Single- and Multi-Level Fourier-RQMC Methods for Multivariate Shortfall Risk

TL;DR

This paper introduces Fourier-based randomized quasi Monte Carlo methods for efficiently estimating multivariate shortfall risk and optimal allocations, significantly improving accuracy and reducing computational costs compared to traditional approaches.

Contribution

The authors develop novel Fourier RQMC algorithms, including a multilevel scheme, with rigorous convergence analysis and demonstrated superior performance over existing methods.

Findings

Fourier RQMC methods outperform benchmarks in accuracy and cost.

Multilevel RQMC reduces computational complexity while maintaining precision.

Theoretical analysis confirms improved convergence rates.

Abstract

Multivariate shortfall risk measures provide a principled framework for quantifying systemic risk and determining capital allocations prior to aggregation in interconnected financial systems. Despite their well established theoretical properties, the numerical estimation of multivariate shortfall risk and the corresponding optimal allocations remains computationally challenging, as existing Monte Carlo based approaches can be numerically expensive due to slow convergence. In this work, we develop a new class of single and multilevel numerical algorithms for estimating multivariate shortfall risk and the associated optimal allocations, based on a combination of Fourier inversion techniques and randomized quasi Monte Carlo (RQMC) sampling. Rather than operating in physical space, our approach evaluates the relevant expectations appearing in the risk constraint and its optimization in the frequency domain, where the integrands exhibit enhanced smoothness properties that are well suited for RQMC integration. We establish a rigorous mathematical framework for the resulting Fourier RQMC estimators, including convergence analysis and computational complexity bounds. Beyond the single level method, we introduce a multilevel RQMC scheme that exploits the geometric convergence of the underlying deterministic optimization algorithm to reduce computational cost while preserving accuracy. Numerical experiments demonstrate that the proposed Fourier RQMC methods outperform sample average approximation and stochastic optimization benchmarks in terms of accuracy and computational cost across a range of models for the risk factors and loss structures. Consistent with the theoretical analysis, these results demonstrate improved asymptotic convergence and complexity rates relative to the benchmark methods, with additional savings achieved through the proposed multilevel RQMC construction.