Moment Estimators for Autocorrelated Time Series and Their Application to Default Correlations

Journal of Credit Risk 14 (2018), 1-29

25 Pages Posted: 10 May 2017 Last revised: 19 Mar 2018

See all articles by Christoph Frei

Christoph Frei

University of Alberta - Department of Mathematical and Statistical Sciences

Marcus Wunsch

UBS AG

Multiple version iconThere are 2 versions of this paper

Date Written: November 7, 2017

Abstract

In credit risk modelling, method-of-moment approaches are popular to estimate latent asset return correlations within and between rating buckets. However, the autocorrelation that is often present in time series of default rates leads to systematically too low estimations. We propose a new estimator that adjusts for the problems of this autocorrelation and the shortness of the time series, thus eliminating a significant portion of the bias observed in classical estimators. The adjustment is based on convergence and approximation results for general autocorrelated time series, and is easily implementable and nonparametric.

Keywords: autocorrelation, credit risk, latent asset return correlation, method of moments

JEL Classification: G31, C51

Suggested Citation

Frei, Christoph and Wunsch, Marcus, Moment Estimators for Autocorrelated Time Series and Their Application to Default Correlations (November 7, 2017). Journal of Credit Risk 14 (2018), 1-29. Available at SSRN: https://ssrn.com/abstract=2965782 or http://dx.doi.org/10.2139/ssrn.2965782

Christoph Frei (Contact Author)

University of Alberta - Department of Mathematical and Statistical Sciences ( email )

Edmonton, Alberta T6G 2G1
Canada
+1 780 492 3613 (Phone)

HOME PAGE: http://www.math.ualberta.ca/~cfrei/

Marcus Wunsch

UBS AG ( email )

Bahnhofstrasse 45
Zurich, 8001
Switzerland

Register to save articles to
your library

Register

Paper statistics

Downloads
98
Abstract Views
421
rank
267,130
PlumX Metrics