Austria
Publication of Financial Sector Assessment Program Documentation—Technical Note on Stress Testing the Banking Sector
Author:
International Monetary Fund. Monetary and Capital Markets Department
Search for other papers by International Monetary Fund. Monetary and Capital Markets Department in
Current site
Google Scholar Close
Publication Date:
21 Jan 2014
eISBN:
9781484377000
Language:
English
Keywords:
ISCR; CR; central bank; interbank market; sensitivity analysis; banking sector; foreign currency; liquidity position
Download PDF (12 MB)

This Technical Note discusses key results of stress testing of the banking sector in Austria. The Austrian banking system is in a recovery phase following the 2008–2009 global financial crisis. Stress testing results suggest that Austrian banks, on aggregate, have sufficient capital buffers to withstand severe but plausible shocks from adverse macroeconomic developments. Under the most severe scenario, the estimated total capital shortfall amounts to 1 percent of GDP. The results of the solvency stress test reflect comfortable initial capital buffers built in response to the crisis, in part because of de-risking of balance sheets, and in part owing to banks’ recapitalization efforts through increased retained earnings.

Abstract

This Technical Note discusses key results of stress testing of the banking sector in Austria. The Austrian banking system is in a recovery phase following the 2008–2009 global financial crisis. Stress testing results suggest that Austrian banks, on aggregate, have sufficient capital buffers to withstand severe but plausible shocks from adverse macroeconomic developments. Under the most severe scenario, the estimated total capital shortfall amounts to 1 percent of GDP. The results of the solvency stress test reflect comfortable initial capital buffers built in response to the crisis, in part because of de-risking of balance sheets, and in part owing to banks’ recapitalization efforts through increased retained earnings.

Introduction1

1. The Austrian stress testing exercise takes place during a period of gradual economic recovery following a period of financial turbulence.2 The equity base of the Austrian banking system on the whole has strengthened, the liquidity situation has improved, and profits have firmed up following the Austrian government capital injections,3 increased reliance on decentralized funding models, and the steady recovery of the CESEE region.4

2. The Austrian banking system has a commercial banking focus with net interest income as the key source of profits (Figure 1). Net interest income amounted to almost three times the income from securities holdings in 2012.5 The breakdown of Austrian banks’ securities portfolio tilts towards fixed income instruments (two thirds), followed by Treasury bills and central banks’ eligible instruments (one fourth), and shares and other variable-yield securities (10 percent). Following tumbling profits in 2011, mainly driven by a step-up in securities loss provisions—including losses against participations in affiliated companies, recent developments point at a recovery of after-tax profits, in spite of the denting effects caused by the financial transaction tax introduced in 2011.6 Return on assets has also picked-up in 2012 standing at 0.3 percent.

Figure 1.
Figure 1.

Profit Breakdown for the Austrian Banking System

(in billion euros)

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF staff estimates

3. The outlook assessment of expected profits is not straightforward given the diversity of activities and exposures of Austrian banks. While profit projections for the three large internationally active Austrian banks hinge on developments in the CESEE region, banks with a domestic retail focus are heavily reliant on the prospects for the Austrian economy. Each of these two groups accounts for about 45 percent of banking system assets. On the other hand, the prospects of (partially) nationalized medium-sized banks, representing about 7 percent of total assets, are mainly linked to the effectiveness of recovery and resolution plans already in train.

4. Baseline forecasts for Austria and the CESEE region show a macroeconomic upturn, albeit at a lower growth rate than before the crisis (Figure 2).7 While Austria’s macroeconomic fundamentals compare favorably with the rest of the euro area, growth remains subdued in 2013, gradually picking up in 2014–2015. The medium-term growth prospects for the CESEE region, although lower than prior to the crisis, remain stronger than those for advanced economies.8

Figure 2.
Figure 2.

Baseline Growth WEO Forecast as of October 2012

(in percent)

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: WEO database.Note: The chart shows the annual projections for real GDP growth for Austria, the most relevant CESEE countries (in terms of Austrian banks’ exposure as of June 2013), and the BIS-weighted CESEE regional projections.

5. The Austrian banking system presents a diversity of business models and corporate structures. The banking sector is comprised of more than 800 unconsolidated institutions, with total consolidated banking sector assets amounting to about €1.1 trillion, or more than 3.5 times GDP in 2012 Q3. The three large internationally active banks account for almost half of total bank assets. The banking system can be divided into a few broad categories based on legal form and traditional business focus. These are, in order of size of assets: joint stock banks, cooperatives banks, savings banks, regional banks and other institutions (Table 1). Many Austrian banks have a multi-tier corporate structure. Cooperatives banks are owned by their depositors and include institutions that were initially set up to promote lending in industrial and agricultural sectors, for example the Volksbanken and Raiffeisen banking groups respectively.9 Savings banks have a somewhat different structure, in which the primary banks partially own the apex institution and there is a cross-guarantee on the liabilities of the group.

Table 1.

Austria FSAP Update: Main Recommendations on Stress Testing

article image

“Immediate” is within one year; “near-term” is 1–3 years; “medium-term” is 3–5 years.

6. Austrian banks have sizable cross-border linkages, especially in the CESEE region, but are not significantly exposed to European peripheral countries. Direct and cross-border lending exposures amount to nearly €460 billion, of which €326 billion are to CESEE countries or under 30 percent of overall banking system assets, mainly through an extensive network of local subsidiaries.10 This diversified regional exposure is highly concentrated in the large internationally active banks (for over 80 percent of aggregate subsidiary assets). Conversely, Austrian banks are primary lenders in CESEE countries, with market shares above one-third in Slovakia, Bosnia, Romania, Albania, and the Czech Republic. On the other hand, foreign-owned banks in Austria represent more than 25 percent of the total banking system by assets, and are dominated by one large bank and two mid-sized banks. Austrian banks’ exposure to European peripheral countries fell to €31 billion from €45 billion in 2008, of which 17 percent were claims on the public sector and 24 percent claims on credit institutions in these countries.

7. Although Austrian public debt has increased significantly during the crisis, it stands below the average for advanced economies and compares favorably to other Aaa-rated peers. The public debt ratio is expected to reach a peak in 2013 at around 74 percent of GDP well below the expected 109 percent ratio for advanced economies and 95 percent for the euro area.11 Baseline projections show that the public debt ratio will decline gradually towards pre-crisis levels of 60 percent of GDP supported by a medium-term reduction of general government debt. Government support to the banking system has been significant, including through the nationalization of three medium-sized banks. While the authorities’ fiscal consolidation plans are on track, uncertainties related to the restructuring of the nationalized banks and the realization of contingent liabilities remain.12

8. The improvement of Austrian banks’ liquidity position has been supported by the ECB’s and the SNB’s monetary operations, as well as by the OeNB’s enhanced supervisory and regulatory requirements. Since 2008, banks have continuously improved their liquidity position across major funding currencies. The increase in liquidity buffers has been mainly facilitated by the ECB’s monetary policy, the repo operations conducted by the SNB, and the swap facilities provided by the SNB and the ECB. Reflecting a recent pick-up in deposit growth at Austrian banks, their dependence on ECB financing is, however, relative low relative to their euro zone peers.13

9. The objective of the FSAP stress testing exercise is to assess the resilience of the Austrian banking sector to adverse macroeconomic conditions and severe stress in global funding markets. The solvency test consists of a TD test undertaken by the OeNB collaboratively with the FSAP team conducted on all 585 consolidated banks licensed in Austria. BU solvency stress tests—focusing on market and sovereign risk—were run by the five largest banks (representing about 60 percent of banking system assets).14 A liquidity stress test covering the largest 29 banking institutions (accounting for around 80 percent of total assets), was conducted based on a range of adverse scenarios, broken-down by major currency, and with severe liquidity stress lasting for up to one year. A contagion module assessed the potential for distress in an individual banking institution to create risks to overall financial stability.

10. Major risk factors were included in the stress tests (Figure 3). To assess credit risk from cross-border exposures, country specific macroeconomic scenarios were generated for twenty-two CESEE countries besides the Austrian economy. A global funding scenario reflecting post-Lehman conditions through increased funding costs and restricted market access in FX swap markets, was used to generate solvency effects from negative funding gaps through fire sales. The potential for domino effects was assessed using a network model of the Austrian interbank market. Contagion effects through financial markets were evaluated using the CoVaR methodology.

Figure 3.
Figure 3.

Key Component of the FSAP Stress-Test

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

11. This technical note is organized as follows. Section II outlines the main risk factors affecting the Austrian banking system. The solvency stress test scenarios, methodology, and results are presented in Section III. The calibration and findings of the liquidity stress tests are explained in Section IV. The interplay between liquidity and solvency effects are shown in Section V. This section also contains the contagion analysis conducted to capture the potential for cascading defaults and fire sale externalities based on network analysis. Contagion through financial markets is examined using a market-based CoVaR approach. The conclusion and main recommendations are laid out in Section VI.

Key Risk Factors

12. Drawing on the FSAP team’s assessment of global and domestic key risks, three external shocks were identified (Annex I): (i) shocks arising from a global slowdown or a resurgence of the euro area sovereign debt crisis from incomplete policy commitments, subdued private domestic demand or frontloaded fiscal consolidation in peripheral countries; (ii) spillovers from the CESEE region due to the escalation of economic imbalances or the realization of political risk in large-exposure countries, and (iii) severe funding stress in global markets including the inability to issue short-term debt or trade cross-currency swaps.

