The determinants of bank loan recovery rates in good times and bad - new evidence

TL;DR

This paper investigates how bank loan recovery rates are influenced by economic conditions, using a Markov switching model to distinguish between good and bad times, revealing that key determinants vary across the credit cycle.

Contribution

It introduces a two-state Markov switching model to analyze the changing determinants of loan recovery rates over different economic cycles.

Findings

Recovery rate determinants differ between good and bad economic times.

Probability of default and loan characteristics have varying impacts depending on the credit cycle.

The model helps explain countercyclical behavior in loan recoveries.

Abstract

We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate how the probability of default and certain loan-specific and other variables hold different explanatory power with respect to recovery rates over `good' and `bad' times in the credit cycle. That is, the relationship between recovery rates and certain loan characteristics, firm characteristics and the probability of default differs depending on underlying credit market conditions. This holds important implications for modelling capital retention, particularly in terms of countercyclicality.