Litigation risk assessment: a novel quantitative recency–frequency–monetary model

Guodong Shi, Jianjie Huang, Jiahao Hou and Zeliang Zhang

Abstract

This paper uses the recency–frequency–monetary (RFM) model and the Kealhofer– McQuown–Vasicek (KMV) model to assess the litigation risk and credit risk of companies, respectively, and it employs the panel vector autoregression model to study their interrelationships. We first propose an RFM-based litigation risk assessment model. To validate the risk model and verify its robustness, we further adopt the KMV model for accuracy testing and perform robustness checks through data sets for different periods. Our research findings indicate a correlation between the litigation risk calculated by the RFM model and the credit risk calculated by the classical KMV model. Specifically, companies with higher litigation risk tend to exhibit higher credit risk. Moreover, there exists a bidirectional causal relationship between a company’s litigation risk and credit risk, with each being a Granger cause of the other. This suggests that when comprehensively evaluating a company’s overall risk, the coupling relationship between its litigation risk and credit risk must be fully considered.