13. A complementary market-based approach was used by the FSAP team to yield insights on the main vulnerabilities affecting large banks’ solvency risk (Annex II). To drill down on market-perceived vulnerabilities, the FSAP team conducted an econometric analysis on the credit risk of the largest listed banks. The analysis looked at the main determinants of major Austrian banks’ solvency risk. The risk factors examined belong to three main categories: (i) Austria macro-financial variables including revisions to market forecasts; (ii) contagion from the main sub-regions in the CESEE, i.e., New EU Member States 2004 (NMS-04), New EU Member States 2007 (NMS-07), Southeastern Europe (SEE), and the Commonwealth of Independent States (CIS).15 To capture CESEE-specific factors, we proxy contagion by credit stress in the region which is unrelated to either domestic or global developments; and (iii) global risk factors, including changes in the state of the global economy as well as developments across asset classes from investors’ portfolio reallocation under stress. The latter include estimated time-varying risk premia from the US equity and fixed income markets, namely equity premium, volatility risk premium, and term premium. The approach builds on Longstaff et al (2011) and uses monthly changes in the credit default swap (CDS) market and in Moody’s KMV expected default frequencies (EDF) to provide a direct measure of changes in market perception of solvency risk. The sample covers the two most widely traded Austrian bank stocks for which CDS spreads or EDF quotes are available. All variables are expressed in monthly changes. The exact definition of the variables is contained in Annex II Table 1. For each bank we regress monthly changes in CDS spreads and EDF estimates on the set of relevant explanatory variables. The time series starts in October 2007 and ends in October 2012.16 Results are shown in Annex II Table 2.

14. Overall, the major risk factors over the short- and medium-term affecting the stability of the Austrian banking sector are listed below:

  1. Deteriorating asset quality. A sharp slowdown in Austria and the CESEE countries could impact significantly asset quality of banks’ domestic portfolios, cross-border operations, and foreign subsidiaries’ loan book.

  2. Declining profits. While domestic credit growth has lost steam, a protracted growth slowdown in CESEE countries could erode significantly net interest margins. Given the high share of CESEE subsidiaries’ profits in total consolidated net operating profits, a persistent depreciation of local currencies vis-à-vis the Euro could further affect banks’ profitability.17

  3. Credit risk from foreign currency lending. Exchange rate volatility (e.g., CHF) or asset price declines associated to repayment vehicles loans (RPVs) could increase credit risk due to the legacy of banks’ FCLs to Austrian households. The high share of FCLs in CESEE may also weigh on credit quality following a sell-off of domestic currencies. The econometric analysis points at the significance of FX developments in CESEE.

  4. Sovereign risk. Although the risk of adverse feedback loops between Austrian banks and sovereign appears unlikely, sovereign risk perceptions have deteriorated during the financial crisis.18 Also, lower valuations of government bonds in CESEE countries, driven by downward revisions to growth or fiscal slippages, could weigh on banks’ capital positions. Rising term premia, reflecting the unwillingness of market participants to hold long-term paper despite a low short-term interest environment, could further dent securities’ valuations as suggested by the econometric results.

  5. Market risk. A widening in credit spreads on European financial institutions or corporate entities could affect banks’ profitability directly through valuation effects on net open positions or indirectly through an increase in risk weights. This effect comes out significant in the econometric analysis. Also exposure to a broad-based financial market downturn affecting a wide set of risk parameters including interest rates, exchange rates, equity returns, commodities, credit spreads, and counterparty risk may erode banks’ profits albeit the impact is expected to be contained given the commercial focus of the banking system.

  6. Securitization risk. Rapid and abrupt downgrades of structured credit products may have a nontrivial impact on capital adequacy ratios as revealed by the breadth and depth of rating downgrades observed during the global financial crisis.19

  7. Funding/Rollover risk. Rising libor-ois spreads, dry-up of issuance in money markets, and disruptions in foreign exchange swap markets in the face of winding down of swap facilities by the SNB, may affect Austrian banks’ refinancing costs or their ability to rollover maturing contracts leading to potential cascade effects through the interbank market.

  8. Financial contagion. The propagation of financial distress through fire sales, the interbank market or contagion from banks following similar business models may affect the solvency/liquidity position of Austrian banks as suggested by the pick-up in correlation of market performance under stress.

  9. Regulatory changes. Upcoming regulatory changes including the implementation of Basel III capital requirement through the CRD IV/CRR directive20 and the repayment of public participation capital in the context of state aid may add to the above pressures on Austrian banks.

15. Stress tests are linked to the main risks identified above. The macro stress tests cover (i), and (ii); sensitivity analysis assesses (iii) through (vi); liquidity stress tests examine (vii), and contagion analysis looks into (viii). The impact of regulatory changes (ix) is covered in the overall discussion on banks’ capital adequacy assessment.

Solvency Stress Tests

A. Macro Scenarios

16. Solvency stress tests were conducted for the entire Austrian banking system using supervisory and macroeconomic data as of end-2012 over the forecasting period 2013–2015. End-of-year supervisory reported data on a consolidated basis became available in May 2013.

17. A two-pronged approach to solvency stress testing was adopted:

  • Top-down tests conducted collaboratively with the OeNB covering all 585 banks licensed in Austria, based on quarterly baseline projections generated by the Austrian Quarterly Model (AQM) for Austria.21 Baseline forecasts for CESEE individual countries were generated by the FORCEE model developed by the OeNB.22 These forecasts are broadly consistent with the IMF WEO forecasts for the region published in October 2012.23

  • “Light bottom-up” tests conducted by the largest five banks (representing about 60 percent of banking system assets) focusing on market risk (with a comprehensive coverage of major risk factors), and sovereign risk (covering all sovereign exposures across all maturity buckets on a consolidated basis).

18. The severity of the stress test is in line, or exceeds, that of recent FSAPs as country-specific adverse scenarios were generated for twenty four countries of relevant exposure to Austrian banks.24 Two adverse macro scenarios and one global funding stress scenario were considered (Figure 8):

  • A global shock and intensification of the euro area economic crisis, generating a two-standard deviation shock to Austrian GDP growth and spillover effects to the CESEE/CIS region leading to a deviation from baseline growth of one and a half standard deviation across the region.25 The severity of the shock is applied to the aggregate CESEE region weighted by country-specific exposures of Austrian banks.

  • A severe recession in CESEE/CIS, consisting in aggravated downturns relative to the previous scenario, bringing trend regional growth down by 1.8 standard deviations (together with a two-standard deviation shock to Austrian GDP growth).26

  • A global funding scenario reflecting the acute stress conditions observed in late 2008 when the global financial crisis hit global and Austrian banks including through increased funding costs and restricted market access in FX swap markets.

19. To assess credit risk from cross-border exposures, country specific macroeconomic scenarios were generated for twenty-two CESEE countries. The OeNB solvency stress testing platform offers a high degree of granularity in the breakdown of credit exposures that allows the construction of adverse macro scenario by country of exposure. Specifically, a battery of adverse scenarios were developed for twenty-two countries27 using a G-VAR model developed by the OeNB covering 51 countries and the euro area estimated over 1995–2012. A double-dip shock to real GDP growth from baseline growth trend is applied over the first two years with positive adjustment dynamics during the last year of the stress test horizon.

B. Modeling Approach

20. The approach to credit risk modeling as a function of macroeconomic developments differs across domestic and cross-border exposures. The exposure at default (EAD) from domestic and cross-border credit stood at 56 percent and 44 percent, respectively. Exposures to the CESEE region accounted for about 70 percent of all cross-border exposures.

  • For domestic exposures, a credit risk model links sectoral corporate probabilities of default (PD) in six Austrian corporate sectors to a wide range of observable macroeconomic variables and a latent risk factor (Box 1).

  • A separate satellite model based on country specific CESEE loan loss provisioning ratios (stock and flow ratios) is calibrated to assess credit risk in cross-border operations and foreign subsidiaries.28 Changes in provisioning ratios are used to proxy changes in PDs.

21. The stressed loss given default (LGD) is estimated separately for collateralized and uncollateralized exposures:29

  • For real estate collateral, country-specific haircuts are estimated for CESEE countries based on the elasticity of GDP growth to house prices and the GDP growth path projections under each scenario.30

  • For uncollateralized exposures, a country-specific LGD, capped at 45 percent for Austria, and linked to the World Bank Doing Business Statistics for CESEE countries—with the distribution truncated at 80 percent—is generated under the baseline scenario. This value is stressed through linear increments each quarter reaching a final add-on of 10 percentage points in 2015Q4 under the adverse scenario.

22. During the stress test horizon profits decline significantly mainly driven by a depreciation of host country currencies31 and the contraction of banks’ balance sheet:

  • Weak macroeconomic performance in the macro stress test triggers the depreciation of local CESEE currencies relative to the Euro.

  • The sustained cumulative depreciation reaches 1.5 (2.0) SD under the adverse (severe) scenario in 2013–2014, with a partial and gradual rebound assumed during the last year of the stress test horizon.32

  • This effect is significant as CESEE subsidiaries’ share in total consolidated net operating profits reached over 50 percent in 2012 Q2.33

  • Operating profits decline further triggered by a drop in net interest income caused by performing loans becoming non-performing. It is assumed that all the components of operating profits decline in line with net interest income.34

Overview of the OeNB’s Credit Risk Model for the Austrian Economy1/

The endogenous variables of the credit risk model are quarterly default frequency rates over 1985-2011.2/ The Austrian economy is divided in the following corporate sectors: construction, production, trade, transport, tourism and services. The set of explanatory variables include nineteen macroeconomic time series. For each variable, up to six quarterly lags are considered.”

