Machine learning with kernels for portfolio valuation and risk management

被引：4
|
作者：
Boudabsa, Lotfi [1 ]
Filipovic, Damir [1 ,2 ]
机构：
[1] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland
[2] Swiss Finance Inst, Zurich, Switzerland
来源：
FINANCE AND STOCHASTICS | 2022年 / 26卷 / 02期
关键词：
Dynamic portfolio valuation; Kernel ridge regression; Learning theory; Reproducing kernel Hilbert space; Portfolio risk management; REPLICATING PORTFOLIO; THEOREM; SPACE; RATES;
D O I：
10.1007/s00780-021-00465-4
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
We 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.
引用
收藏
页码：131 / 172
页数：42
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