Liquidity Stress Testing in Asset Management -- Part 1. Modeling the Liability Liquidity Risk

TL;DR

This paper develops statistical models for liability liquidity risk in asset management, focusing on redemption shock estimation, risk measurement, and translating market stress scenarios into fund liability impacts, addressing a gap in standardized models.

Contribution

It introduces new mathematical and statistical approaches for modeling redemption shocks and liability risk, including zero-inflated models and factor models for stress scenario translation.

Findings

Proposed zero-inflated models for redemption shocks

Calibrated models for normal and stressed scenarios

Factor models linking market stress to liabilities

Abstract

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal to develop mathematical and statistical approaches, and provide appropriate answers. In this first part that focuses on liability liquidity risk modeling, we propose several statistical models for estimating redemption shocks. The historical approach must be complemented by an analytical approach based on zero-inflated models if we want to understand the true parameters that influence the redemption shocks. Moreover, we must also distinguish aggregate population models and individual-based models if we want to develop behavioral approaches. Once these different statistical models are calibrated, the second big issue is the risk measure to assess normal and stressed redemption shocks. Finally, the last issue is to develop a factor model that can translate stress scenarios on market risk factors into stress scenarios on fund liabilities.