For each corporate sector, the number of explanatory variables is selected by applying the Forward-Stepwise Selection algorithm.3/ For each number of regressors, the best five models are selected in terms of their explained sum of squares. Each model is estimated with an unobserved component reflecting a latent risk factor according to the following specification:

y i , t = β 0 , i + Σ j = 1 k x j , t β j , i + z i , t λ i + ε i , t z i , t = z i , t − 1 ϕ i + w i , t

where yi is the logit-transformed sectoral default frequency rate for sector i, k is the number of macroeconomic variables, xj, is the jth macroeconomic variable, Zi is the unobserved factor, and εi, and Vi, are uncorrelated error terms.

Aggregate credit risk is driven by both common variables across multiple sectors as well as by sector-specific variables. Common variables include inflation, interest rates, and credit growth; the latter enters with a negative sign suggesting that credit growth is driven mainly by productive investment projects rather than by lenient prudential standards. Sector-specific variables include, for instance, exports in the transport sector, capital investment in the trade sector, and oil prices in the construction sector.

The results suggest that a latent risk factor is only significant in small credit risk models. For credit risk models including more than seven macroeconomic variables, the evidence for a latent risk factor vanishes. This suggests that a broad macroeconomic dataset is able to capture most of the drivers of credit risk. Hence the Austrian credit risk models—given the availability of a wide set of macroeconomic data for the Austrian economy—do not have to rely on latent factors.

1/ Based on Kerbl, S. and M. Sigmund (2011). 2/ Default frequencies are estimated as the ratio of quarterly defaults to the total number of firms drawing on the Kreditschutzverband von 1870 database. 3/ The Forward Stepwise Selection method starts with an intercept and adds the regressors which contribute most to the fitness of the model as measured by the BIC.

C. Sensitivity Analysis

Foreign Currency Lending

23. A sensitivity analysis on foreign currency lending was conducted to quantify the indirect credit risk from a FX shock. Foreign currency loans (FCLs) pose additional risk due to the declining ability to pay of unhedged borrowers spearheaded by the appreciation of foreign currency. The analysis was conducted separately for domestic and CESEE exposures. The methodological approach was a function of the structure of the loans and the availability of data sources.

24. The legacy of FCLs in Austria remains a concern even if new foreign currency lending has come to a halt (Figure 4).35 As of 2012 Q3, FCLs to domestic non-banks amounted to €50.7 billion, corresponding to 15.3 percent of all domestic loans, of which € 34.6 billion were owned by households (share of 25 percent of housing loans) and € 10.0 billion by corporates (share of 7 percent of loans to non-financial corporates).

Figure 4.
Figure 4.

Breakdown of Bank Lending by Borrower

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB

25. Given the predominant structure of domestic FCLs as bullet loans with long remaining maturities,36 credit risk from a FX shock was assessed using an indirect approach (Box 2). About 60 percent of FCLs to households and corporates are arranged as bullet loans, associated with repayment vehicles (RPVs), with 40 percent being amortizing loans. An analysis based on loan loss provisioning data would not be reliable as bullet loans hardly show any default event. Also, mounting credit risks typically propel the conversion of FCLs into Euro loans biasing the analysis. In effect, the provisioning rate on FCLs granted to non-banks stood at 1.1 percent in 2012 Q3, less than one third of that associated to euro loans which may, however, underestimate latent credit risk which could crystallize at maturity.

26. The analysis assumes a protracted appreciation of the Swiss Franc vis-à-vis the Euro. The sensitivity analysis is conducted for Swiss Franc loans. About 90 percent of FCLs to households and corporates are denominated in Swiss Francs. We assume that the Swiss Franc appreciates by 1.5 SD over 2012Q3–2014Q4 with the nominal exchange rate climbing from 1.21 to 1.07 and stabilizing in the last year of the stress test horizon37 (Figure 5).

Figure 5.
Figure 5.

FX Scenario for the Swiss Franc

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: Dealogic and IMF estimates
Figure 6.
Figure 6.

Drivers of Changes in CT1 for the Whole Banking System

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF staff estimates
Figure 7.
Figure 7.

Sensitivity Analysis for the Solvency Stress Test

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF staff calculations

27. In addition, bullet loans associated to RPVs are exposed to market risks that may weigh on the performance of the investing vehicle impairing borrowers’ debt servicing capacity. FCLs linked to RPVs involve the risk that in case of adverse exchange rate developments or capital market underperformance the capital accumulated through the RPV may not suffice to repay the loan at maturity. RPVs are closely associated to FCLs. From the €30.0 billion bullet loans outstanding in September 2012, €25.8 billion were FCLs, of which €24.0 billion were denominated in CHF. On the other hand, 60 percent of all FCLs are linked to RPVs. The risk characteristics of RPVs vary across product categories. About three quarters of RPVs are directly linked to capital market developments with over half of outstanding loans linked to mutual funds-based life insurance instruments.

28. A separate sensitivity test was conducted by the FSAP team to examine the potential rise in estimated projections of baseline funding gaps of RPVs under adverse market developments (Annex IV). The analysis dew on the breakdown of market sensitive investment vehicles across asset classes using tail returns of proxy distributions under the estimated annual payments implicit in the computation of current funding gaps. A combined scenario added distress from market underperformance to FX shocks. The results of this analysis should be interpreted with caution. A number of extensive assumptions had to be made to the many unknowns in the underlying data. Also these figures provide a conservative estimate as the FX shock has been included separately in the sensitivity analysis of the solvency stress test to calibrate indirect credit risk in the domestic portfolio.

Indirect Credit Risk from FCLs in Austria

The modeling framework assumes that FCL borrowers are unhedged. An appreciation of CHF triggers an increase in the value of outstanding debt expressed in EUR by D * ΔFX (where FX is the nominal exchange rate of EUR per CHF). This leads to a rise in the debt-to-income ratio by DI*ΔFX which in turn affects credit losses with an elasticity estimated at 2.5.

The increase in loan-loss provisioning rates can be approached by:

Δ LLPR = − 2.5 * D I * Δ FX

We assume a protracted appreciation of the Swiss France vis-à-vis the Euro with volatility equal to 1.5 SD. The exchange rate path is driven by the square root of t-law:

FX t = FX 0 * exp ( t * 1.5 * σ )

Additional impairments from the equation above are distributed equally over each loan’s remaining maturity. The stress test loss is the accumulated loss over the stress test horizon 2012Q4-2013Q1.

29. Credit risk from FCLs in CESEE countries was assessed drawing on impairment data broken down by currency. A ‘FX boost factor’ defined as the elasticity of changes in loan loss provisioning rates (LLPRs) relative to local currency loans triggered by an appreciation of the FX is applied to all FCLs in Swiss Francs.38 The difference between the LLPR of FCL, assuming they develop like local currency loans, and that including the FX boost effect is computed as the additional losses attributed to the sensitivity test.

30. Cross-rates between local currencies in CESEE countries and Swiss Francs are consistent with the assumptions of the stress test and the sensitivity analysis of domestic FCLs. The projection of local currency relative to the Swiss Franc assumes a compounding effect from the depreciation of local currency vis-à-vis the Euro assumed in the projection of operating profits and the depreciation of the Euro vis-à-vis the Swiss Franc envisaged in the sensitivity test.

Securitization Risk

31. Stress test on securitization positions are applied through an increase in risk weighted assets. Opacity on the underlying credit exposures and non-linear payoffs limit the use of a credit risk modeling approach to these exposures. Instead a credit risk migration matrix is assumed in line with the baseline scenario calibration of the 2011 EBA stress test exercise.

32. Migration matrices are calculated separately for medium-risk and high-risk positions (Table 3). Stressed risk weights are computed as a weighted average of the original risk weights and the migration factors. Regulatory reporting data is available on a single deal basis by product type, underlying asset and geographic distribution.

Table 2.

Financial System Structure

article image
Source: OeNB
Table 3.

Applied Risk Weights under the IRB Approach

article image
Source: EBA 2011 stress testing exercise

33. The impact on banks’ capital ratios is twofold. First, the impact of defaulted exposures is 1,250 percent risk-weighted. Second, stressed risk weights from securitization positions are combined with those from non-securitization assets to compute risk weighted assets.

Sovereign Risk

34. Sovereign risk is measured in the adverse scenario through changes in sovereign yields leading to a repricing of all affected bonds (Annex V). Holdings of government bonds in both the banking book and the trading book are repriced. The scope of sovereign includes: all central governments (but no central banks), all regional governments, and all local authorities.39 We assume that the term structure of sovereign risk shifts upwards for all countries to which Austrian banks are exposed, including sovereign bonds held by CESEE subsidiaries to comply with local liquidity requirements. Haircuts to the banking book are applied to adjusted (marked-to-market) balance sheet values.

35. The approach allows for changes in term premia observed under market stress. In volatile conditions, investors typically require an excess yield to commit to holding a long-term bond instead of a series of shorter-term bonds. The calibration of the sovereign shock includes changes in the slope and curvature of the yield curve associated to historical stress rather than a parallel shift on spreads. When there is no available quote at a specific maturity to derive a valuation haircut, the nearby maturities’ haircuts are interpolated.

