Portfolio Credit Risk and Macroeconomic Shocks: Applications to Stress Testing Under Data-Restricted Environments

Abstract

This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.Portfolio credit risk measurement is greatly affected by data constraints, especially when focusing on loans given to unlisted firms. Standard methodologies adopt convenient, but not necessarily properly specified parametric distributions or simply ignore the effects of macroeconomic shocks on credit risk. Aiming to improve the measurement of portfolio credit risk, we propose the joint implementation of two new methodologies, namely the conditional probability of default (CoPoD) methodology and the consistent information multivariate density optimizing (CIMDO) methodology. CoPoD incorporates the effects of macroeconomic shocks into credit risk, recovering robust estimators when only short time series of loans exist. CIMDO recovers portfolio multivariate distributions (on which portfolio credit risk measurement relies) with improved specifications, when only partial information about borrowers is available. Implementation is straightforward and can be very useful in stress testing exercises (STEs), as illustrated by the STE carried out within the Danish Financial Sector Assessment Program.

Macro-financial linkages and bank behaviour: evidence from the second-round effects of the global financial crisis on East Asia

“…Extensive research has linked bank credit risk, profitability, capital adequacy and credit supply to the overall condition of the economy. A large amount of work has applied macrostress testing (Du¨llmann and Erdelmeier 2009;Segoviano Basurto and Padilla 2007;Sorge and Virolainen 2006) and VAR framework (Alves 2005;Castre´n et al 2008;Jacobson et al 2005) to analyse the relationships between macroeconomic condition and bank credit risk.…”

A Comprehensive Multi-Sector Tool for Analysis of Systemic Risk and Interconnectedness (Syrin)

“…For example, in countries where subsidiaries of foreign banks operate, it might not be possible to get market-based indicators on the subsidiary (such indicators might exist for the consolidated bank, but this is not adequate). In these cases, we have employed supervisory information to estimate a bank's PoD, which indicates the probability that losses experienced by a bank would violate a supervisory-defined capital buffer (Segoviano and Padilla 2006). 39 Probabilities of distress for investment funds.…”

Banking Stability Measures

“…For example, in Segoviano (1998), Segoviano and Lowe (2002), and , we present simple approaches that vary in the degree of sophistication to quantify portfolio credit risk (all of these approaches were based on parametric models, and accounted only for correlations that were fixed through the cycle) and analyze the procyclicality of banking regulation and its implications for financial stability. In Segoviano and Padilla (2006), we present a framework for macroeconomic stress testing combined with a model for portfolio credit risk evaluation, which accounts for linear and non-linear dependencies among the assets in banks' portfolios and their changes across the economic cycle; however, in all these cases, we focus on individual banks' portfolios or in the overall aggregate banking system. In Segoviano (2004, 2006), and Aspachs et al (2006) we focus on systemic risk, making use of average measurements of distress of the system, which do not incorporate banks' distress dependencies, nor their changes across the economic cycle.…”