TY - BOOK ID - 14277976 TI - Portfolio credit risk and macroeconomic shocks : applications to stress testing under data-restricted environments AU - Segoviano, Miguel A. AU - Padilla, Pablo. AU - International Monetary Fund. PY - 2006 SN - 1451865430 1462330622 1451909969 9786613829078 1452762244 1283516624 PB - [Washington, D.C.] : International Monetary Fund, DB - UniCat KW - Risk. KW - Bank investments. KW - Bank loans. KW - Bank capital. KW - Bank credit KW - Bank portfolios KW - Banks and banking KW - Investments KW - Capital KW - Loans KW - Economics KW - Uncertainty KW - Probabilities KW - Profit KW - Risk-return relationships KW - Banks and Banking KW - Finance: General KW - Financial Risk Management KW - Industries: Financial Services KW - Money and Monetary Policy KW - Mathematical Methods KW - Econometric and Statistical Methods: Other KW - Model Evaluation and Selection KW - Optimization Techniques KW - Programming Models KW - Dynamic Analysis KW - Business Fluctuations KW - Cycles KW - Banks KW - Depository Institutions KW - Micro Finance Institutions KW - Mortgages KW - Financing Policy KW - Financial Risk and Risk Management KW - Capital and Ownership Structure KW - Value of Firms KW - Goodwill KW - International Financial Markets KW - Financial Institutions and Services: Government Policy and Regulation KW - Monetary Policy, Central Banking, and the Supply of Money and Credit: General KW - Finance KW - Financial services law & regulation KW - Banking KW - Monetary economics KW - Credit risk KW - Asset valuation KW - Stress testing KW - Financial regulation and supervision KW - Financial institutions KW - Financial sector policy and analysis KW - Asset and liability management KW - Credit KW - Money KW - Financial risk management KW - Asset-liability management KW - Denmark UR - https://www.unicat.be/uniCat?func=search&query=sysid:14277976 AB - 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. ER -