36. The sovereign shock is calibrated for fifty eight countries (Annex V. Table 2):

  • For fifty countries, the shock is derived from the 90th percentile of the historical distribution of annual changes of daily yields ranging between 3-month and 30-year time-to-maturity using the Bloomberg-based generic 5-year government bond yields over the period 2005–12. The change in yields is used to reprice all government bonds under a cash-flow approach matching a modified duration formula to each maturity bucket.

  • For Belarus, Bulgaria, Luxembourg, Malta, and Romania, the haircut is computed using extreme returns for the most liquid outstanding international bond as of Dec 2012, given the limited time series of the generic yield curve (Annex V Table 1).40 For Cyprus, the haircut is calibrated from the sovereign yield curve as of Dec 2012. For Estonia only international loans were outstanding at end-2012.

Market Risk

37. Market risk sensitivity analyses were run by the largest five banks as part of the “light bottom-up approach.” Parameters are applied to trading book positions as of 31 December 2012. Valuation effects are reported for each risk category (interest rates, FX, etc.) individually, leaving the parameters of the other risk categories unchanged, as well as in total (changing the parameters of all risk categories at the same time).

38. Interest rates and FX rates were calibrated in consistency with the macro stress scenario (Table 5):

Table 4.

Haircuts for Unencumbered Eligible Collateral

(in percent of collateral value after haircut)

article image
Source: OeNB. Note: AC1 includes central government or central bank securities; AC2 other public sector entities, supranational institutions, Agencies and Jumbo-Pfandbriefe; AC3 non-financial corporate, Pfandbriefe, secured bank bonds, structured covered bonds, or multi-cedula; AC4 credit institutions and financial corporates; and AC5 asset-backed securities.
Table 5.

Market Risk Parameters

article image
article image
article image
Source: OeNB and 2011 EBA stress test exercise.

  • Volatilities of daily changes in interest rates were scaled by a factor of two and were additionally scaled over a one-year time horizon.41 Eastern Europe interest rates were determined by an equally weighted basket of CZK and PLN.42 Other non-emerging markets’ interest rates were determined by an equally weighted basket of CHF, JPY and AUD. Other parameters in the interest rates category (Asia, volatilities) were calibrated in line with the EBA 2011 stress test.

  • FX rates were defined vis-à-vis the Euro. The scenario assumes a Euro depreciation in line with the scenario used in the solvency stress test and the FCL sensitivity analysis. Emerging Markets were defined as an equally weighted basket of eight CESEE/CIS countries. Other non-emerging markets’ FX rates were determined by an equally weighted basket of CHF, AUD and CAD.

  • Concerning FX volatilities, the EBA 2011 calibration is considered for rates involving major currencies (i.e. non Emerging Market) as well as for rates involving at least one Emerging Market currency.

39. Other risk categories include the credit risk factors identified in the FSAP team econometric exercise. Specifically, itraxx high yield Europe, itraxx Europe crossover and itraxx senior financials were included as key risk factors in the sensitivity analysis. Parameters for these credit risk factors as well as for other market risk parameters (Equity, Funds, Commodities, Counterparty) were calibrated in line with the adverse EBA 2011 stress test scenario.

Basel III Implementation

40. Full implementation of Basel III requirements, including front-loading of phase-in capital arrangements and Basel III RWAs, would have the following estimated impact in projected regulatory ratios:

  • CET1 would shed 1.4 percentage points relative to EBA CT1 capital for the whole banking system and 1.6 percentage points for the large international banks. The impact on Basel III Tier 1 would be slightly more significant at 1.7 and 2.2 percentage points for the system and the large international banks, respectively.

  • The impact on the total adequacy ratio is more uncertain depending on the treatment of the €19.8 billion (€10.0 billion) long-term subordinated debt held by the banking system (large international banks) under Basel III. The effect on the CAR could range between 1.9 and 4.0 (2.3 and 4.9) percentage points for the banking system (large international banks).

  • The main driver of the decline in CET1 is the phase-out of participation capital subscribed by the government in the context of state aid (€4.1 billion for the whole banking sector of which €3.0 billion was issued by the largest international banks), which is set to kick-in in January 2018.

D. Solvency Stress Test Results

41. The analysis suggests that Austrian banks benefit from sufficient capital buffers, including under most adverse circumstances (Figures 9–11). The results of the solvency stress test reflect improvement in banks’ initial capital condition, in part because of de-risking of balance sheets, and in part due to banks’ recapitalization efforts through increased retained earnings:

Figure 8.
Figure 8.

Macroeconomic Assumptions for real GDP growth (yoy) in Austria and CESEE

(in percent)

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNBNote: CESEE projections show the June 2012 BIS-weighted country quarterly projections for twenty-three countries in Central, Eastern, South Eastern, and Commonwealth of Independent States.
Figure 9.
Figure 9.

Solvency Stress Test Results—Distribution of Core Tier I

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF estimates. The solvency stress test is conducted over the whole banking sector on a consolidated basis. Box plots include the mean (yellow dot), the 25th and 75% percentile (shaded area), and the 10th and 90th percentiles (whiskers). The line reflects the hurdle rate.
Figure 10.
Figure 10.

Solvency Stress Test Results—Distribution of Tier I

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF estimates. The solvency stress test is conducted over the whole banking sector on a consolidated basis. Box plots include the mean (yellow dot), the 25th and 75% percentile (shaded area), and the 10th and 90th percentiles (whiskers). The line reflects the hurdle rate.
Figure 11.
Figure 11.

Solvency Stress Test Results—Distribution of Total Capital Ratios

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB and IMF estimates. The solvency stress test is conducted over the whole banking sector on a consolidated basis. Box plots include the mean (yellow dot), the 25th and 75% percentile (shaded area), and the 10th and 90th percentiles (whiskers). The line reflects the hurdle rate.

  • Under the most severe macroeconomic scenario, banks representing less than 7 percent of total bank assets would fall below the regulatory threshold. The estimated aggregate capital needed to bring back the capital ratios of these banks above the regulatory minimum amounts to 0.3 percent of total bank assets, or about 1 percent of GDP.

  • Yet, the thin capital buffers in some banks warrant enhanced monitoring (Figures 12–14). The percent of total bank assets under the 6–8 percent core Tier 1 capital bucket increases from about 4.5 percent under the baseline to 17 percent under the severe scenario at the end of the stress test horizon.

  • Although estimated aggregate losses under the adverse scenario would hit severely large internationally active banks, they exhibit a relatively better capital position under baseline projections (Figures 15–17). For instance, the projected losses under the severe scenario would dent large banks’ core Tier 1 ratio by 4.8 percentage points well above the average 2.8 percentage point losses estimated for the Austrian banking system.

Figure 12.
Figure 12.

Solvency Stress Test Results—CT1 Capital Buckets

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB
Figure 13.
Figure 13.

Solvency Stress Test Results—Tier I Capital Buckets

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB
Figure 14.
Figure 14.

Solvency Stress Test Results—Total Capital Ratio Buckets

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB
Figure 15.
Figure 15.

Weighted-Average Core Tier I Capital Ratios

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB
Figure 16.
Figure 16.

Weighted-Average Tier I Capital Ratios

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB
Figure 17.
Figure 17.

Weighted-Average Total Capital Ratios

Citation: IMF Staff Country Reports 2014, 016; 10.5089/9781484377000.002.A001

Source: OeNB

42. The main driver of the decline in regulatory capital is credit risk in CESEE (Figure 6):

  • The projected profit and loss effect from severe macroeconomic stress adds up to 1.5 percentage points driven by credit risk effects.

  • More than two-thirds of expected losses from credit risk come from CESEE exposures while under one-fourth are originated domestically.

  • Full implementation of Basel III requirements, including front-loading of phase-in capital arrangements and Basel III RWAs, would have an impact of 1.4 percentage points led by the phase-out of eligible capital.

43. The large internationally-active Austrian banks would remain, on aggregate, above the regulatory hurdle even under full implementation of Basel III qualitative phase-in arrangements. Following the Supervisory Guidance issued by the OeNB and the FMA on strengthening the sustainability of the large international banks’ business models in March 2012, the top three banks have continued building capital buffers to be compliant with the full implementation of qualitative and quantitative Basel III rules for CET1 as of 2012 Q4. 43 Going forward, large international banks’ projected capital ratios are above the transitional quantitative hurdle rates at end-2015, even under the most severe scenario.

44. Sensitivity analyses, not related to the main scenarios, show a potential for some limited additional indirect credit risk losses (Figure 7). For the whole banking sector, indirect credit risk from FX appreciation in foreign currency loans, both in Austria and CESEE/CIS, add up to 35 bps in CT1 ratios. Market risk from underperformance of market instruments associated to RPVs in the stock of domestic bullet loans would contribute an additional 7 bps. The estimated impact from securitization exposure risk, at 10 bps, is not very significant.

45. For the largest five banks, the BU stress tests confirm the above results and the assessment that market risk is not a major source of vulnerability. Specifically, they suggest that credit risk from FCL appears to have greater impact on capital adequacy than the combined realization of sovereign risk and market risk. The additional credit risk from FCL would depress CT1 capital ratios for the five banks by 41 bps under the most adverse scenario. Sovereign risk is also noteworthy, but less significant, with an additional impact of 51 bps, and with the caveat that these are conservative estimates as portfolio correlations across high-yield assets (i.e., Romania, Ukraine) and safe haven bonds (i.e., Germany, Switzerland) were ignored in the analysis (leading to an overestimation of sovereign risk). On the other hand, market losses from adverse shocks on a wide range of risk factors including interest rates, FX, equity prices, commodities, high yield credit risk, and CVA are insignificant.

Liquidity Stress Tests

46. A set of TD liquidity stress tests were carried out to evaluate the Austrian banking system’s liquidity exposure and its liquidity risk bearing capacity:

  • The liquidity stress tests are conducted on a consolidated basis, on the largest twenty nine banks subject to the weekly cash-flow based liquidity reporting to the OeNB, which account for over 80 percent of total banking system assets.

  • To ensure consistency with the solvency stress test, the liquidity stress tests are based on 2012Q4 data.

  • The analysis is forward-looking. Shocks are applied to contractual as well as behavioral cash-in, cash-out flows, and shocks to the counterbalancing capacity over five maturity buckets (up to 5 days, 1m, 3m, 6m, 12m).

  • Stressed haircuts are calculated on the reported collateral value after the haircut applied by the central bank on eligible collateral. Stressed haircuts are a function of the central bank where the security has been deposited due to differences in eligibility criteria applied by different central banks.

  • Stress tests are conducted separately for six currency buckets (EUR, USD, CHF, GBP, JPY, other). The currency breakdown applies to cash-flows as well as to the counterbalancing capacity.

47. The liquidity stress tests cover three time horizons, five scenarios of market stress, and three approaches to the treatment of the counterbalancing capacity:

  • Three time horizons are considered, i.e., 30-day, 90-day, and one-year. For each horizon, five market scenarios are developed, namely baseline, market mild, market medium, market severe, and combined scenario. Under the latter, an idiosyncratic shock on the rollover of retail and wholesale deposits is built in addition to a capital market shock.

  • For each horizon and market scenario, three approaches to the counterbalancing capacity in terms of severity are considered:

    • Full counterbalancing capacity: including less liquid assets (BBB non-financial corporate bonds, credit claims and other pledgable assets) evaluated at baseline haircuts but excluding committed liquidity lines and liquidity injections from parent banks;

    • Increased focus on market liquidity: haircuts for less liquid assets (BBB non-financial corporate bonds, credit claims and other pledgable assets) increase to 100 percent;

    • Market liquidity: haircuts on unencumbered eligible asses deposited at the Eurosystem increase to 100 percent for securities with ratings below A- (in addition to the restriction under ‘Increased focus on market liquidity’).

48. Under the combined scenario, the assumption that all banks face an idiosyncratic shock at the same time means that the estimated liquidity shortfall yields a conservative estimate. It provides an estimate of the additional liquidity buffer required across the system, such that each bank would weather a substantial combined market and idiosyncratic shock. The combined scenario assumes an idiosyncratic component in the rollover rate of deposits along with a reaction function of banks’ lending decisions that vary across time horizons:

  • Over a 30-day horizon, expected rollover rates of wholesale deposits drop to 90 percent (95 percent) for wholesale (retail) deposits. Banks do not cut expected new loans; instead they cut their expected financial investments by 50 percent.

  • The assumed rollover rate declines to 80 percent (90 percent) for wholesale (retail) deposits under a 90-day shock. Banks shed new investments by 100 percent.

  • The one-year test also assumes a jump in the drawings of committed lines by households and non-financial corporates by 100 percent, a reduction of non-financial loans by 4 percent, and a drop of new unsecured loans to other banks by 100 percent.44

49. The calibration of the liquidity stress test draws on the assumptions built in the solvency stress test and on extensive analysis of the national and international evidence. In addition to the baseline haircuts on eligible collateral deposited at central banks, further haircuts are applied to reflect the combined impact of market and funding stress assumed under the macroeconomic adverse scenario:

  • For collateral deposited at the Eurosystem, haircuts reach up to 100 percent under the market severe scenario and 30-day horizon, depending on the issuer’s rating and the asset class (Table 4);

  • For foreign currency collateral, haircuts under the market severe scenario are calibrated at 10 percent (30-day) and 15 percent (90-day) due to the stricter eligible criteria used by other central banks (e.g. SNB);

  • For tradable assets not deposited with central banks, additional haircuts are a function of the rating of the security.45

50. Also, the PD shifts for Austrian credit claims generated under the adverse scenario are embedded into the credit risk migration constructed to estimate the dry-up of liquidity:46

  • Haircuts on non-tradable collateral, i.e., credit claims, across credit quality ratings, are linked to estimated PD shifts.47 The haircuts increase over the horizon of the liquidity stress to reflect the deteriorating macroeconomic environment.

  • The run-off rate of expected inflows from paper in own maturing portfolio is mapped to estimated PD shifts. The mapping is applied to non-financial corporate bonds with ratings from AAA+ to BBB- deposited at the Eurosystem and to other bonds not deposited at the central bank.

51. The results of the solvency stress test also feed markets’ reaction towards banks’ ability to issuance over the one-year liquidity test:

  • In the first quarter, liquidity dries up for all issuers irrespective of the quality of the underlying collateral. Market uncertainty hinders investors’ ability to differentiate across issuers and instruments.

  • In the second quarter, banks posting high core Tier I ratios under the solvency test regain partial access to funding markets.48 They are able to place 50 percent of their expected issuance.

52. The liquidity stress test carried out as part of the FSAP is more stringent than that implied by Basel III liquidity coverage ratio (LCR):

  • Under the LCR, banks must hold sufficient liquid funds to withstand a 30-day stress scenario. Acute funding stress assumed under the FSAP liquidity stress test extends up to one-year.

  • High quality liquid assets (HQLA) under the LCR are aggregated across currencies. Under the FSAP test, banks are required to hold liquid assets from which they can generate inflows to close the net funding gap in each currency and each maturity bucket.

  • The FSAP test incorporates stricter assumptions on banks’ forward-looking plans albeit with a less severe run-off rate for wholesale deposits. Under the market severe scenario of the FSAP test, funding markets dry-up completely for unsecured interbank funding, short-term debt, long-term secured and unsecured debt, and cross-currency swaps.49 On the other hand, the revised LCR test assumes run-off rates of up to 40 percent (rather than 20 percent) for deposits and unsecured funding from non-retail customers.50

  • Market funding risk captured through stressed haircuts is, on aggregate, more severe under the FSAP liquidity stress test. Haircuts on unencumbered eligible assets deposited at the Eurosystem increase to up to 100 percent for securities with ratings below A-.

53. Austrian banks’ funding structure appears resilient across major currency buckets. Under a scenario comparable with that of recent European FSAPs (Table 6), assuming total closure of the unsecured interbank and FX swap markets, and with substantial haircuts in the counterbalancing capacity,51 the total liquidity shortfall based on the cumulated counterbalancing capacity amounts to only 0.1 percent (30 day horizon), 0.3 percent (90 day horizon), and 0.2 percent (1 year scenario) of total liabilities of the twenty nine banks in the sample.52 The improvement in the liquidity position of Austrian banks can be attributed to enhanced liquidity supervision and monitoring by the OeNB and strengthened supervisory standards of banks’ liquidity risk management.

Table 6.

Liquidity Stress Test Medium Scenario Parameters

article image
Source: OeNB

54. Moreover, OeNB’s liquidity stress tests show that the foreign currency liquidity position of the system has substantially improved since 2008, amid lingering vulnerabilities in CHF funding in some banks:

  • In October 2008, the FMA and OeNB stepped up their liquidity monitoring requiring banks to submit highly granular weekly liquidity reports including contractual and behavioral data by currency and maturity bucket. Weekly liquidity stress tests for monitoring purposes are regularly conducted.53

  • Since 2008, banks have continuously improved their USD and CHF liquidity position and the stress test shows that resilience to a USD and CHF funding shocks is now high.

  • For the CHF liquidity position, the results draw, however, a more nuanced picture. About half of the banks in the sample made substantial progress regarding their resilience to CHF liquidity shocks. Given the maturity structure of the CHF assets of Austrian banks and the limits to reduce the portfolio it is important that the other half further diversify funding sources across counterparties and instruments, and lengthen funding tenors.

Contagion Analysis

55. A contagion module assessed the potential for distress in a financial firm to create risks to overall financial stability. For the simulation of the scenarios, two separate initial conditioning events (shocks) were considered:

  • Rising funding pressures: A ‘global funding scenario’ was laid out to replicate the post-Lehman liquidity strains including a sharp rise in funding cost and credit market freezes. A bank facing a liquidity squeeze engages in fire sales to obtain liquidity. In a first step, this reaction erodes the banks’ capital buffer. In a second step, the post-shock capital base is combined with a network model to simulate cascading defaults in the Austrian interbank market.

  • A drop in market value: a repricing of market risk factors causes portfolio/credit losses pushing a bank’s financial returns to the left tail of the distribution. The bank reaches its VaR returns in the market-implied value of assets.

56. The transmission of each separate initial shock from an individual bank to the broader banking sector is spread through the following channels, respectively:54

  • Bilateral Exposure: counterparties with a significant exposure to the failing firm may suffer material losses resulting in their inability to satisfy their obligations thus transmitting distress to other parts of the financial system down the credit chain in the form of cascading defaults.

  • Market Contagion: Market participants’ revise their expectations on the solvency of other firms following similar business models than the firm in distress, conditional on the broader economic environment.

57. The bilateral exposure channel is captured by a funding/network analysis conducted by the OeNB drawing on Austrian banks’ bilateral matrix of exposures. The stress test assesses the solvency impact of liquidity strains from fire sales and rising funding costs and the potential for indirect default cascades through the Austrian interbank market.

