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.
Highlights
Academic Editors: Wing Fung ChongThe global financial crisis 2007–2009, which severely affected the world economies, showed the great importance of the appropriate calculation of credit risk in pricing financial contracts
To model the ARR with covariates shifted in time, we assume a linear relationship between the logarithm of aggregated recovery rates and the explanatory variables in the following model ln(ARR(t)) = β 0 + β 1 growth of the GDP (GGDP) Europe(t + 2months) + β 2 GGDP USA(t)
We examine the relation between monthly aggregated recovery rates and different exogenous factors describing the macroeconomic environment, interest-rate movements, and stock markets
Summary
The global financial crisis 2007–2009, which severely affected the world economies, showed the great importance of the appropriate calculation of credit risk in pricing financial contracts. Examine the determinants of bank loans recoveries using the “Ultimate Recovery Database”, a broad database supplied by Moody’s covering various debt instruments from the US defaulted companies These authors report a positive impact of annual GDP growth on RR. Keijsers et al (2018) consider a similar factor model to couple individual recovery rates, three macroeconomic variables including GDP, and individual characteristics of loan and borrower. They are able to explain the cyclicality in recovery rates and default rates driven by latent factors for all observed variables.
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