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Liquidity Funding Shocks: the Role of Banks’ Funding Mix

Abstract

This study attempts to evaluate the impact of an increase in banks’ funding stress and its transmission to the real economy, taking into account different funding sources banks can rely on. Using aggregate data from eight Euro area financial systems, we find that following a liquidity funding shock, both credit and GDP decline in different amounts and lengths. GDP reverts faster than credit. Furthermore, periphery countries experience a more pronounced fall in deposits and credit growth and the negative effects from the shock last longer than in core countries. Banks’ funding seems to play a relevant role as periphery countries rely more on wholesale funding during normal times.

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Fig. 1

Source: Thomson Reuters

Fig. 2

Source: ECB

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Notes

  1. Long-term refinancing operations were introduced in June 2009 to support bank lending and liquidity in the Euro area money market.

  2. Data corresponding to GDP and GDP deflator were collected from Eurostat according to the ESA10 procedure. All data are in annual growth rates. All countries presented seasonally adjusted data, except for Ireland which only provides non-seasonally adjusted data. Hence, we apply annual growth rates to all series to control for any sort of seasonal effect. Financial aggregates were taken from the ECB aggregated balance sheet of Euro area MFIs (excluding the Euro system) published in the Statistical Data Warehouse of the ECB. Euribor–OIS spread is taken from Thomson-Reuters Datastream.

  3. We thank Love and Zicchino (2006) and Abrigo and Love (2016) for their Stata code, and we refer to them for further details.

  4. The baseline model is chosen given the limitation of our time series to 48 quarters. The fact that the estimation uses lags as instruments reduces the number of degrees of freedom when estimating the sub-samples. Moreover, it is not feasible to estimate the extended model with nine endogenous variables with additional lags.

  5. These results are interpreted in this example relative to the sample average growth of variables for the whole sample, as an approximation to a stationary growth path.

  6. During downturns, households’ saving rate in core countries remains relatively stable while in periphery countries households increase their savings for precautionary reasons at the beginning but after some years their saving rates start falling in order to maintain their consumption level.

  7. As we increase the time horizon for the responses, the standard errors of the estimated responses increase (especially for the subsamples), which explains why the cumulative response for all countries is a bit lower than for the core.

  8. Periphery countries display higher levels of leverage in our sample, which makes them more exposed to financial system stress. In particular the average capital to assets ratio of core countries is 16.75 compared to 12.02 in periphery countries for the period studied. In addition, periphery countries are more bank-based, so banking system weaknesses have a higher impact on the rest of the economy.

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Acknowledgments

The authors would like to thank participants at the 6th International Conference of the Financial Engineering and Banking Society for helpful discussions. We also thank two anonymous referees as well as the invited editor Ned Prescott for their insightful comments. Finally, we are grateful to Marta González Escalonilla for excellent research assistance.

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Correspondence to Antonio Álvarez.

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Appendix

Appendix

Table 4 Estimation results: all countries. VAR panel is estimated by GMM with t statistics in parentheses. Fixed effects are removed prior to estimation (see Section 5)
Table 5 Estimation results: core countries only. VAR panel is estimated by GMM with t statistics in parentheses. Fixed effects are removed prior to estimation
Table 6 Estimation results: periphery countries only. VAR panel is estimated by GMM with t statistics in parentheses. Fixed effects are removed prior to estimation
Fig. 5
figure 5

Impulse response functions: GDP shock panel

Fig. 6
figure 6

Impulse response functions: Spread shock panel

Fig. 7
figure 7

Impulse response functions: Spread shock core countries

Fig. 8
figure 8

Impulse response functions: Spread shock periphery countries

Table 7 Cumulative impulse response functions of core countries to a temporary shock of 20 basis points to the spread level
Table 8 Cumulative impulse response functions of periphery countries to a temporary shock of 20 basis points to the spread level

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Álvarez, A., Fernández, A., García-Cabo, J. et al. Liquidity Funding Shocks: the Role of Banks’ Funding Mix. J Financ Serv Res 55, 167–190 (2019). https://doi.org/10.1007/s10693-019-00314-8

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Keywords

  • Liquidity funding shocks
  • ECB policy
  • Panel VAR

JEL Classification

  • E50
  • E58
  • F45