Findes ikke på dansk
Hans Dewachter
- 12 April 2019
- WORKING PAPER SERIES - No. 2261Details
- Abstract
- The paper proposes a framework for assessing the impact of system-wide and bank-level capital buffers. The assessment rests on a factor-augmented vector autoregression (FAVAR) model that relates individual bank adjustments to macroeconomic dynamics. We estimate FAVAR models individually for eleven euro area economies and identify structural shocks, which allow us to diagnose key vulnerabilities of national banking systems and estimate short-run economic costs of increasing banks’ capitalisation. On this basis, we run a fully-fledged cost-benefit assessment of an increase in capital buffers. The benefits are related to an increase in bank resilience to adverse shocks. Higher capitalisation allows banks to withstand negative shocks and moderates the reduction of credit to the real economy that ensues in adverse circumstances. The costs relate to transitory credit and output losses that are assessed both on an aggregate and bank level. An increase in capital ratios is shown to have a sharply different impact on credit and economic activity depending on the way banks adjust, i.e. via changes in assets or equity.
- JEL Code
- E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 30 January 2019
- WORKING PAPER SERIES - No. 2230Details
- Abstract
- This paper provides evidence on the strategic lending decisions made by banks facing a negative funding shock. Using bank-firm level credit data, we show that banks reallocate credit within their loan portfolio in at least three different ways. First, banks reallocate to sectors where they have a high market share. Second, they also reallocate to sectors in which they are more specialized. Third, they reallocate credit towards low-risk firms. These reallocation effects are economically large. A standard deviation increase in sector market share, sector specialization or firm soundness reduces the transmission of the funding shock to credit supply by 22, 8 and 10%, respectively.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages - Network
- Research Task Force (RTF)
- 11 December 2018
- WORKING PAPER SERIES - No. 2214Details
- Abstract
- We assess the contribution of economic and financial factors in the determination of euro area corporate bond spreads over the period 2001-2015. The proposed multi-market, no-arbitrage affine term structure model is based on the methodology proposed by Dewachter, Iania, Lyrio, and Perea (2015). We model jointly the ‘risk-free curve’, measured by overnight index swap (OIS) rates, and the corporate yield curves for two rating classes (A and BBB). The model includes four spanned and six unspanned factors. We find that, in general, both economic (real activity and inflation) and financial factors (proxying risk aversion, flight to liquidity and general financial market stress) play a significant role in the determination of the spanned factors and hence in the dynamics of the risk-free yield curve and corporate bond spreads. Across the risk-free OIS curve, macroeconomic and financial factors are each responsible on average for explaining 30 and 65 percent of yield varation, respectively. For A- and BBB-rated corporate debt, the selected financial variables explain on average 50 percent of the variation in corporate spreads during the last decade.
- JEL Code
- E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
- 9 January 2018
- OCCASIONAL PAPER SERIES - No. 205Details
- Abstract
- This paper studies the cyclical properties of real GDP, house prices, credit, and nominal liquid financial assets in 17 EU countries, by applying several methods to extract cycles. The estimates confirm earlier findings of large medium-term cycles in credit volumes and house prices. GDP appears to be subject to fluctuations at both business-cycle and medium-term frequencies, and GDP fluctuations at medium-term frequencies are strongly correlated with cycles in credit and house prices. Cycles in equity prices and long-term interest rates are considerably shorter than those in credit and house prices and have little in common with the latter. Credit and house price cycles are weakly synchronous across countries and their volatilities vary widely – these differences may be related to the structural properties of housing and mortgage markets. Finally, DSGE models can replicate the volatility of cycles in house and equity prices, but not the persistence of house price cycles.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy