Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors |
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| Affiliation: | 1. Finance Discipline Group, UTS Business School, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia;2. Manchester Business School, Crawford House, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom;1. Newcastle University Business School, 5 Barrack Road, Newcastle upon Tyne NE1 4SE, UK;2. CeNDEF, Amsterdam School of Economics, Universiteit van Amsterdam, Roetersstraat 11, 1018 WB Amsterdam, The Netherlands;1. Laboratoire de Mathématiques Nicolas Oresme, Université de Caen Basse-Normandie, Campus II, Science 3, 14032 Caen, France;2. Ecole supérieure de commerce IDRAC, 47, rue Sergent Michel Berthet CP 607, 69258 Lyon Cedex 09, France;3. Department of Statistics, Athens University of Economics and Business, 76 Patission str, 10434, Athens, Greece;1. Sfax National School of Electronics and Telecommunications, Sfax Technopark, BP 1163, CP 3018, Tunisia;2. Laboratory of Probability and Statistics. Sfax University, Sfax Faculty of Sciences, BP 1171, Tunisia;1. Swiss Seismological Service (SED), Swiss Federal Institute of Technology of Zurich (ETHZ), Switzerland;2. Department of Earth, Ocean and Ecological Sciences, University of Liverpool, UK |
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| Abstract: | This paper presents a dynamic portfolio credit model following the regulatory framework, using macroeconomic and latent risk factors to predict the aggregate loan portfolio loss in a banking system. The latent risk factors have three levels: global across the entire banking system, parent-sectoral for the intermediate loan sectors and sector-specific for the individual loan sectors. The aggregate credit loss distribution of the banking system over a risk horizon is generated by Monte Carlo simulation, and a quantile estimator is used to produce the aggregate risk measure and economic capital. The risk contributions of the individual sectors and risk factors are measured by combining the Hoeffding decomposition with the Euler capital allocation rule. For the U.S. banking system, we find that the real GDP growth rate, the global and sector-wide frailty risk factors and their spillovers significantly affect loan defaults, and the impacts of the frailty factors are not only economy-wide but also sector-specific. We also find that the frailty risk factors make more significant risk contributions to the aggregate portfolio risk than the macroeconomic factors, while the macroeconomic factors help to improve the accuracy and efficiency of the credit risk forecasts. |
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| Keywords: | Risk contribution Conditional value-at-risk Euler capital allocation Hoeffding decomposition Default probability |
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