We employ a novel modeling approach to capture the impact of the COVID-19 pandemic on sectoral insolvency rates in Austria. Turnover shocks derived from a macroeconomic scenario generate stress to firms’ profits and cash flows. Over time, both the equity and the liquidity (cash and bank) positions deteriorate, which causes insolvencies if firms fall under certain thresholds. Our model builds on data for nonfinancial incorporated Austrian enterprises available from the BACH and SABINA databases. Since only two firm-level variables (equity ratio, cash and bank) are available at sufficient coverage, we generate a hypothetical firmlevel dataset for 17 NACE 1 sectors by using a Monte Carlo simulation. The granularity of our model allows us to assess the impact of mitigating measures implemented in light of the COVID-19 shock. Such measures serve to cushion the loss of companies’ revenue and households’ income triggered by the COVID-19 containment measures. Put differently, they are meant to minimize the damage resulting from the deliberate temporary reduction in economic activity. In our analysis, we only investigate measures aimed at firms. These measures include equity injections via grants and subsidies (e.g. short-time work), longterm payment deferrals (e.g. credit guarantees) and short-term payment deferrals (e.g. social security contributions). We used all available data sources to calibrate the mitigating measures, with August 31, 2020, as cutoff date. The model indicates a marked increase of COVID-19-induced insolvency rates, but mitigating measures reduce such insolvencies substantially. Without mitigating measures, the insolvency rate would rise to 5.8% by the end of 2020, more than quintupling its pre-crisis average (2017–2019: 1.0%). By end-2022, 9.9% of all Austrian firms would fail, which corresponds to an annual insolvency rate of 3.3%. With mitigating measures in place, the insolvency rate is significantly lower, reaching 2.1% by end-2020, and 6.9% by end-2022. Projected insolvency rates should be interpreted with caution. The merit of this novel approach, however, lies less in the calculated sectoral insolvency rates themselves, but in the model’s capacity to compare and rank the efficiency and efficacy of various mitigating measures. As to the current measures, we, for instance, find that credit guarantees appear most effective, followed by fixed cost support and short-time work. In the short term, delayed filing for insolvency is most efficient, but is set to mostly reverse itself in 2021, once public institutions recommence their usual practice. At the OeNB, the model has also been used to assess implementation delays and the extension of mitigating measures. We intend to continuously extend the model, both in terms of its core functionality and the calibration of mitigating measures to address questions from (1) a macroeconomic perspective, in particular the loss of productive capacities (potential output), (2) a fiscal policy perspective, to estimate the costs of mitigating measures, and (3) a macro- and microprudential banking supervisory perspective, to provide a basis for estimating credit default probabilities for the banking system.

In response to the COVID-19 pandemic, many governments around the globe have imposed strict containment measures to prevent the further spreading of the virus. While saving lives, such lockdowns have also led to the largest peacetime economic shock since the Great Depression of the 1930s. To lessen the blow, governments have been complementing containment measures with mitigating measures. The latter serve to cushion both companies’ and households’ loss of revenue and income suffered during lockdowns, when nonessential economic activity has been suspended or cut to a minimum. In this paper, we only consider mitigating measures addressed to incorporated firms and banks. To assess the vulnerabilities of the Austrian economy and banking system, we follow a two-step approach. First, we have developed a novel model to assess the impact of both containment and mitigating measures on the real economy. This approach combines firm-level micro data from two different databases. To close remaining data gaps, we employ a Monte Carlo simulation to assess the effects of two scenarios based on the current OeNB economic forecast for Austria. We combine these scenarios capturing various policy reactions, i.e. mitigating measures, with firms’ solvency and liquidity positions and ultimately derive sectoral insolvency rates. Second, we use the OeNB’s top-down stress testing framework ARNIE to assess the COVID-19 impact on the banking system. Rather than employing large-scale regression models to derive risk parameters for credit risk, we infer default probabilities of banks’ credit exposure from the Austrian insolvency rates described above. Then, we extrapolate insolvency rates for domestic retail exposures and nondomestic exposures of the Austrian banking system. Here, we assume that individual industry sectors face similar challenges across countries and that country-specific GDP forecasts reflect the overall severity with which individual countries are affected by the pandemic. To this end, we draw on GDP forecasts by the ECB for countries other than Austria as well as country aggregates to calculate scaling factors based on the relative GDP-level deviation. We find that the mitigating measures up to end-August 2020, while effective, only partly offset the COVID-19-induced shock to Austrian firms and banks. They do, however, play an important role in lowering insolvency rates both on aggregate and in the hardest-hit sectors. As a side effect, the mitigating measures taken by the Austrian government and other institutions help improve the outlook for the Austrian banking system, which may benefit indirectly. Moreover, the top-down solvency stress test results show that the Austrian banking system – not only on an aggregate, but also on a disaggregate level – remains well capitalized despite the expected increase in insolvencies. At the time of publication, both COVID-19 containment and mitigating measures will have been extended, which calls into question some of the results of the paper. However, the main conclusion will nevertheless hold: only a substantial further deterioration of the COVID-19 pandemic could put the banking system in a difficult position.

