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The national segmentation of euro area bank balance sheets during the financial crisis

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Abstract

This paper analyses bank balance sheet data in conjunction with macroeconomic and other financial variables. Our aim is to understand the nature of the instability in financial intermediation in the euro area during the recent financial crises. We define “large changes” as significant departures in the actual evolution of balance sheet variables during the crisis from their historical association with the business and financial cycles. In the course of the global 2008–2009 financial crisis, such “large changes” were features of the behaviour of cross-border inter-bank flows, both within the euro area and between the euro area and the rest of the world. By contrast, retail assets and liabilities, as well as inter-bank flows among banks of the same country, did not significantly deviate from historical regularities. Since the euro area sovereign crisis of 2011–2012, “large changes” have been more pervasive. In particular, a significant home bias in the sovereign bond market has emerged.

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Fig. 1

Source: ECB

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Source: ECB

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Notes

  1. See ECB (2014) for an extensive discussion and survey of the literature on the determinants of sovereign bond yields before and after the sovereign debt crisis.

  2. Regulation (EC) No XX/2013 of the ECB of 24 September 2013 concerning the balance sheet of the monetary financial institutions sector (Recast), OJ L 15, 7.11.2013, p. 14.

  3. The data used in the paper relate to notional stocks, which are constructed based on measure of transactions and therefore are corrected for series breaks arising, e.g. from mergers and acquisitions or changes in the classification of counterparties (see the “Annex” for further discussion).

  4. As hyperpriors for \(\lambda , \mu \) we choose Gamma densities with mode equal to 0.2, 1 and standard deviations equal to 0.4, 1 respectively. Our prior on \(\psi \), i.e. the prior mean of the main diagonal of \(\Sigma \), is an inverse Gamma with scale and shape equal to \((0.02)^{2}\).

  5. See Giannone et al. (2015) for the details of the MCMC algorithm.

  6. In this paper, we use the simulation smoother described in Carter and Kohn (1994).

  7. While loans to other counterparties can also be object of securitisation activities, these are not reflected in the paper as they are assumed to have lower magnitude compared to the impact securitisation has on loans to non-financial sectors.

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Authors and Affiliations

  1. European Central Bank, Sonnemannstrasse 20, 60314, Frankfurt am Main, Germany

    A. Colangelo & M. Lenza

  2. Federal Reserve Bank of New York, 33 Liberty Street, New York, NY, 10045, USA

    D. Giannone

  3. Goldman Sachs, 120 Fleet Street, London, EC2A 2BB, UK

    H. Pill

  4. London Business School, Regent’s Park, London, NW1 4SA, UK

    L. Reichlin

  5. CEPR, 33 Great Sutton Street, London, EC1V 0DX, UK

    D. Giannone & L. Reichlin

  6. ECARES-Université Libre de Bruxelles, Av. F. D. Roosevelt, 1050, Bruxelles, Belgium

    A. Colangelo & M. Lenza

Authors
  1. A. Colangelo
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  2. D. Giannone
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  3. M. Lenza
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  4. H. Pill
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  5. L. Reichlin
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Corresponding author

Correspondence to M. Lenza.

Additional information

The views expressed in this paper are those of the authors and do not necessarily reflect the position of the European Central Bank, the Eurosystem, the Federal Reserve Bank of New York, the Federal Reserve System or Goldman Sachs.

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Supplementary material 1 (pdf 133 KB)

Data annex

Data annex

Our dataset includes 22 bank balance sheet variables and 12 macroeconomic and financial variables of the euro area. All variables are available at the monthly frequency and the sample ranges from January 1999 to October 2015. The bank balance sheet series are from the dataset on Monetary Financial Institutions (MFIs) balance sheet statistics compiled by the European Central Bank. The macroeconomic and financial block of the database consists of variables drawn from the Statistical Data Warehouse of the European Central Bank.

As regards the balance sheet data, the aggregations we use in the data analysis are constructed from the main instrument categories, by grouping sectors of the counterparties to yield information on MFI positions vis-à-vis euro area residents, and specifically the Eurosystem, other MFIs, financial corporations other than MFIs, the government sector, firms and households, and the rest of the world. Special attention is paid to the so-called euro area wholesale lending market, which is defined as the lending market where MFIs operate along with financial intermediaries other than MFIs. Those consist of insurance corporations, pension funds, non-MMF investment funds, other financial intermediaries (including captive financial institutions and money lenders) and financial auxiliaries.

In addition, MFI balance sheet statistics allow the split between intra-euro area domestic and cross-border positions, and we take these breakdowns into account when studying inter-MFI positions. With this respect it is worth stressing that the operational framework of the Eurosystem provides that monetary policy operations are always conducted via the relevant NCB; the resulting positions are thus to be considered, by definition, domestic.

When analysing developments in MFI loans to firms and households, we also take into account the impact of loan securitisation (and other loan transfers) on MFI balance sheets.Footnote 7

On this basis, the variables employed in the empirical analysis in the main text are:

Bank assets

  • Loans to euro area firms and households

  • Loans to euro area government

  • Deposit and loan claims on the Eurosystem

  • Domestic loans to MFIs

  • Intra-euro area cross-border loans to MFIs

  • Other euro area wholesale loans (This series covers both domestic and intra-euro area cross-border positions vis-à-vis insurance corporations, pension funds, non-MMF investment funds, other financial intermediaries (including captive financial institutions and money lenders) and financial auxiliaries.)

  • Loans to non-euro area residents

  • Domestic holdings of government debt securities

  • Intra-euro area cross-border holdings of government debt securities

  • Holdings of euro area MFI securities

  • Holding of securities issued by the euro area private sector

  • Holdings of debt securities issued by non-euro area residents

  • Holdings of equity issued by non-euro area residents

  • Remaining assets

Bank liabilities

  • Deposits of euro area firms and households

  • Deposits of euro area government

  • Deposit and loan liabilities to the Eurosystem

  • Domestic deposits from MFIs

  • Intra-euro area cross-border deposits from MFIs

  • Other euro area wholesale deposits (This series includes deposits and loan liabilities of euro area MFIs (excluding the Eurosystem) vis-à-vis euro area financial intermediaries other than MFIs, i.e. insurance corporations, pension funds, non-MMF investment funds, other financial intermediaries and financial auxiliaries. Long-term deposits vis-a-vis FVCs are however excluded from our exercise as they are usually fictionally imputed to the MFI balance sheet as a balancing liability when loan securitisation activities (or other loan sales) do not result in the de-recognition of the assets).

  • Deposits from non-euro area residents

  • Debt securities issued

The macroeconomic and financial block

  • Industrial production (euro area)

  • Harmonised Index of Consumer prices (euro area), HICP

  • Unemployment rate (euro area)

  • Producer price index (euro area), PPI

  • 3-month Euribor rates (euro area)

  • Industrial production (US)

  • Consumer price index (US), CPI

  • Federal Funds rate (US)

  • DJ Eurostoxx (stock prices)

  • Sovereign bond yields, 2 years maturity (euro area)

  • Sovereign bond yields, 5 years maturity (euro area)

  • Sovereign bond yields, 10 years maturity (euro area)

The data enter the VAR model in annual log levels (i.e. 12*log) except the rates (i.e. the Euribor, the Federal Funds rate, the sovereign bond yields and the unemployment rate) which enter the model in levels.

More details on the construction of the data series are available in the web appendix.

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Colangelo, A., Giannone, D., Lenza, M. et al. The national segmentation of euro area bank balance sheets during the financial crisis. Empir Econ 53, 247–265 (2017). https://doi.org/10.1007/s00181-016-1221-2

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