58. The contagion channel is examined by the FSAP team using a combined market and balance sheet-based approach. Contagion effects from Austrian banks’ left tail comovement in leverage ratios and financial returns with other domestic and global banking institutions are assessed using the CoVaR methodology.

A. The Funding/Network Analysis

59. The funding/network analysis tries to address two missing links of the traditional solvency stress test. First, the solvency effects from a negative liquidity gap in banks facing funding pressures. Second, the potential for default cascades triggered by an insolvent firm on its creditors, leading in turn, to severe strains on the latter counterparties, transmitting distress throughout the entire banking sector.

60. The one-year global funding scenario mimics the effects of the post-Lehman liquidity shock on Austrian banks. In particular:

  • New issuance: unsecured interbank, FX swap markets and capital markets close.55 For the very strong banks,56 markets reopen gradually allowing up to 70 percent of banks’ expected issuance after the first quarter of liquidity stress;

  • Net cash-outflows: contingent liabilities rise triggered by a 50 percent jump in the drawings of committed lines; inflows from loans/maturing paper is banks’ own portfolio decrease in line with the PD shifts estimated under the solvency test;57

  • Counterbalancing capacity: liquid assets suffer from sharp price declines driving up haircuts across asset classes. For assets deposited at the central bank, Table 4 applies. For assets not deposited at the central bank, granular haircuts range between 12.25 percent (investment grade) to 80 percent (sub-investment grade).58

61. The cost of funding increases for both retail deposits and wholesale funding. The cost of expiring term retail funding increases by about 140 bps. For those few banks that are able to access capital markets, the cost of new issuance rises by 70 bps.59

62. Banks showing negative funding gaps engage in distress sales in an effort to obtain liquidity. The sudden increase in market supply of assets in a downward market drives down prices significantly. The stressed value of assets is computed on the basis of haircuts in the counterbalancing capacity. Banks that become illiquid during the stress test horizon incur asset fire losses up to the depletion of their counterbalancing capacity.

63. Domino effects are estimated using a default cascade model for the consolidated Austrian interbank market (Box 3). Cascading effects may occur when the failure of one bank causes its creditors to fail, and so on:

  • The contagion mechanism is based on a network approach of interbank exposures;

  • Bilateral netting of intra-group exposures is allowed. The assumed LGD on net exposures is 100 percent;60

  • A bank is considered in default when its capital adequacy ratio at the consolidated level falls below 8 percent.61

Overview of Furfine’s Network Model1

The analysis examines whether the failure of an individual institution may pose a risk to financial stability. Distress in a financial firm may cause systemic risk, when its failure triggers severe knock-on effects due to high interbank exposures. The interbank market is modeled as a network whereby each bank’s financial exposures vis-à-vis other banks can serve as a potential channel of contagion through which solvency risk can spread across banks.

The examination of contagion is direct; that is, it is based on analysis of an underlying set of data that measure credit exposures bilaterally. The simulations are conducted assuming that creditor losses are realized immediately with recovery rates of 0 percent (i.e. complete loss).

The exercise tracks the lender’s capacity to absorb the shock by verifying whether it has enough loss absorbing capital to cover the losses. If the generated loss is greater than its capital base, the lender will default on its own creditor counterparties, potentially unleashing a wave of defaults through a domino effect along the credit chain. The number of defaults in the default cascade provides a measure of the interconnectedness of the interbank market.

The degree of contagion for a given failure scenario depends crucially on the nature of banking relationships. In particular, the number and the capitalization of the counterparties to significant debtor banks are crucial determinants of the degree of contagion.

1/ Based on Furfine (2003) ‘Interbank Exposures: Quantifying the Risk of Contagion’, Journal of Money, Credit and Banking 35 (1): 111–128.

64. The results suggest that the Austrian banking system is resilient to potential funding and contagion stress transmitted through creditors’ exposures in the Austrian interbank market. Under the global funding scenario, large banking groups do not experience losses due to their strong counterbalancing capacity, as well as the network structure of the Austrian interbank market.62 The impact on the core Tier I ratio of the whole banking system is limited to 108 bps, and is driven primarily by banks’ fire sales rather than by cost of funding effects or cascading defaults.

65. The results of the contagion analysis should be interpreted with caution. First, fire-sale assets are calibrated exogenously. The spiral effects from further declines in prices as a function of the aggregate increase in supply of assets are not modeled explicitly. Also, the mark-to-market effects from common exposures to stressed assets by banks holding similar assets are not computed. Second, domino effects are transmitted through the Austrian interbank market. Induced failures from the inability to service other debt instruments or from stress of other counterparties operating outside the Austrian interbank market are not considered. Third, contagion effects from a bear-market sentiment to banks following similar business models to the bank in distress are excluded.

B. The CoVaR Analysis

66. The CoVaR approach is complementary to the network analysis. It addresses some of the caveats outlined above. First, contagion effects are measured using equity market valuations picking up spillovers unrelated to credit exposures. Distress in an individual bank may propagate by reversing market sentiment to other firms holding the same asset classes or following similar business models. Second, financial instability transmitted through distress in global banks is captured in the analysis. For large international Austrian banks, instability is more likely to be spread from/to global counterparts, acting in the CESEE region or in global funding markets, than throughout the domestic interbank market.63

67. The CoVaR framework is used to assess whether individual distress could pose a material risk to financial stability. Although there is not a unique definition of financial distress, we assume that a firm is in distress when it reaches its VaR.64 We take the approach of the US Financial Stability Oversight Council (FSOC) and characterize a financial system as stable when it is not the source of, nor amplify the impact of, shocks.65

68. The channel of propagation of financial distress is contagion through financial markets and changes in banks’ leverage. Even in the absence of significant creditors’ exposure to the distressed firm, contagion may occur if investors believe that the vulnerability of the failing firm is common across similar firms because of the type or scope of activities. The quantification of contagion effects depends on: (i) the definition of the financial system; (ii) the economic and financial circumstances in which a firm’s failure arises.

69. To assess the transmission of systemic risk through the financial system, two relevant peer groups to Austrian banks are constructed: (i) a European banking system including forty internationally active banks (Annex VI Table 1); (ii) a CESEE banking system formed by fourteen large foreign banks active in the CESEE region (Annex VI Table 2). Banks active in CESEE are defined in terms of absolute exposures—to capture systemic risk transmitted through deleveraging at distressed prices—, and in terms of the share of their CESEE operations in consolidated assets—to reflect solvency risk from macroeconomic stress in the region—(Annex VI. Figure 1). The analysis is performed on banks’ market valued asset weekly returns over the period April 2005-Dec 2012.66

70. The potential for individual distress to unleash global financial contagion depends crucially on the broader financial environment. The key role played by economic and financial conditions in triggering and reinforcing contagion effects has come to the fore during the global financial crisis as highlighted by Tarullo (2011). To capture these effects, time-varying estimates of VaR/CoVaR dynamics are characterized using European and US financial risk factors as state variables (Annex VI Table 3).

71. The differential impact of financial state variables and banks’ contribution to systemic risk measured by ΔCoVaR across European and CESEE banking systems reveals that (Annex VI Table 4):

  • Among the different financial variables used as state variables, liquidity strains exhibit the strongest predictive power to forecast European and CESEE banking system tail returns. European banks’ left tail returns are also affected by an uptick in equity market volatility, changes in the short-end of the yield curve, and the sovereign debt crisis.

  • The effect of individual banks’ distress on banking system tail returns is very significant. Asymmetries are also very noticeable. For the European banking system, the coefficient of individual negative returns on banking system returns is more than four times larger than the coefficient of positive returns, whereas for the CESEE peer group, it is over two times larger.

72. Outward cross-border spillovers to European banks active in the CESEE region appear relatively limited. The market-based CoVaR analysis identifies the rise in tail co-movement of financial institutions conditional on individual banks’ distress controlling for a set of time-varying financial state variables. The results suggest that on average, the risk that severe distress affecting the top two Austrian banks is transmitted to other banks in the CESEE region is not negligible, but is less that the systemic risk potentially introduced by severe distress affecting other CESEE peer banks (Annex VI Table 5).67

73. Inward cross-border spillovers from distress in CESEE banks differ markedly across peers. The CoVaR analysis also suggests that individual banks whose distress may have the most impact on Austrian banks are those headquartered in the CESEE region that are also active across CESEE, or those foreign banks that have the highest presence in the region (Annex VI Table 6). The severity of the distress transmitted to Austrian banks by its CESEE peers does not appear to be significantly different than is the case for other banks. Roughly the same banks that are systemic for Austrian banks are those that are similarly systemic for the peer group.

74. The analysis provides some evidence for the need to combine a micro-prudential and macro-prudential perspective in the regulation of systemic institutions. The results from the CoVaR analysis suggest that there is a weak link between individual and systemic risk. There is no correlation between banks’ risk in isolation, measured by their VaR, and banks’ contribution to systemic risk, measured by their ΔCoVaR. This lack of relationship applies to both the European and the CESEE banking systems (Annex VI, Figure 2). This suggests that any add-on on regulatory capital requirements designed to contain spillover effects needs to be calibrated drawing on both individual and systemic risk analysis.68

Conclusions and Recommendations

75. Stress test results suggest that the Austrian banking system is adequately capitalized against adverse macroeconomic shocks. The support provided by the Austrian Bank Stability Package and the gradual de-risking of banks’ balance sheets by selectively rebalancing portfolio exposures to the CESEE region have contributed to strengthen capital buffers. Under the most severe macroeconomic scenario, banks representing less than 7 percent of total bank assets would fall below the regulatory threshold, and the estimated aggregate capital needed to bring back the capital ratios of these banks above the regulatory minimum amounts to 0.3 percent of total bank assets, or about 1 percent of GDP.69 The aggregate results masks, however, a certain degree of heterogeneity within and across segments, with some mid-and small-sized banks being relatively more vulnerable, particularly those with low initial risk buffers and profitability issues.

