Obje ctive : To measure and analyse co-movements in the fragility of EU banks Two main issues addressed:

1. Banking Integration and Co-movements in EU Banks’ FragilityAndrea Brasili – Giuseppe VulpesUniCredit GroupResearch DepartmentJoint ECB-CFS-Banco de España Conference on"Financial Integration and Stability in Europe"Madrid, 30 November 2006

2. Objective and motivation of the paper • Objective: • To measure and analyse co-movements in the fragility of EU banks • Two main issues addressed: • 1) decompose a bank fragility indicator into three components (EU wide, domestic and bank specific - idiosyncratic): the weight of the EU wide component • 2) Analyse the sources of co-movements, i.e. the role of common macro shocks • Motivation: • Completion of EMU and introduction of the euro may have increased the degree of interconnectedness among EU banks and this may have important policy implications, e.g. for financial stability and for the structuring of banking supervision in Europe

3. The fragility indicator • Distance to default (DD) is our fragility indicator. It is a Merton based (i.e. option pricing) indicator derived from the Black and Scholes formula, i.e. • The DD represents a measure of bank risk with some desirable properties. Gropp, Vesala and Vulpes (2002 and 2004) show that: • it encompasses most elements of bank risk (namely assets returns, volatility of assets and leverage); 2) it is inherently forward-looking and available more frequently than traditional balance-sheet indicators; 3) it is capable to predict a material deterioration in a bank’s condition (up to 18 months in advance) and not affected by the presence of implicit or explicit safety net. • The DD may have however some reliability limitations for banks with low trading volume (small and mid sized banks): this could bias our results towards lowering co-movements in bank risk

4. The data DD = f(Market value of assets, Volatility of assets) Obtainable from observed market value of equity, equity volatility and balance sheet liabilities • Weekly and monthly (avg of weekly) DDs to distinguish between short-term and cyclical co-movements and to reduce noise in weekly data • Sample: • 160 EU listed banks for which market data are available • balanced panel (99 banks observed for the whole sample period November 1994-December 2004)

5. Descriptive analyses: simple correlation (all banks) • Correlations in bank fragility are on average rather low. However this finding is not surprising: • the riskness of small and mid-sized banks is largely driven by domestic and idiosyncratic dynamics; • contemporaneous correlations do not take into account the transmission mechanism of common shocks

6. Descriptive analyses: simple correlation (large banks) • Correlation in bank fragility among a set of large banks (total assets as of end 2002 above 100 bln euro: 28 banks belonging to 9 EU countries) appears significantly higher and patterns of cross-country correlation clearly emerge (e.g: BE and NL, NL and ES) • Several cases in which the correlation in bank fragility appears particularly high even at the cross-border level. For example: Banco Santander (ES) and ING (NL) (63% weekly, 82% monthly); the correlation among Commerzbank (DE) and Banca Intesa (IT) (52% weekly, 81% monthly).

7. Descriptive analyses: frequency distribution of pairwise correlations (weekly changes / domestic)

8. Descriptive analyses: frequency distribution of pairwise correlations (weekly changes / cross-border)

9. Descriptive analyses: correlation at leads and lags Cross correlogram weekly DDs changes Capitalia (IT) and Banco Pastor (ES)

10. Descriptive analyses: dynamic correlation and cohesion

11. Descriptive analyses: dynamic correlation and cohesion (Large banks)

12. The dynamic factor model • E = EU wide component • N = national component • I = idiosyncratic component • Estimation of the three unobserved components through a sequential procedure First step: extraction of the EU wide component by running the dynamic factor model on all banks in the sample => residuals bundle of national + idiosyncratic components Second step: extraction of the national component by running the dynamic factor model on the residuals of the first step for banks incorporated in the same country

13. Model’s results

14. Frequency distribution of the variance explained by the EU component (weekly DD changes)

15. Frequency distribution of the variance explained by the EU component (monthly DD changes)

16. Model’s results (Large banks)

17. Drivers of co-movements macroEU = common macro shocks at EU level macroN = common macro shocks at domestic level e = residuals => banking sector specific component

18. Drivers of co-movements Avg. across banks of the variance explained by each factor • Common EU macro shocks are significant but not the sole source of co-movements. What else ? common exposures (countries, global shocks), interbank linkages ?

19. Summary • The relevance of EU wide common shocks appears not negligible since on average they explain around 42% of the variance in bank risk (monthly DD changes). • The EU-wide component in bank risk is increasing in time, perhaps reflecting greater banking integration among EU-banks. • The EU component is much huger for large banks, explaining up to 80% of the variance in bank risk and this set of banks constitutes the main transmission channel of common shocks. • Once we remove the influence of common macroeconomic shocks, the dynamics of EU banks’ fragility is largely accounted for by banking system specific factors and these factors are becoming increasingly important at a EU-wide level • The dynamic correlation shows that common EU-wide shocks are more relevant at cyclical and/or long-term frequencies. However, co-movements at very high frequencies (i.e. in the very short term) are relevant for large banks, which could be indicative of some form of contagion.

20. Implications and possible extensions • These results have implications as regards the monitoring of financial stability conducted by central banks and supervisory authorities • They also provide indication as to “who should monitor whom” and call for more supervisory co-operation at EU wide level • There are a number of potential applications and extensions: • sources of common shocks (drivers of co-movements in banks’ fragility): what is the role of factors other than common macro shocks ? • derivation of a EU-wide fragility indicator cleaned from country specific and idiosyncratic shocks