In this paper we present the main concepts and methods of the stress tests that the Oesterreichische Nationalbank conducted in 2013 in close cooperation with the IMF under the latter's Financial Sector Assessment Program (FSAP). We cover solvency and liquidity stress tests as well as, as part of our contagion analysis, the interaction of solvency with liquidity. The paper’s objective is to contribute to the growing literature on applied stress testing by (i) sharing our methodological approaches, in particular innovations to cash flow-based liquidity stress testing, and by (ii) discussing the calibrations employed in what were the most extensive stress tests conducted for Austria in the past five years. Moreover we (iii) provide results at an aggregated level. The 2013 FSAP stress tests for Austria also mark the first public appearance of the OeNB’s new systemic risk assessment tool, ARNIE (“Applied Risk, Network and Impact assessment Engine”). By covering recent methodological as well as operational progress, we also shed light on practical challenges. Finally, we identify the need for further work, in particular with regard to the interaction of solvency and liquidity stress testing, and contagion analysis more generally. analysis more generally.

In the context of stress testing, the interaction between solvency and liquidity stress has received too little attention in academic research as well as in practical application. This paper develops a number of interaction channels from solvency to liquidity and from liquidity to solvency. Thereby it draws on supervisory experience, case studies and the available theoretical literature. It then presents a conceptual framework to quantify the interaction by extending the Austrian central bank's framework for solvency and liquidity stress testing. In an empirical example based on the Austrian banking system the paper investigates the relative materiality of these channels. It uncovers two important trade-offs, one between quantitative impact of channels and the respective model risk/parameter uncertainty and another between conceptual quality and actionable output. The paper concludes that the importance of interaction between solvency and liquidity is too high to simply ignore and suggests approaches to model and to address the aforementioned trade-offs.

A framework to run system-wide, balance sheet data-based liquidity stress tests is presented. The liquidity framework includes three elements: (a) a module to simulate the impact of bank run scenarios; (b) a module to assess risks arising from maturity transformation and rollover risks, implemented either in a simplified manner or as a fully-fledged cash flow-based approach; and (c) a framework to link liquidity and solvency risks. The framework also allows the simulation of how banks cope with upcoming regulatory changes (Basel III), and accommodates differences in data availability. A case study shows the impact of a "Lehman" type event for stylized banks.

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.

This paper presents a second-generation solvency stress testing framework extending applied stress testing work centered on Cihak (2007). The framework seeks enriching stress tests in terms of risk-sensitivity, while keeping them flexible, transparent, and user-friendly. The main contributions include (a) increasing the risk-sensitivity of stress testing by capturing changes in risk-weighted assets (RWAs) under stress, including for non-internal ratings based (IRB) banks (through a quasi-IRB approach); (b) providing stress testers with a comprehensive platform to use satellite models, and to define various assumptions and scenarios; (c) allowing stress testers to run multi-year scenarios (up to five years) for hundreds of banks, depending on the availability of data. The framework uses balance sheet data and is Excel-based with detailed guidance and documentation.

This paper describes a prototype quantitative framework for gauging systemic risk which explicitly characterizes banks' balance sheets and allows for macro credit risk, interest income risk, market risk, network interactions, and asset-side feedback effects. In presenting our results, we focus on projections for systemwide banking assets in the United Kingdom, considering both unconditional distributions and stress scenarios. We show how a combination of extreme credit and trading losses canprecipitate fundamental defaults and trigger contagious default associated with network effects and fire sales of distressed assets. Despite the joint normality of all risk factors, the model generates a bimodal asset distribution.

Direct cross-border lending is an important component in the ongoing process of financial deepening in Central, Eastern and Southeastern Europe (CESEE) and the Commonwealth of Independent States (CIS). We use a loan-level dataset of Austrian banks to study the characteristics as well as the major driving forces of direct cross-border lending in CESEE and the CIS. Direct cross-border lending to nonbanks by Austrian banks expanded rapidly over the last few years; the bulk of loans is extended to corporate customers and is denominated in a foreign cur- rency, with the euro taking a prominent position. By means of a series of univari- ate analyses, we provide support for the relevance of geographic proximity - small and medium-sized banks mainly lend to neighboring countries. Banks' direct lending also seems to follow nonfinancial FDI by Austrian corporates to CESEE and the CIS. We furthermore analyze the interdependencies between direct (i.e. by Austrian headquarters) and indirect (i.e. by local subsidiaries) cross-border lending and find support for a complementary effect between the two. In addi- tion, host country factors such as GDP growth, private sector credit growth, financial intermediation growth and wage growth are also associated with direct lending growth. To be published in Financial Stability Report 17.

