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Accuracy of mortgage portfolio risk forecasts during financial crises

Author

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  • Lee, Yongwoong
  • Rösch, Daniel
  • Scheule, Harald
Abstract
This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Value-at-Risk, VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon. Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different degrees of uncertainty.

Suggested Citation

  • Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:440-456
    DOI: 10.1016/j.ejor.2015.09.007
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    Cited by:

    1. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
    2. Huy Truong Quang & Yoshinori Hara, 2019. "Managing risks and system performance in supply network: a conceptual framework," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 32(2), pages 245-271.
    3. Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.

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