Recovery rates: Uncertainty certainly matters

Gambetti, Paolo; Gauthier, Geneviève; Vrins, Frédéric

doi:10.1016/j.jbankfin.2019.07.010

Cited by 31 publications

(47 citation statements)

References 44 publications

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“…This latter algorithm actually includes the same economic uncertainty index also on specification 1, together with a housing market indicator. It hence appears that the findings of Gambetti et al (2019) about the usefulness of economic uncertainty proxies for modeling recovery rates are also validated in the context of NPLs. We attribute this result to the well-established capability of uncertainty measures to anticipate economic fluctuations (see references in Section 2.4).…”

Section: Variable Importance Rankingsmentioning

confidence: 87%

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Forecasting recovery rates on non-performing loans with machine learning

Bellotti

Brigo

Gambetti

et al. 2021

International Journal of Forecasting

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We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process-prior to the portfolio's sale to the debt collector-are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential.

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Section: Variable Importance Rankingsmentioning

confidence: 87%

“…Eventually (Gambetti et al, 2019). Their usefulness for retail loans is instead first investigated in this study.…”

Section: Business Cycle Variablesmentioning

confidence: 99%

Forecasting recovery rates on non-performing loans with machine learning

Bellotti

Brigo

Gambetti

et al. 2021

International Journal of Forecasting

Self Cite

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“…As it is shown by several references, in fact, economic uncertainty is a key determinant of the economic outlook (Kose and Terrones, 2012;ECB, 2016;Gieseck and Largent, 2016;Ludvigson et al, 2019). Measures of economic uncertainty have also proved to be particularly effective to model bond recovery rates (Gambetti et al, 2019). Their usefulness for retail loans is instead first investigated in this study.…”

Section: Business Cycle Variablesmentioning

confidence: 82%

Forecasting Recovery Rates on Non-Performing Loans with Machine Learning

et al. 2019

Self Cite

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We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process -prior to the portfolio's sale to the debt collector -are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential.

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“…Through different model specifications, including linear regressions, regression trees, and support vector regressions, they conclude that the stock index return, the corporate bond spread, and the unemployment rate are among the most salient determinants of historical recovery rates. Gambetti et al (2019) identify economic uncertainty as an additional factor. They measure economic uncertainty using several proxies, including the VIX, survey-based, and news-based variables.…”

Section: Examples Of Studies Effect Methodsmentioning

confidence: 99%

The Determinants of Market-Implied Recovery Rates

François

2019

Risks

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In the presence of recovery risk, the recovery rate is a random variable whose risk-neutral expectation can be inferred from the prices of defaultable instruments. I extract market-implied recovery rates from the term structures of credit default swap spreads for a sample of 497 United States (U.S.) corporate issuers over the 2005–2014 period. I analyze the explanatory factors of market-implied recovery rates within a linear regression framework and also within a Tobit model, and I compare them with the determinants of historical recovery rates that were previously identified in the literature. In contrast to their historical counterparts, market-implied recovery rates are mostly driven by macroeconomic factors and long-term, issuer-specific variables. Short-term financial variables and industry conditions significantly impact the slope of market-implied recovery rates. These results indicate that the design of a recovery risk model should be based on specific market factors, not on the statistical evidence that is provided by historical recovery rates.

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Recovery rates: Uncertainty certainly matters

Cited by 31 publications

References 44 publications

Forecasting recovery rates on non-performing loans with machine learning

Forecasting recovery rates on non-performing loans with machine learning

Forecasting Recovery Rates on Non-Performing Loans with Machine Learning

The Determinants of Market-Implied Recovery Rates

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