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Direct cross-border lending is an important component in the ongoing process of financial deepening in Central, Eastern and Southeastern Europe (CESEE) and the Commonwealth of Independent States (CIS). We use a loan-level dataset of Austrian banks to study the characteristics as well as the major driving forces of direct cross-border lending in CESEE and the CIS. Direct cross-border lending to nonbanks by Austrian banks expanded rapidly over the last few years; the bulk of loans is extended to corporate customers and is denominated in a foreign currency, with the euro taking a prominent position. By means of a series of univariate analyses, we provide support for the relevance of geographic proximity – small and mediumsized banks mainly lend to neighboring countries. Banks’ direct lending also seems to follow nonfinancial FDI by Austrian corporates to CESEE and the CIS. We furthermore analyze the interdependencies between direct (i.e. by Austrian headquarters) and indirect (i.e. by local subsidiaries) cross-border lending and find support for a complementary effect between the two. In addition, host country factors such as GDP growth, private sector credit growth, financial intermediation growth and wage growth are also associated with direct lending growth.

In quantitative financial stability analysis, the link between the macroeconomic environment and credit risk is of particular importance when assessing the risk hidden in loan portfolios. Macroeconomic stress testing, in particular, which aims at measuring the impact of an economic crisis on individual banks or on the entire financial system, depends on means to quantitatively assess this link. Hence, the objective of this paper is to provide a methodological update of the OeNB’s previous credit risk model that improves the capture of the relation between macroeconomic variables and probabilities of default for the main Austrian corporate sectors. In addition to the standard model based on individual macroeconomic variables, the paper explores solutions to two important challenges: first, the challenge related to the exploitation of potential information inherent in a larger macroeconomic data set and second, the problem that accounts for potential nonlinearity in the relation between credit and business cycles. The first issue is addressed via a regression model based on a principal components analysis that takes in a wider range of macroeconomic variables than commonly practiced. The second issue is addressed via a threshold approach. This paper presents the estimation results for the three different models and discusses them on the basis of an illustrative example.

This study investigates the relevance of network topology for the stability of payment systems in the face of operational shocks. The analysis is based on a large number of simulations of the Austrian large-value payment system ARTIS that quantify the contagion impact of operational shocks at participants’ sites. It uncovers that only few accounts are systemically important. We also find that network indicators at the node level can have some explanatory power, which is higher when the analysis focuses on contagion measured by the number of banks with unsettled payments than on that measured by the value of unsettled payments. The explanatory power is, however, lower than that of the more traditional measures of node activity(value and volume) of payments. At this stage of our research, network indicators at the network level seem to be of limited use for stability analysis.

This paper presents the methodology, scenarios and results of the stress tests conducted for the update of Austria's Financial Sector Assessment Program (FSAP) in 2007. The focus of the paper lies in particular on the following two macroeconomic stress scenarios: (a) a regional shock in Central, Eastern and Southeastern Europe hitting Austrian banks through their large exposure in the region, and (b) a global downturn in economic activity causing a deterioration of Austrian banks' domestic loan portfolios, whereby in the second scenario, contagion risk within the Austrian interbank market was also taken into account. Stress test calculations were performed by the OeNB for all Austrian banks (top-down approach) as well as by the six largest Austrian banking groups for their respective exposure (bottom-up approach). The pa- per describes the methodologies for scenario construction and the stress tests themselves and then discusses the scenarios as well as the stress test results in detail, including a comparison of the two approaches. Finally, the paper presents the results of additional sensitivity stress tests for credit risk emanating from foreign currency lending, for the most important catego- ries of market risk and for liquidity risk. Overall, the update of Austria's FSAP 2007 confirmed the results of previous stress testing exercises, in particular for the large Austrian banking groups that show considerable shock resistance mainly as a result of their generally sound capital buffers and high profitability.

In 2002 the Oesterreichische Nationalbank (OeNB) launched in parallel several projects to develop modern tools for systemic financial stability analysis, off-site banking supervision and supervisory data analysis. In these projects the OeNB’s expertise in financial analysis and research was combined with expertise from the Austrian Financial Market Authority (FMA) and from academia. Systemic Risk Monitor (SRM) is part of this effort. SRM is a model to analyze banking supervision data and data from the Major Loans Register collected at the OeNB in an integrated quantitative risk management framework to assess systemic risk in the Austrian banking system at a quarterly frequency. SRM is also used to perform regular stress testing exercises. This paper gives an overview of the general ideas used by SRM and shows some of its applications to a recent Austrian dataset.