An integrated approach to stress-testing: the Austrian Systemic Risk Monitor (SRM)

Boss, Michael; Krenn, Gerald; Puhr, Claus; Summer, Martin

doi:10.1017/cbo9780511635618.014

Cited by 2 publications

(4 citation statements)

References 4 publications

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“…Similar to Boss et al, 3 M arquez-Diez-Canedo and Martínez-Jaramillo 19 and Martínez-Jaramillo et al 20 analyze the loss distribution for the Mexican banking system as a whole, where the losses stem from initial shock and contagion risk. The main di®erence between their works and that of Boss et al 3 is in the information used and the conceptual framework employed. Using the data from the Mexican central bank, M arquez-Diez-Canedo and Martínez-Jaramillo 19 and Martínez-Jaramillo et al 20¯n d that neither the network structure nor the level of correlation of joint losses alone can cause dramatic changes in the shape of the loss distribution, but the probability distribution of the random shocks changes the shape of such distribution dramatically.…”

Section: Introductionmentioning

confidence: 92%

“…Boss et al 3 propose a model for systemic risk monitor, and then derive the aggregate loss distribution for banking systems, where the losses are from credit risk, market risk and contagion risk as well as the combination of these risks. Similar to Boss et al, 3 M arquez-Diez-Canedo and Martínez-Jaramillo 19 and Martínez-Jaramillo et al 20 analyze the loss distribution for the Mexican banking system as a whole, where the losses stem from initial shock and contagion risk. The main di®erence between their works and that of Boss et al 3 is in the information used and the conceptual framework employed.…”

Section: Introductionmentioning

confidence: 99%

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Loss distribution of systemic defaults in different interbank networks

2016

Int. J. Mod. Phys. C

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We analyze the impact of the network structure, the default probability and the loss given default (LGD) on the loss distribution of systemic defaults in the interbank market, where network structures analyzed include random networks, small-world networks and scale-free networks. We¯nd that the network structure has little e®ect on the shape of the loss distribution, whereas the opposite is true to the default probability; the LGD changes the shape of the loss distribution signi¯cantly when default probabilities are high; the maximum of the possible loss is sensitive to the network structure and the LGD.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Loss distribution of systemic defaults in different interbank networks

2016

Int. J. Mod. Phys. C

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“…Boss et al (2004) did liquidity risk stress tests for the whole Austrian banking sector, for the aggregated sectors (joint stock banks, savings banks, Raiff eisen cooperatives and others) and for the sample of systematically important banks (which included 15 largest banks). Liquidity ratios were shocked by four scenarios: market value of bonds decreased by 10%, market value of equities decreased by 20%, other banks withdraw 20% of interbank deposits and nonbank customers withdraw 20% of their deposits.…”

Section: Theoretical Background Of Liquidity Ratios and Stress Testingmentioning

confidence: 99%

“…Two studies applied very high threshold: Komárková et al (2011) simulated that 50% of interbank claims become unavailable, Boss et al (2007) assumed that 50% of non-bank deposits will be withdrawn. Three studies use much lower haircut: 20% (Negrila, 2010;Boss et al, 2004 andRychtárik, 2009). In accordance with the three last studies, we will simulate a withdrawal of 20% of interbank deposits.…”

Section: Scenario 2: Confi Dence Crisis On the Interbank Marketmentioning

confidence: 99%

Liquidity Risk Sensitivity of Czech Commercial Banks

Vodová

2014

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The recent fi nancial crisis has shown that a liquidity risk plays an important role in the current developed fi nancial system. One of the effi cient tools of liquidity risk management is stress testing which can show banks their potential vulnerability to liquidity shocks. The aim of this paper is therefore to measure the liquidity risk sensitivity of Czech commercial banks and to fi nd out the most severe scenario and the most vulnerable bank. Our sample included signifi cant part of the Czech banking sector; we used unconsolidated balance sheet data over the period from 2000 to 2011 which were obtained from annual reports of Czech banks. We have evaluated liquidity risk of each bank in the sample via six diff erent liquidity ratios. Then we stressed these baseline values in three stress scenarios: run on a bank (simulated by a 20% withdrawal of deposits), confi dence crisis on the interbank market (simulated by a withdrawal of 20% of interbank deposits) and use of committed loans by counterparties (simulated by a 5% increase of loans provided to nonbank clients). We measured the impact of all scenarios by relative change of liquidity ratios. The impact of modelled liquidity shocks diff ers among scenarios. The most serious liquidity problems would be caused by the fi rst scenario -run on a bank. The negative infl uence of third scenario (use of committed loans) is less severe. The confi dence crisis on the interbank market would not aff ect bank liquidity at all. The results also show that the severity of the impact of all scenarios worsens in periods of fi nancial distress. We have also found that large and medium sized banks are most vulnerable to liquidity shocks, mainly to massive deposit withdrawals.

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An integrated approach to stress-testing: the Austrian Systemic Risk Monitor (SRM)

Cited by 2 publications

References 4 publications

Loss distribution of systemic defaults in different interbank networks

Loss distribution of systemic defaults in different interbank networks

Liquidity Risk Sensitivity of Czech Commercial Banks

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