Measuring Financial Resilience Using Backward Stochastic Differential Equations

TL;DR

This paper introduces a new measure called the resilience rate to quantify financial recovery, using advanced stochastic calculus and backward stochastic differential equations with jumps, with applications demonstrated through financial examples.

Contribution

It develops a novel stochastic calculus framework for the resilience rate based on BSDEs with jumps, linking it to dynamic risk measures and resilience-acceptance sets.

Findings

Resilience rate can be represented as an expectation of the BSDE generator.

Properties of the resilience rate are connected to the properties of the BSDE generator.

Illustrated applications in canonical financial models.

Abstract

We introduce the resilience rate as a measure of financial resilience. It captures the expected rate at which a dynamic risk measure recovers, i.e., bounces back, when the risk-acceptance set is breached. We develop the corresponding stochastic calculus by establishing representation theorems for expected time-derivatives of solutions to backward stochastic differential equations (BSDEs) with jumps, evaluated at stopping times. These results reveal that the resilience rate can be represented as a suitable expectation of the generator of a BSDE. We analyze the main properties of the resilience rate and the formal connection of these properties to the BSDE generator. We also introduce resilience-acceptance sets and study their properties in relation to both the resilience rate and the dynamic risk measure. We illustrate our results in several canonical financial examples and highlight their implications via the notion of resilience neutrality.