Machine Learning With Kernels for Portfolio Valuation and Risk Management
39 Pages Posted: 18 Jun 2019 Last revised: 16 Jan 2020
Date Written: June 9, 2019
We introduce a statistical 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 terminal cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We derive dimension-free sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.
Keywords: dynamic portfolio valuation, kernel ridge regression, learning theory, reproducing kernel Hilbert space, portfolio risk management
JEL Classification: C15, G32
Suggested Citation: Suggested Citation