Magyar nyelven nem elérhető
Angelos T. Vouldis
- 18 December 2023
- WORKING PAPER SERIES - No. 2883Details
- Abstract
- The analysis of contagion in financial networks has primarily focused on transmission channels operating through direct linkages. This paper develops a model of financial contagion in the interbank market featuring both direct and indirect transmission mechanisms. The model is used to analyse how shocks originating from outside sectors impact the functioning of the interbank market and investigates the emergence of instability in this setting. We conduct simulations on actual interbank bilateral exposures, constructed manually from a supervisory dataset reported by the largest euro area banks. We find that while the impact of direct contagion increases gradually with the shock intensity, the effect of indirect contagion is subject to threshold effects and can increase abruptly when the threshold is exceeded. In addition, the risk posed by indirect contagion has a higher upper bound compared to direct contagion. Finally, we find that in terms of overall impact, the shocks to the value of sovereign debt and non-bank financial institutions represent the most significant risk to the functioning of the interbank market.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
- 27 July 2018
- WORKING PAPER SERIES - No. 2171Details
- Abstract
- Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our method aims to utilise the entire relevant information present in a dataset to detect outliers in an automatized way, a feature that renders the method suitable for application in large dimensional datasets. Our proposed five-step procedure for regression outlier detection entails a robust selection stage of the most explicative variables, the estimation of a robust regression model based on the selected variables, and a criterion to identify outliers based on robust measures of the residuals' dispersion. The proposed procedure deals also with data redundancy and missing observations which may inhibit the statistical processing of the data due to the ill-conditioning of the covariance matrix. The method is validated in a simulation study and an application to actual supervisory data on banks’ total assets.
- JEL Code
- C18 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Methodological Issues: General
C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages