Loan pricing under estimation risk

Neuberg Richard () and Hannah Lauren ()
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Neuberg Richard: Department of Statistics, Columbia University, New York, NY 10027, USA
Hannah Lauren: Department of Statistics, Columbia University, New York, NY 10027, USA

Statistics & Risk Modeling, 2017, vol. 34, issue 1-2, 69-87

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.

Keywords: Estimation risk premium; credit scoring; default probability prediction (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/strm-2016-0005

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