In Search for Yield? New Survey-Based Evidence on Bank Risk Taking. 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. Motivation.
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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
Common banking factors
Factor Augmented Vector AutoRegressive Models (FAVAR)
Feedback to banks
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.
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”)
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.
Not useful for analyzing shocks transmission between individual banks (network effects)
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.