Efficient Monte Carlo Valuation of Corporate Bonds in Financial Networks

TL;DR

This paper introduces a novel Monte Carlo method for efficiently valuing corporate bonds within complex financial networks, addressing the limitations of existing techniques in capturing rare default events and network effects.

Contribution

We develop Bi-Level Importance Sampling with Splitting, a scalable and asymptotically optimal approach that decouples bank defaults from network dynamics for improved estimation accuracy.

Findings

Method effectively captures rare default events.

Scales well with large financial networks.

Validated on real-world network data.

Abstract

Valuing corporate bonds in systemic economies is challenging due to intricate webs of inter-institutional exposures. When a bank defaults, cascading losses propagate through the network, with payments determined by a system of fixed-point equations lacking closed-form solutions. Standard Monte Carlo methods cannot capture rare yet critical default events, while existing rare-event simulation techniques fail to account for higher-order network effects and scale poorly with network size. To overcome these challenges, we propose a novel approach -- Bi-Level Importance Sampling with Splitting -- and characterize individual bank defaults by decoupling them from the network's complex fixed-point dynamics. This separation enables a two-stage estimation process that directly generates samples from the banks' default events. We demonstrate theoretically that the method is both scalable and asymptotically optimal, and validate its effectiveness through numerical studies on empirically observed networks.