In search for yield new survey based evidence on bank risk taking
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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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In Search for Yield? New Survey-Based Evidence on Bank Risk Taking

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In search for yield new survey based evidence on bank risk taking

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


Motivation

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.


Our research questions and main findings

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.


Structure of the presentation

Structure of the presentation

  • Data and stylized facts

  • Empirical model and results

  • Implications for stress testing


Data and stylized facts

Data and Stylized Facts


The federal reserves survey of terms of business lending stbl

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


Share of risky loans across banking groups 1997

Share of risky loans across banking groups (1997)


Share of risky loans across banking groups 2007

Share of risky loans across banking groups (2007)


Empirical model and results

Empirical Model and Results


T hree features are necessary to identify a risk taking channel of monetary policy

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.


Modelling feedback between banks and the macroeconomy using a favar model

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

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Impulse responses of macroeconomic variables to an expansionary monetary policy shock

Impulse responses of macroeconomic variables toan expansionary monetary policy shock


Impulse responses of new bank lending to monetary policy shocks

Impulse responses of new bank lending tomonetary policy shocks

 Banking groups

 Risk groups


Impulse responses of new bank lending to monetary policy shocks1

Impulse responses of new bank lending tomonetary policy shocks


Impulse responses of interest rate spread to monetary policy shocks

Impulse responses of interest rate spread tomonetary policy shocks


Impulse responses of new bank lending to commercial property price shocks

Impulse responses of new bank lending tocommercial property price shocks


Summary of findings

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.


Implications for stress testing

Implications for Stress-Testing


Aim of stress testing models

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”)

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Can dynamic factor models address the challenges in stress testing

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)

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What are the implications for actual stress testing exercises

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.

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