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Forecasting expected and unexpected losses

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  • Mikael Juselius
  • Nikola Tarashev

Abstract

Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie "unexpected losses". This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks' loss-absorbing resources.

Suggested Citation

  • Mikael Juselius & Nikola Tarashev, 2020. "Forecasting expected and unexpected losses," BIS Working Papers 913, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:913
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    References listed on IDEAS

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    Cited by:

    1. Martin Birn & Renzo Corrias & Christian Schmieder & Nikola Tarashev, 2023. "Banks' credit loss forecasts: lessons from supervisory data," BIS Working Papers 1125, Bank for International Settlements.

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    More about this item

    Keywords

    loss rate forecasts; cyclical turning points; expected loss provisioning; bank capital;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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