76. The banking sector appears well positioned to meet Basel III capital requirements. On aggregate, the banking sector would comfortably pass the hurdle rates laid out by the Basel III phase-in arrangements for CET1 under the most severe scenario. The front-loading of Basel III full implementation,70 including the total phase-in of deductions from CET1, enhanced risk-weighted assets, and a 2.5 percent capital conservation buffer, would have an impact of 1.4 percentage points on core Tier I capital, on top of 1.7 percentage point impact from projected profit and loss effects from severe macroeconomic stress. Capital buffers above the minimum Tier 1 capital ratio are somewhat thinner as Austrian banks hold limited amounts of non-common equity Tier 1 qualifying capital, in the form of private participation capital71 and minority interests.

77. The stress test results must be interpreted with caution. Stress test results need to be interpreted with caution given asset quality—particularly in some CESEE countries—is still deteriorating and difficult to assess with full confidence. The upcoming bank asset quality reviews by the ECB should provide a more robust basis for assessing the strength of the balance sheets of Austrian banks and the policy responses that may be needed.72 Also the three-year stress testing horizon does not consider the repayment of state participation capital.73 It might become challenging for some individual banks to comfortably satisfy capital regulatory ratios if their profit generation capacity and ability to issue CET1 qualifying capital is severely undermined under protracted stress. Further, the potential for a capital surcharge of up to 2.0 percent of RWAs laid out by the forthcoming CRD IV/CRR on domestic systemic institutions lies outside the scope of the stress testing exercise.74 Also stress rests results are based on Basel III regulatory minimum capital requirements as hurdle rates which may lie below the required market expected capital buffers to keep funding costs low. Other caveats include the reliance of stress testing methods on reduced-form approaches and estimated quantitative relationships that may not hold in periods of extreme stress. Finally, stress tests are based on consolidated supervisory data and, as it is typical in other FSAPs, do not allow for capital or liquidity ring-fencing measures in host countries which could constrain the free allocation of capital as well as funding relationships between foreign subsidiaries and parent Austrian banks.

78. Banks should continue building capital buffers gradually to support market confidence. The realization of a confluence of adverse events, including market expected buffers well beyond regulatory ratios, and the implementation of restrictions on cross-border transfer of capital across jurisdictions may become a source of concern in the medium term. Additional capital requirements may be imposed on Austrian banks by investors in order to ensure that they keep pace with their peers, and as a result of their specific business model and comparatively large exposures to the CESEE region, where underlying asset quality is subject to significant uncertainty. These concerns may be exacerbated by an increase in financial market fragmentation across the region, including through the potential introduction of capital or liquidity ring-fencing measures in host countries, which would constrain the free allocation of capital as well as funding relationships between foreign subsidiaries and parent Austrian banks.

79. Banks’ funding structure appears resilient to the full implementation of Basel III liquidity coverage ratio. The improvement in the liquidity position of Austrian banks can be attributed to enhanced liquidity supervision and monitoring conducted by the OeNB and strengthened supervisory standards of banks’ liquidity risk management. Local liquidity supervision requirements are significantly more stringent than those agreed under the revised liquidity risk framework of Basel III, and crucially, are applied across currency buckets. Still, banks should continue lengthening funding tenors in foreign exchange to be well prepared to withstand any funding shock in global markets.

80. The Austrian banking system is also resilient to potential funding and contagion stress based on network analysis. Under a global funding scenario, large banking groups do not experience losses due to their strong counterbalancing capacity, as well as the network structure of the Austrian interbank market. The impact on the CT1 ratio of the whole banking system is limited, and is driven primarily by fire sales rather than cost of funding or contagion effects. On the other hand, the risks of cross-border spillovers between Austrian and other peer banks active in the CESEE region appear limited.

81. The contagion analysis can serve as an input in the calibration of domestic capital surcharges for domestic systemic banks. Under the CRD IV/CRR regulatory framework, the Austrian authorities will have the option to set a systemic institution capital surcharge to contain systemic risk in the domestic market. The network analysis informs how distress in a particular financial institution can be transmitted throughout the Austrian interbank market causing a broader impairment to financial stability. Contagion through market signaling or wrong-way risk can be measured using a market-based approach such as CoVaR. Both tools can serve to monitor systemic risk so that any rapidly growing activities that may pose systemic risk can be identified early and, where needed, those systemic risks addressed.

Annex I. Risk Assessment Matrix (RAM)

article image
article image

Annex II. Identification of Key Risk Factors: A Market-Based Approach

The FSAP team conducted a market-based analysis to drill down on the main determinants of major Austrian banks’ solvency risk. The risk factors examined belong to three main categories: (i) Austria macro-financial variables; (ii) contagion from the CESEE region unrelated to domestic/global developments; and (iii) global risk factors, including changes in estimated risk premia. The approach builds on Longstaff et al (2011) and uses monthly changes in the credit default swap (CDS) market and in Moody’s KMV expected default frequencies (EDF) to provide a direct measure of changes in market perception of solvency risk. For each bank we regress monthly changes in CDS spreads and EDF estimates on the set of relevant explanatory variables. The time series starts in October 2007 and ends in October 2012.75

The first set of variables includes market revisions in macroeconomic projections for the Austrian economy. Given that most economic data releases are backward-looking, published with a lag, at low frequency, and subject to rounds of revisions, we use analysts’ economic forecasts as a proxy for market expectations of Austria’s economic fundamentals (GDP, industrial production, current account balance, real wages, unemployment, and sovereign CDS).

Concerns on the large exposure of Austrian banks to the CESEE region are reflected by idiosyncratic sovereign risk unexplained by systematic risk factors. Changes in sovereign spreads in the region capture market revisions in countries’ economic outlook as well as valuation losses from government bond holdings by CESEE subsidiaries. In line with the OeNB modeling framework, we consider four country aggregates: New EU Member States (NMS-2004), New EU Member States 2007 (NMS-2007), Southeastern Europe (SEE), and the Commonwealth of Independent States (CIS).76 We compute monthly changes in the sub-regional CDS weighted by consolidated BIS exposures of Austrian banks as of September 2008. To identify contagion from the CESEE region, we regress for each sub-region the monthly changes in sovereign CDS spreads on the other explanatory variables in the system—including Austria specific and global variables-, and use the orthogonal residuals as a proxy of contagion from exposure to the CESEE region.

To capture the effect of stressful financial scenarios on solvency risk we consider fluctuations in market returns and infer changes in risk premia. Market returns show the state of financial markets including the equity, fixed-income, commodities, and derivative segments. Heightened risk aversion translates into higher risk premia demanded by global investors. Following the financial market literature, we estimate equity, volatility, and term risk premia.

The equity premium is computed as the change in the price-earnings ratio for the stock market index. We calculate changes to the equity risk premium as monthly fluctuations in the price-earnings ratio (P/E) of the S&P 100 index. Intuitively, when risk aversion (and the variance risk premium) is high, agents reallocated their portfolios from risky assets to safe haven, depressing market prices (and the P/E ratio), and increasing expected market returns.

We compute the volatility risk premium as the difference between implied and realized volatility. We replicate the analysis of Garman and Klass (1980) and construct an efficient estimator of market volatility. We use the following notation: σ2 = variance of price change, C0 = previous closing price, C1 = current closing price, O1 = current opening price, H1 = current highest price, L1 = current lowest price, normalized prices: u = H1 – O1, d = L1 – O1 and c = C1 – O1, and f = fraction of the day that trading is closed. The most efficient estimator is obtained by the composite ratio:

σ ̂ 2 2 = 0.12 ( O 1 − C 0 ) 2 f + 0.88 σ ̂ 1 2 ( 1 − f )

where σ̂12=0.511(u−d)2−0.019[c(u+d)−2ud]−0.383c2

We compute σ̂32 as a 20-day period moving average on the S&P 100 index. Our estimated volatility premium at time t is constructed as the difference between the VIX index, capturing market expectations of near-term volatility conveyed by the S&P option price, and the most efficient measure of realized volatility:

V o l − p r e m t = V I X t − σ ̂ 2

The term premium is estimated as the expected excess return on US 5 year Treasury bonds proxied by the linear combination of one through five year forward rates proposed by Cochrane and Piazzesi (2005).

