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Estimating the probability of a non-Markovian rating transition from partially unobserved histories
Journal of Risk Management in Financial Institutions,
12
(3),
256-267
(2019)
Abstract
There is substantial evidence that bank rating data display non-Markovian structures. We introduce a non-Markovian parameter in a simple model for rating transition histories. Accounting for the frequent statistical obstacle of partially missing transitions, we make use of the expectation maximisation (EM) algorithm to estimate all model parameters and find a marked non-Markovian effect for our data.
Keywords: rating transitions; PD; missing data; non-Markovian process; EM algorithm
The full article is available to subscribers to the journal.
Author's Biography
Rafael Weißbach has a diploma in mathematics from Göttingen University, a doctorate in statistics and a venia legendi in econometrics from the Technische Universität Dortmund. He worked full-time from 2001 to 2004 as a risk analyst and portfolio manager in the credit risk management division of an international bank. His current interests lie in modelling credit risk-related parameters such as rating migration matrices and default correlations. Rafael Weißbach has published in the Journal of the American Statistical Association and the Journal of Econometrics and Biometrika, among others. Since 2009, he has been an associate professor of statistics and econometrics in the Department of Economics at the University of Rostock in Germany. His forthcoming textbook, Introduction to Financial Statistics (German language), will be published in May 2019 by Springer.
Friederike Schmal is currently a research assistant and doctoral candidate at the Institute of Econometrics and Economic Statistics at the University of Münster. She studied at the universities of Rostock in Germany and Lund in Sweden and was best graduate (MSc) in business mathematics of her cohort.
Citation
Weißbach, Rafael and Schmal, Friederike (2019, June 1). Estimating the probability of a non-Markovian rating transition from partially unobserved histories. In the Journal of Risk Management in Financial Institutions, Volume 12, Issue 3. https://doi.org/10.69554/XGHC4305.
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