Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement

TL;DR

This paper introduces machine learning techniques, especially Gaussian Process regression, to efficiently estimate portfolio tail risk measures like VaR and TVaR in nested simulation frameworks, reducing computational costs and improving accuracy.

Contribution

It develops sequential algorithms using Gaussian Process models for adaptive simulation allocation, enhancing tail risk estimation in portfolio management.

Findings

Gaussian Process models improve estimation accuracy.

Sequential algorithms reduce computational costs.

Enhanced uncertainty quantification for risk measures.

Abstract

We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor levels at the risk horizon. Traditionally, evaluating portfolio losses of an outer scenario is done by computing a conditional expectation via inner-level Monte Carlo and is computationally expensive. We introduce several inter-related machine learning techniques to speed up this computation, in particular by properly accounting for the simulation noise. Our main workhorse is an advanced Gaussian Process (GP) regression approach which uses nonparametric spatial modeling to efficiently learn the relationship between the stochastic factors defining scenarios and corresponding portfolio value. Leveraging this emulator, we develop sequential algorithms that adaptively allocate inner simulation budgets to target the quantile region. The GP framework also yields better uncertainty quantification for the resulting VaR/TVaR estimators that reduces bias and variance compared to existing methods. We illustrate the proposed strategies with two case-studies in two and six dimensions.