EconPapers    
Economics at your fingertips  
 

Explaining Aggregated Recovery Rates

Stephan Höcht, Aleksey Min, Jakub Wieczorek and Rudi Zagst
Additional contact information
Stephan Höcht: Assenagon Asset Management S.A., Zweigniederlassung München, Prannerstraße 8, 80333 München, Germany
Aleksey Min: Chair of Mathematical Finance, Technical University of Munich, Parkring 11, 85748 Garching, Germany
Jakub Wieczorek: Rothesay, The Post Building, 100 Museum Street, London WC1A 1PB, UK
Rudi Zagst: Chair of Mathematical Finance, Technical University of Munich, Parkring 11, 85748 Garching, Germany

Risks, 2022, vol. 10, issue 1, 1-30

Abstract: 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.

Keywords: credit risk; dynamic factor model; Global Credit Data; Markov switching model; recovery rate; regression model (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-9091/10/1/18/pdf (application/pdf)
https://www.mdpi.com/2227-9091/10/1/18/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:10:y:2022:i:1:p:18-:d:721934

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jrisks:v:10:y:2022:i:1:p:18-:d:721934