We denote by pt(n) the log of a n-year 1 dollar discount bond at time t. The excess total return of holding an n-year bond at time t and selling it as an n-1 bond at time t+1 is given by

r x t + 1 ( n ) = p t + 1 ( n − 1 ) − p t ( n ) − y t ( 1 )

where yt(1) denotes the log yield of an 1-year bond. CP run regressions of excess returns on 1- through 5- forward rates, where the forward rate at time t for a 1-year loan issued at time t+n-1 and repaid at time t+n is denoted by

f t ( n ) = p t ( n − 1 ) − p t ( n )

The same linear function of forward rates forecasts holding period returns at all maturities and define a single return-forecasting factor as

F t = ( y t ( 1 ) , f t ( 2 ) , f t ( 3 ) , f t ( 4 ) , f t ( 5 ) )

We use Cochrane and Piazzesi’s estimates of the loadings of the single factor on the average excess return of holding a 2- through 5-year maturity bond:

1 4 Σ n − 2 5 r x t + 1 n = − 3.24 − 2.14 y t ( 1 ) + 0.81 f t ( 2 ) + 3.00 f t ( 3 ) + 0.80 f t ( 4 ) − 2.08 f t ( 5 ) + ɛ ¯ t + 1

using one-through five-year Treasury Strips data from the fair value curve provided by Bloomberg. We proxy changes in the term premium by the change in the expected average excess return of holding a two- through five-year government bond.

The results of regressing changes in solvency risk estimates on Austrian, CESEE and global risk factors suggest that (Annex II Table 2):

Annex II. Table 1.

Variable Definitions for Econometric Analysis on Risk Factors

article image
Annex II. Table 2.

Econometric Results of Risk Factors for Solvency Stress Test

article image
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Annex III. Stress Test Matrix (STeM) for the Banking Sector

article image
article image
article image
article image
article image
article image

Annex IV. Sensitivity Analysis of Repayment Vehicle Foreign Currency Loans

Repayment vehicle loan (RPV) exposures continue to pose a challenge to the Austrian banking system.77 In the third quarter of 2012, 11.2 percent of net loans to the private sector were based on a repayment vehicle. Loans linked to RPVs play a greater role in lending to households than to non-financial corporations. As of September 2012, RPV loans to domestic nonbanks amounted to €30.0 billion, of which €27.3 billion (share of 20.7 percent) were granted to households and €2.7 billion (share of 2.0 percent) to non-financial corporations. By contrast to amortizing loans, the repayment of RPV loans does not take place in regular installments but at maturity (bullet loans). During the life of the loans the borrower makes a monthly payment towards a RPV. At maturity, these payments and their financial returns are used to pay back the principal of the loan.

According to the latest survey conducted in mid 2011, the total funding gap of RPVs amounted to €5.3 billion (18.2 percent). The information on the typical structure of RPVs is not available from regulatory reporting sources but derived from surveys among banks. Two such surveys were conducted in recent years: one in spring 2009 and another in autumn 2011. Both covered more than 90 percent of the outstanding volume in RPV loans of Austrian banks. In mid-2011 funding gaps of RPV loans denominated in FC and granted to domestic households and corporates amounted to €4.7 billion (with funding gaps of 20.1 percent and 18.6 percent for CHF and JPY, respectively) whereas RPV loans denominated in Euro accounted for €0.6 billion (15.6 percent funding gap).

Mutual funds-based life insurance products are at large the main contributors to RPVs funding gap. The reason is twofold. First, they account for the largest share of RPVs (54.0 percent). Second, they show the largest funding gap (21.0 percent against an average of 16.0 percent for the remaining vehicles). Its contribution to the estimated aggregate shortfall as of September 2012 (€5.5 billion) is estimated at €3.4 billion.

The sensitivity analysis of tail risk conducted by the FSAP team reveals that bullet loans ‘at risk’ are manageable even under extreme events. Adding an additional annual yield shock (i.e., 100 yield shock (adverse) and 200 yield shock (severe)) combined with a 1.5 SD CHF appreciation of the loan face value at maturity, the maximum funding gap would increase from an accumulated €4.2 billion to €8.6 billion. Given an average remaining maturity of 13 years, this implies an additional loss of under 1bn during the stress test horizon. These results should be interpreted with caution. A number of extensive assumptions had to be made to the many unknowns in the underlying data, mainly on the breakdown of the projected RPV value under the baseline scenario into assets already paid into the RPV, future payments into the RPV, and the assumed performance of the RPV. Also they should not be added up to the sensitivity results in FCLs as it would duplicate the effect of the FX shock.

  • An upward revision of unemployment forecast emerges as the main Austrian macroeconomic determinant of changes in solvency risk.

  • Contagion from CESEE is the most notable risk factor. The explanatory power of the regression increases significantly from 0.1 to 0.5–0.6. In terms of sub-regions, the effect is mainly driven by NMS-04 and NMS-07 with the former being associated with sharp depreciations of the Hungarian forint. A negative outlook in the SEE and CIS region prove not to be statistically significant.

  • Heightened stress in European banks measured by the Itraxx Europe senior financial index, and higher credit risk in sub-investment grade European corporates tend to widen solvency risk. On the other hand, a pick-up of sovereign distress in Austria or in the GIIPS has no significant impact on Austrian banks’ solvency risk consistent with the limited exposure to domestic and European peripheral countries.

  • The results suggest that an increase in the term risk premium may contribute to rising credit spreads. By affecting movements in long-term rates (despite the central banks’ monetary policy reaction) it may lift up banks’ funding costs (direct channel) as well as credit risk of households and nonfinancial corporations (indirect channel).

Annex IV. Table 1.

Stress Test of RPV Yield and CHF Shock by Product Category

(in million euros)

article image
Source: IMF staff calculations drawing on OeNB survey (June 2011).

50th percentile of daily annual returns over Jan 2005 through Dec 2012 of the following proxy instruments: equity funds (Eurostoxx 50 Equity Index, fixed income funds (JPM euro EMBI global europe), balanced funds (JPMorgan Investment Funds - Global Balanced Fund in EUR), mutual funds-based life insurance (Franklin Mutual Series Fund Inc - Mutual European Fund), other instruments (average yield of the above instruments).

The analysis assumes average residual maturity of 13 years as of September 2012.

An accumulated CHF appreciation of 1.5 SD over 2013–2014 with a stabilizing rate thereafter, increases outstanding debt for CHF denominated loans.

Annex V. Sovereign Risk Calibration

Sovereign risk is measured in the adverse scenario through changes in sovereign yields leading to a repricing of all affected bonds. Holdings of government bonds in both the banking book and the trading book are repriced.

The scope of ‘sovereign’ follows the CRD IV definition in the standardized approach. It includes: all central governments (but no central banks), all regional governments, and all local authorities. Exposures classified under the IRB approach are segmented following the same breakdown.78

All direct and indirect sovereign exposures are stressed including those held by CESEE subsidiaries. The net direct exposure comprises gross exposures (long) net of cash short position of sovereign debt (without derivative hedges such as CDS). This is referred to as the “net direct position.” The indirect sovereign exposures includes both on and off balance sheet exposures:

  • Direct derivatives positions are subject to fair value adjustments based on the relevant shock (e.g., for an interest rate derivative, use the shock on interest rates) and the relevant CVA adjustments.

  • Indirect exposures (those with counterparties other than the sovereign itself, i.e. CDS) are treated in a similar way, subject to fair value adjustments of the relevant shock and the CVA adjustment.

The methodological approach is as follows:

  • Under stress, the term structure of sovereign risk shifts upward for all countries to which Austrian banks are exposed, including sovereign bonds held by CESEE subsidiaries to comply with local liquidity requirements.

  • The approach allows changes in risk term premia associated with the excess yield that investors require to commit to holding a long-term bond instead of a series of shorter-term bonds under volatile conditions (see Annex II for estimated impact on large banks’ market-implied solvency perceptions).

  • The calibration of the sovereign shock on the level of spreads (parallel shift), on the slope and on the curvature of the yield curve is based on historical yields at each maturity date of the term structure, using the modified duration approach. When there is no available maturity to derive a valuation haircut, the relevant haircuts are interpolated.

  • The shock is calibrated for fifty eight countries. For fifty countries, the shock is derived from the 90th percentile of the historical distribution of annual changes of daily yields of Bloomberg generic 5-year government bond yields over the period 2005–12. The change in yields is used to reprice all government bonds under a cash-flow approach matching a modified duration formula to each maturity bucket.

  • For Belarus, Bulgaria, Luxembourg, Malta, and Romania, the haircut is computed using extreme returns for the most liquid outstanding international bond as of Dec 2012, given the limited time series of the generic yield curve.79 For Cyprus, the haircut is calibrated from the sovereign yield curve as of Dec 2012. For Estonia, only international loans were outstanding as of Dec 2012.

Annex V. Table 1.

International Bonds for Calculation of Sovereign Haircuts

(Countries with no generic bonds)

article image
Source: Bloomberg.

Haircuts to the banking book are applied to adjusted (marked-to-market) balance sheet values. It means that banks have recognized losses or gains before the haircut itself is applied from search-for-yield or flight-to-quality dynamics. All exposures are reported before the deduction of provisions, the application of credit conversion factors, or credit risk mitigation techniques:

  • For exposures valued at amortized cost, the valuation loss for each country of exposure and sovereign bucket is calculated as:

    • Valuation loss=amortized cost-market value + market value*haircut

  • The resulting losses are distributed across the stress testing horizon.

Annex V. Table 2.

Sovereign Haircuts by Selected Countries of Exposure

(in percent)

article image
Source: Bloomberg and IMF staff estimates. Note: The shock is derived from the 90th percentile of the historical distribution of annual changes of daily yields of Bloomberg generic 5-year government bond yields over the period 2005–12. For Belarus, Bulgaria, Luxembourg, Malta, and Romania, the haircut is computed using extreme returns for the most liquid outstanding international bond as of Dec 2012, For Cyprus, the haircut is calibrated from the sovereign yield curve as of Dec 2012.