BANCA A NAŢIONALĂ A ROMÂNIEI BANCA NAŢIONAL - PDF

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BANCA A NAŢIONALĂ A ROMÂNIEI Outline 1. Literature review 2. Methodology 3. Data 4. Empirical analysis 5. Conclusions and future research work 1. Literature review two main quantitative approaches to assess

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BANCA A NAŢIONALĂ A ROMÂNIEI Outline 1. Literature review 2. Methodology 3. Data 4. Empirical analysis 5. Conclusions and future research work 1. Literature review two main quantitative approaches to assess banking stability NPLs prediction models Central banks (Austria, France, Germany ) EWSs for banking crisis Cihack and Schaeck (2007) Allen (2005) Demirguc-Kunt and Detragiache (1998,2005) large variety of factors leading to bank distress Macroeconomic variables (GDP growth, real interest rate, real exchange rate, inflation, credit growth) Microeconomic variables (FSIs: CAR, tier 1 to assets ratio, nonperforming loans to assets ratio, ROE, net profit to income, operational costs to assets) Institutional variables (deposit insurance schemes, financial liberalization, regulation) 2. Methodology banking stability: definition and instruments Banking stability: the status of the banking system in which credit institutions, specific markets and infrastructure are adequately performing their role within the economy, even in the case of extreme, but plausible events Proxy for baking stability: Quality of assets (weight of net overdue and doubtful claims in total assets) General index for measuring the downside risk in the banking sector (rating downgrade probability) two main transmission channels of unemployment over banking NPL effect stability An increase in the unemployment rate will cause a contraction of the reimbursing capacity of households, triggering an increase in the default rate Demand for new loans effect An increase in the unemployment rate might produce a material reduction of demand for new loans, which could lead to a significant deterioration of the ratio between the bearing interest assets and bearing interest liabilities NPL model standard model design NPL = α + FE + f ( FSI, Macro i, lag ( t) lag ( ) i, t i t ) Rating downgrade model DP = i, t [ a+ f ( Micro i, t 1, Macrot 1 )] 1+ e 1 3. Data exhaustive sample of commercial banks, Romanian legal persons Panel features 31 credit institutions 8.5 years of data (Dec Jun. 2008) monthly observations Explanatory variables Microprudential data Macroeconomic control variables Lags considered: up to one year preliminary empirical analysis (NPL prediction model) preliminary empirical analysis (Rating downgrade model) 4. Empirical analysis reasonable predictive power, according to panel estimation standards (NPL model) NPL ratio is relatively inelastic on short term to labor market shocks Consistently significant and positive relationship between the unemployment rate (1y lag) and the NPL ratio, but Rather low impact in the case of a sharp increase The quarterly growth rate of the gross wage in the economy robustly indicates an inverse relationship between the household s income and the NPL ratio, but Rather low impact in the case of a material change fixed effects a proxy for the quality of individual credit risk management techniques the positive values for fixed effects reflect poorer then average credit risk management skills significant correlation between the level of fixed effects and supervisors rating on management quality (65.93%) good econometric performance, according to logit estimation standards (Dg model) high and robust discriminatory power AUROC in sample: 84.4% AUROC out of sample: 83.8% Reasonable level of model stability (bootstrap approach) four classes of risk (traffic light approach) sensitivity analysis Scenario 1: 1 pp increase in unemployment rate Scenario 2: 3 pp increase in unemployment rate Scenario 3: 5 pp increase in unemployment rate Scenario 4: 7 pp increase in unemployment rate as for December 2008 Downgrade probabi 80% 70% 60% 50% 40% 30% 20% 10% 0% Prediction S3 (+5pp) S4 (+7pp) performance migration in stress events Even in the worst case scenario, the largest share of the banking system would still find itself on green light Only 2.66 percent of the banking system assets would have a high probability of downgrade (all these banks are rated 2) Conclusions The unemployment rate seems to be the most significant macroeconomic variable for explaining system-wide short term movements in the performance of the Romanian credit institutions Econometric evidence suggests that the main transmission channel of unemployment over banking stability is the demand for new loans effect, while the effect over NPLs is rather low The performance of the Romanian credit institutions could decrease significantly in the context of a severe unemployment rate increase towards the levels recorded in early 2000s, but without systemic implications on short term Future research work Test the predictive power of unemployment rate on other indicators used in the assessment of asset quality, such as gross overdue and doubtful loans to total loans ratio Test the statistical significance of the unemployment rate on the default rates of consumer loans Enlarge the set of candidate explanatory variables (both micro and macro) for a more refined empirical research on the risk drivers of bank distress in Romania Thank you for your attention!
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