Loan pricing under estimation risk
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Richard Neuberg
und
Lauren Hannah
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.
Acknowledgements
The authors thank Paul Glasserman for many valuable comments, and Ji Zhu for sharing his implementation of kernelized logistic regression in R.
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© 2017 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- A double clustering algorithm for financial time series based on extreme events
- Improved algorithms for computing worst Value-at-Risk
- Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price
- Company rating with support vector machines
- Loan pricing under estimation risk
Artikel in diesem Heft
- Frontmatter
- A double clustering algorithm for financial time series based on extreme events
- Improved algorithms for computing worst Value-at-Risk
- Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price
- Company rating with support vector machines
- Loan pricing under estimation risk
