Explaining aggregated recovery rates

Artikel

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Risks ; ISSN: 2227-9091 ; Volume: 10 ; Year: 2022 ; Issue: 1 ; Pages: 1-30 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: credit risk ; dynamic factor model ; Global Credit Data ; Markov switching model ; recovery rate ; regression model

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Höcht, Stephan ; Wieczorek, Jakub ; Zagst, Rudi
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2022

Further information

• DOI: doi:10.3390/risks10010018
• Handle: http://hdl.handle.net/10419/258329
• Last update: 10.03.2025, 11:45 AM CET