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

30 Pages Posted: 16 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: March 14, 2018

Abstract

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

Keywords: autocorrelation, credit risk, latent asset return correlation, method of moments (MoM), bias correction.

Suggested Citation

Frei, Christoph and Wunsch, Marcus, Moment Estimators for Autocorrelated Time Series and Their Application to Default Correlations (March 14, 2018). Journal of Credit Risk, Vol. 14, No. 1, 2018. Available at SSRN: https://ssrn.com/abstract=3141168

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

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