Earticle

초록

영어

This paper presents a dynamic multi-factor model using macroeconomic and latent risk factors for loan portfolio credit loss distribution. The latent risk factors are grouped based on three levels, global risk factor across the whole commercial banking system, parent-sectoral risk factors for the intermediate aggregate level and individual sectoral risk factors. For the purpose of estimation, the dynamic multi-factor model is presented as a state space model. For U.S. banking system, we nd that the global and sector-wide frailty risk factors and their spillovers aects the variation of loan defaults in addition to the real GDP growth rate. The credit loss distribution of loan portfolio over a specic risk horizon is generated by Monte Carlo simulation given on the estimated parameters and risk factors and the economic capital is measured using a quantile estimator. Given economic capital estimate, we show the risk contribution of individual sectors and risk factors by combining the Hoeding decomposition with Euler capital allocation rule. The total credit loss is represented as the additive sum of marginal losses due to loan sectors, risk factors and all possible interaction terms.

목차

Abstract
 1 Introduction
 2 Charge-o˙s in U.S. Commercial Banking System
 3 Dynamic Multi-Factor Framework
  3.1 Model speci˝cation
  3.2 State-space model representation
  3.3 Extension with macroeconomic factors
 4 Empirical Analysis
  4.1 Without macroeconomic factors
  4.2 With macroeconomic factors
 5 Forecasting of Economic Capital and Risk Attribution
  5.1 Forecasting of economic capital
  5.2 Risk contribution of loan sectors
  5.3 Risk contribution of risk factors
 6 Conclusion
 References
 Appendix

저자

• Yongwoong Lee [ Finance Discipline Group, UTS Business School, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia ]
• Ser-Huang Poon [ yManchester Business School, Crawford House, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom ]

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  한국재무학회 학술대회

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  2006~2024

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  KDC 325 DDC 330