TARGET balances in the euro area: the case of Germany

ABSTRACT

Applying a BVAR model, the present paper first identifies the possible drivers of Germany’s TARGET claims. In this context, in terms of potential causes, a distinction is made between a rise in the global risk assessment, tensions within the euro area, and European monetary policy. It becomes evident that the TARGET flows between 2015 and 2017 can be ascribed in large part to monetary policy and to a minor extent to the risk assessment within the euro area. At the peak of the European debt crisis between 2010 and mid-2012, the TARGET flows were affected by uncertainty in the euro area as a dominant factor, although global factors also played a key role according to the model. The BVAR model we use opens up the possibility of studying the causes of current fluctuations in Germany’s TARGET claims.

KEYWORDS:

• Target balances
• risk
• monetary policy
• bayesian vector autoregression
• sign restrictions

JEL CLASSIFICATION:

• C32
• E52
• F32

Acknowledgments

We are grateful to Joscha Beckmann, Ulrich Grosch, Malte Knüppel and Stephan Kohns as well as participants of the Bundesbank research seminar for helpful comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily coincide with the views of the Deutsche Bundesbank or the Eurosystem.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The first generation of TARGET, the real-time gross settlement (RTGS) system owned and operated by the Eurosystem, commenced operations on 4 January 1999, a few days after the launch of the euro. Migration to the more advanced TARGET2 took place in successive stages in 2007 and 2008. The term “TARGET” is used throughout the present paper to refer to both the first and second generation of the system.

² See ECB press release, 10 May 2010.

³ A robustness analysis with an uninformative Minnesota type prior delivered qualitatively similar results.

⁴ Generally, there is a wide range of measures that reflect uncertainty. In this paper, we use the VIX, since it appropriately records risk assessment in financial markets, which are key for the dynamics of the other variables used in this model. Furthermore, its calculation is transparent and the index is timely available in a monthly frequency. Other indicators like survey data, composite indices or indicators based on big data gauge uncertainty in a broader sense and may be more relevant for other economic variables. However, additional information is often bought at the price of less transparency.

⁵ Estimating the model with a negative restriction on $EAsprea{d}_t$ yields qualitatively similar results. Results are available upon request.

⁶ We modified the BEAR Toolbox in order to include such unidentified shocks. In the present case, two shocks are required to consider all sign restrictions not already implied by the identified shocks.

⁷ This shock of rising global risk differs from a possible monetary policy impulse from the United States in that it has the opposite effect on the VIX. An accommodative monetary policy by the Fed should, taken in isolation, lower the risk assessment on the financial markets. It is not explicitly identified as it is unlikely to have any clear-cut and systematic impact on Germany’s TARGET balances. Possible effects are captured in the model by the two unidentified shocks.

⁸ This requires the definition of two unidentified shocks in this model as well.

⁹ These restrictions represent a euro area-specific settlement process and account for Germany’s role as a financial gateway to the world. If a national central bank in the euro area (other than the Bundesbank) purchases a domestic government bond from an investor outside the euro area, the transaction is usually settled via a commercial bank in Germany. This process translates into an increase in German target claims and a liability of the German commercial bank vis-á-vis the trading partner outside the euro area.

¹⁰ Shadow interest rates by Leo Krippner can be obtained from the website of the Reserve Bank of New Zealand, https://www.rbnz.govt.nz/research-and-publications/research-programme/additional-research/measures-of-the-stance-of-united-states-monetary-policy/comparison-of-international-monetary-policy-measures. Note that the use of shadow rates requires a negative sign on the restriction on the policy variable.