In Search for Yield? New Survey-Based Evidence on Bank Risk Taking

1. In Search for Yield?New Survey-Based Evidence on Bank RiskTaking Claudia M. Buch (University of Tübingen, IAW & CESifo) Sandra Eickmeier (Deutsche Bundesbank) Esteban Prieto (University of Tübingen) Bundesbank-SUERF-Conference „The ESRB at 1“ Berlin, November 8-9, 2011

2. Motivation • Build up of large exposure to credit risk by the banking system in the run up to the financial crisis. • Loose monetary policy has been blamed for risk-taking of banks. • Should the monetary authorities consider financial stability aspects when conducting monetary policy? • We use disaggregated banking and macro-data to analyze link between macroeconomic shocks and bank risk-taking.

3. Our research questions and main findings • Do monetary policy and collateral shocks influence bank risk taking? • Monetary policy or collateral shocks do not increase risk-taking for the banking system as a whole. • All banks reduce interest rate spreads. • Is there any kind of systematic heterogeneity in the way how banks react to exogenous shocks? • Small domestic banks increase risky lending the most and reduce risk-premia most aggressively. • Foreign banks tend to lower risk.

4. Structure of the presentation • Data and stylized facts • Empirical model and results • Implications for stress testing

5. Data and Stylized Facts

6. The Federal Reserves’ Survey of Terms of Business Lending (STBL) • Volume and pricing of new loans, stratified sample of 400 banks • Distribution of new loans into different risk categories (minimal, low, moderate, acceptable risk): Shift across risk categories reflects changes in overall credit standards. • Loan ratings are based on soft information (management quality) and hard information(cash flow, credit history, credit ratings, quality of collateral). • Information for different banking groups • Large domestic, small domestic, foreign banks • Information on loan contract terms • Panel of 140 banking variables

7. Share of risky loans across banking groups (1997)

8. Share of risky loans across banking groups (2007)

9. Empirical Model and Results

10. Three features are necessary to identify a risk-taking channel of monetary policy. • Information on new loans and their risk structure ex ante • Information on different banks or banking groups • Studies using micro-level information address these issues • Ioannidou, Ongena & Peydro (2009) • Jiminenez, Ongena, Peydro & Saurina (2010) • Interactive model of the macroeconomy and the banking sector • Multivariate time series models • Our data, together with the empirical framework, capture all three features.

11. Modelling feedback between banks and the macroeconomy using a FAVAR model Bank 1  Bank 2 Bank 3 Common banking factors (unobserved) Macroeconomic factors (observed)  . . . Vector auto- regressive Model (VAR) (macroeconomic shocks) Bank N Factor Augmented Vector AutoRegressive Models (FAVAR) Feedback to banks 11

12. Impulse responses of macroeconomic variables toan expansionary monetary policy shock

13. Impulse responses of new bank lending tomonetary policy shocks  Banking groups  Risk groups

14. Impulse responses of new bank lending tomonetary policy shocks

15. Impulse responses of interest rate spread tomonetary policy shocks

16. Impulse responses of new bank lending tocommercial property price shocks

17. Summary of findings • No evidence of more risk-taking following monetary policy and collateral shocks at the aggregate level. • Heterogeneity matters: • Small domestic banks increase exposure to new credit risk. • Large domestic and foreign banks do not increase risk in response to macroeconomic shocks. • Small banks reduce risk spreads by more than larger banks • Concerns about a wide-spread risk-taking channel might be overstated. • But it might be necessary to limit risk-taking incentives particularly for smaller banks.

18. Implications for Stress-Testing

19. Aim of stress-testing models Determine under which conditions the aggregate capitalization of the banking system is low. Create scenarios for macroeconomic conditions and forecast implications for bank losses and capitalization. Required information: Exposure of banks and reactions to macroeconomic conditions (common exposures across all banks) Idiosyncratic shocks affecting large banks Interdependencies among banks (direct and indirect, through common exposures or business models) Forecasts of macroeconomic conditions Assessment of structural changes (“Lucas critique”) 19

20. Can dynamic factor models address the challenges in stress-testing? Advantages: Dynamic factor models are a useful tool for analyzing feedback between the banking sector and the macroeconomy. They highlight the importance of systemic risk arising from common exposures to macroeconomic shocks. They can be applied in a data-rich environment and provide high flexibility with regard to variables considered. Can be applied to all banks, not only publicly traded banks. Disadvantages: Not useful for analyzing shocks transmission between individual banks (network effects) 20

21. What are the implications for actual stress-testing exercises? Factor models can be a useful part of the toolbox for macroeconomic stress-testing. Factor models require large cross-section and time dimensions: Build up databases that provide sufficiently long time series and make them available for researchers. Common exposures may matter: Smaller and mid-sized financial institutions have to be included as well. 21