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Loan pricing under estimation risk

Author

Listed:
  • Neuberg Richard

    (Department of Statistics, Columbia University, New York, NY 10027, USA)

  • Hannah Lauren

    (Department of Statistics, Columbia University, New York, NY 10027, USA)

Abstract

Financial product prices often depend on unknown parameters. Their estimation introduces the risk that a better informed counterparty may strategically pick mispriced products. Understanding estimation risk, and how to properly price it, is essential. We discuss how total estimation risk can be minimized by selecting a probability model of appropriate complexity. We show that conditional estimation risk can be measured only if the probability model predictions have little bias. We illustrate how a premium for conditional estimation risk may be determined when one counterparty is better informed than the other, but a market collapse is to be avoided, using a simple example from pricing regime credit scoring. We empirically examine the approach on a panel data set from a German credit bureau, where we also study dynamic dependencies such as prior rating migrations and defaults.

Suggested Citation

  • Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    • Handle: RePEc:bpj:strimo:v:34:y:2017:i:1-2:p:69-87:n:4
    DOI: 10.1515/strm-2016-0005
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    References listed on IDEAS

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