Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4959901 | European Journal of Operational Research | 2017 | 28 Pages |
Abstract
In this work we develop advanced techniques for measuring bank insolvency risk. More specifically, we contribute to the existing body of research on the Z-Score. We develop bias reduction strategies for state-of-the-art Z-Score measures in the literature. We introduce novel estimators whose aim is to effectively capture nonstationary returns; for these estimators, as well as for existing ones in the literature, we discuss analytical confidence regions. We exploit moment-based error measures to assess the effectiveness of these estimators. We carry out an extensive empirical study that contrasts state-of-the-art estimators to our novel ones on over ten thousand banks. Finally, we contrast results obtained by using Z-Score estimators against business news on the banking sector obtained from Factiva. Our work has important implications for researchers and practitioners. First, accounting for nonstationarity in returns yields a more accurate quantification of the degree of solvency. Second, our measure allows researchers to factor in the degree of uncertainty in the estimation due to the availability of data.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Davide Salvatore Mare, Fernando Moreira, Roberto Rossi,