilityBusiness & EconomicsMathematicsMathematical Methods In Social Sciencesdynamic portfolio valuationkernel ridge regressionlearning theoryreproducing kernel hilbert spaceportfolio risk managementreplicating portfoliotheoremspaceratestext::journal::journal article::research article