Black-Box CoVaR and Its Gradient Estimation
Year: 2025
Type: article
Abstract: CoVaR is an important measure of financial systemic risk. Due to the complex structures and dependence of portfolios, estimating the CoVaR of financial systems as well as its gradient information could be very challenging, especially in a black-box setting, where only noisy estimates are available. For CoVaR and its gradient estimation, we propose a stochastic approximation algorithm that is applicable in various complicated cases, including black-box and streaming data scenarios. We establish the strong consistency and rate of convergence of the algorithm under appropriate technical conditions. In particular, the convergence rates of our proposed CoVaR and its gradient estimators are of orders O(n^{-2/5}) and O(n^{-2/7}), respectively, where n is the number of algorithm iterations. Simulation experiments are also performed to support our theoretical findings.
Source: INFORMS journal on computing
Institutions: Fudan University, Stony Brook University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4414350613