Bayesian inference for predicting the default rate using the power prior
- Title
- Bayesian inference for predicting the default rate using the power prior
- Author
- 김성욱
- Keywords
- Default rate; Bayesian approach; power prior; AR(1) model; historical data; Gibbs sampling
- Issue Date
- 2006-12
- Publisher
- 한국통계학회
- Citation
- CSAM(Communications for Statistical Applications and Methods), v. 13, No. 3, Page. 685-699
- Abstract
- Commercial banks and other related areas have developed internal models
to better quantify their financial risks. Since an appropriate credit
risk model plays a very important role in the risk management at financial
institutions, it needs more accurate model which forecasts the credit losses,
and statistical inference on that model is required. In this paper,
we propose a new method for estimating a default rate. It is a Bayesian approach
using the power prior which allows for incorporating of historical data to estimate
the default rate. Inference on current data could be more reliable if there exist
similar data based on previous studies. Ibrahim and Chen (2000) utilize these data
to characterize the power prior. It allows for incorporating of historical data
to estimate the parameters in the models. We demonstrate our methodologies
with a real data set regarding SOHO data and also perform a simulation study.
- URI
- http://kiss.kstudy.com/thesis/thesis-view.asp?key=2582538https://repository.hanyang.ac.kr/handle/20.500.11754/108869
- ISSN
- 2287-7843
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED MATHEMATICS(응용수학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML