Financial Stability Report 24
- Dezember 2012.
Financial Stability Report 24 (PDF, 3,3 MB) Dezember 2012.
Management Summary (PDF, 1,8 MB) en 15.12.2012 00:00:00
Austria’s Real Economy: Supported by the Low Interest Rate Environment (PDF, 2,4 MB) en 15.12.2012 00:00:00
How Do Austrian Banks Fund Their Swiss Franc Exposure? (PDF, 2,4 MB) Auer, Kraenzlin, Liebeg. Austrian banks have traditionally issued large volumes of Swiss franc-denominated loans. Although new issuance has virtually stopped since 2008, the outstanding volume (CHF 81 billion at mid-2012) will continue to pose a challenge to financial stability at least in the oming decade. This study examines how Austrian banks have refinanced their Swiss franc positions and how this changed with the onset of the financial crisis. We document the importance and evolution of three main funding sources: (1) the secured and unsecured nterbank money market, (2) Swiss franc-denominated bond issuances, and (3) central bank financing operations. Our findings are that while activity in the unsecured money market almost came to a halt around the collapse of Lehman brothers and the issuance of Swiss franc-denominated bonds also decreased, the cross-border repo market proved resilient. Moreover, an important role in dealing with the funding drought was played by central bank operations, namely repo operations by the Swiss National Bank (SNB) and swap facilities provided by the SNB and the ECB. en foreign currency loan, banking supervision, banking sector stability, lender of last resort, refinancing, interbank market E52, E58, F33, F36, G21 15.12.2012 00:00:00
Contagiousness and Vulnerability in the Austrian Interbank Market (PDF, 2,8 MB) Puhr, Seliger, Sigmund. The purpose of this paper is to analyze (hypothetical) contagious bank defaults, i.e. defaults not caused by the fundamental weakness of a given bank but triggered by failures in the banking system. As failing banks become unable to honor their commitments on the interbank market, they may cause other banks to default, which may in turn push even more banks over the edge in so-called default cascades. In our paper we distinguish between contagiousness (the share of total banking assets represented by those banks that a specific bank brings down by contagion) and vulnerability (the number of banks by which a bank is brought down by cascading failures). Our analysis consists of three steps: first, we analyze the structure of the Austrian interbank market from end-2008 to end-2011. Second, we run (hypothetical) default simulations based on Eisenberg and Noe (2001) for the same set of banks. Finally, we estimate a panel data model to explain the (hypothetical) defaults generated by these simulations with the underlying structure of the network using network indicators that reflect (i) the network as a whole, (ii) a subnetwork or cluster, and (iii) the node level based on banks’ interbank lending relationships. As a result we find strong correlations between a bank’s position in the Austrian interbank market and its likelihood of either causing contagion or being affected by contagion. Although our analysis is based on a dataset constrained to the interbank market of unconsolidated Austrian banks, we believe our findings could be verified by analyzing other banking systems (albeit with a different model calibration). Given the importance of identifying systemically important banks for the formulation of macroprudential policy, we believe that our analysis has the potential to improve our assessment with regard to second-round effects and default cascades in the interbank market. en Interbank market, network indicators, contagion, panel analysis C23, G21, D85, G01 15.12.2012 00:00:00
Clustering Austrian Banks’ Business Models and Peer Groups in the European Banking Sector (PDF, 2,2 MB) Ferstl, Seres. As the European banking sector is becoming increasingly intertwined, the degree of interdependence is also rising. Consequently, it is key to conduct comparisons for a timely identification of emerging patterns of this development. Furthermore, the product range of banks has expanded so that heterogeneity across the banking sector has also been growing rapidly. This rising heterogeneity makes it increasingly impractical to carry out comparisons on an aggregate level. A more efficient approach is identifying one or ore ”common denominators” of similar banks and establishing groups of banks which share this (these) common denominator(s). In this paper, we consider the business models of banks as one such common denominator, which can be described by a set of variables. These variables span a high-dimensional space where each bank represents a point, which can be measured by a statistical distance. Points close to each other may constitute a group, while points distant from these points will not belong to that group. Therefore, the objective of this study is, on the one hand, to define an efficient set of variables correctly reflecting the business models of banks and, on the other hand, to find subsets of high similarity. By applying statistical clustering techniques we aim to understand banks’ business models, thereby gaining new insights into the design of the European banking sector and, in particular, identifying peer groups relevant to the top Austrian banks. Assessing the distribution of risk and identifying certain business patterns within those groups allows a meaningful ranking of Austrian banks in comparison to their European competitors.2 The analysis in this paper is conducted on the basis of a purely quantitative methodology and the results should be interpreted accordingly. en Austrian banks, cluster analysis, data-driven decision support C02, C44, C58 15.12.2012 00:00:00
Annex of Tables (PDF, 1,7 MB) en 15.12.2012 00:00:00
Notes (PDF, 126 kB) en 15.12.2012 00:00:00