Assessing the shape of the distribution of interest rates: lessons from French individual data
BibTeX
@MISC{Lacroix_assessingthe,
author = {Renaud Lacroix and Banque De France},
title = {Assessing the shape of the distribution of interest rates: lessons from French individual data},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
This paper considers the problem of fitting a finite Gaussian mixture with an unknown number of components to interest rates provided by a sample of credit institutions. Indeed, mixture models are able to represent arbitrarily complex probability density functions distributions, and hence outperform the usual non parametric kernel density estimates in several ways. Therefore, this tool enables us to improve our understanding of the credit market. The data are extracted from individual contracts and gathered on a quarterly basis. They provide a loan-by-loan disaggregation of average rates at bank level available in the monthly MIR reports. Loans to non-financial firms and to households are splitted up into eleven categories defined by instrument and maturity of loans. Each category is analyzed separately. Two major econometric issues are dealt with: how to estimate the number of components, and how to estimate the parameters defining the model. Several techniques are compared, which allows us to show how the results may be sensitive to the methodology used, especially when sample survey issues are properly taken into account: for instance, the model is enhanced in order to include discrete components which arise from clusters in the distribution. In a second step, this set-up allows us to estimate precisely the modes of the distribution. Then, these modes are interpreted as resulting from the segmentation of the credit market. We illustrate this topic through a kind of scoring of credit institutions based on ex-post probabilities for loans to be in the
Keyphrases
interest rate french individual data credit institution credit market bank level major econometric issue finite gaussian mixture several technique monthly mir report sample survey issue ex-post probability non-financial firm average rate loan-by-loan disaggregation discrete component quarterly basis mixture model several way complex probability density function distribution individual contract unknown number second step eleven category
