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Illiquidity Transmission in a Three-Country Framework: A Conditional Approach

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Abstract

In this paper we investigate the causality of liquidity in a three-country framework. Due to evidence that liquidity is of greater importance during crises and to provide a deeper insight into the dynamics of liquidity shocks between the United States, Germany, and the United Kingdom, we estimate a Markov-switching vector autoregression model and calculate impulse response functions for different economic states. Indeed, we find liquidity spillovers to be more pronounced during unstable periods and identify the leading role of the United States. Moreover, we use numerous macroeconomic and financial market variables to analyze the specific factors behind liquidity. The overall economic outlook and the condition of the U.S. financial market turn out to be important.

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Notes

  1. 1.

    Malkhozov et al. (2014) estimate the noise measure developed by Hu et al. (2013) for 6 countries and shortly discuss country specific and global liquidity events. However, the focus of their study is the effect of funding constraints on international stock returns.

  2. 2.

    E.g., the National Bureau of Economic Research (NBER).

  3. 3.

    Hu et al. (2013) show that the results are robust when using spline based methods.

  4. 4.

    See Gürkaynak et al. (2007) for further details.

  5. 5.

    The monthly median of spreads from executable quotes of all issued government bonds is on average 2.57 bp, 3.82 bp, and 2.02 bp in Germany, the U.K., and the U.S., respectively, based on Bloomberg’s CBBT pricing source (available since October 2003).

  6. 6.

    For details about the NBER recession definition see the announcement from the NBER’s Business Cycle Dating Committee (9/20/2010).

  7. 7.

    OECD recession is defined here as the period following a peak through the trough. Turning points are identified by the OECD.

  8. 8.

    In small samples the distribution of the test statistic often differs from the assumed \(\chi^{2}\) distribution, which is why we follow Candelon and Lütkepohl (2001) and perform bootstrapped versions of the tests. In all cases the null of constant parameters is rejected for many break dates.

  9. 9.

    All results are robust, whether we use original or standardized (mean zero, unit variance) data. For ease of interpretation, original data is preferable in this section while standardized data is used for impulse response functions in Sect. 3.3.

  10. 10.

    Model estimation is done based on the Scilab package Grocer. Thanks to Éric Dubois for helpful comments.

  11. 11.

    The RCM is 100 if and only if \(p_{t}=0.5\leavevmode\nobreak\ \forall t\) in a two regime model and 0 if and only if \(p_{t}\in\{0,1\}\leavevmode\nobreak\ \forall t\). Models with a RCM below 50 are usually said to be able to classify the data into different regimes, see e.g., Chan et al. (2011).

  12. 12.

    Jean-Marie Robine, Siu Lan Cheung, Sophie Le Roy, Herman Van Oyen, Frano̧is R. Herrmann, 2003 Heat Wave Project: Report on excess mortality in Europe in Summer 2003. EU Community Action Programme for Public Health, Grant Agreement 2005114.

  13. 13.

    Münchner Rückversicherung (2003): Topics Geo, Jahresrückblick Naturkatastrophen 2003, p. 23.

  14. 14.

    Thanks to Martin Ellison for providing his Ox program which confirmed our Scilab implementation.

  15. 15.

    All results for partial IRFs are qualitatively the same but with confidence bands considerably increasing with the time horizon due to reasons mentioned above and therefore not reported.

  16. 16.

    The dividend yield for the DAX 30 is available to us only since mid of 1997. Since we do not want to shorten the time interval again we use MSCI indices instead. The correlation between the dividend yield of DAX 30, FTSE 100, and S&P 500 with the dividend yield of its corresponding MSCI index is larger than 96 % anyway.

  17. 17.

    Historical data for VFTSE is only dated back until 2000. Prior to that, we construct the index using the average of the next put and call FTSE index option with a remaining maturity of at least 21 business days. The correlation of the VFTSE and our approximation is more than 98 % after 2000 and therefore our proxy seems to be appropriate.

  18. 18.

    CPI does not have the expected sign in the univariate regression. Findings are mixed in the literature (e.g., Goyenko and Ukhov 2009; Goyenko et al. 2011) depending on the maturity and whether a bond is on-/off-the-run. Using realized CPI instead of forecasts results in insignificant coefficients.

  19. 19.

    Even constructing the binary vector Stress from regime probabilities of a Markov-switching model with German and British data only does not alter the result that most explanatory variables are based on U.S. data.

  20. 20.

    To strengthen our results, we used other approaches like lasso as robustness check. The results remain unaffected. Moreover, since our dependent variable Stress is an indicator for global liquidity crises we also defined global explanatory variables using first principal components of country-specific variables of the same type in the stepwise regression. However, this approach leads to an adjusted \(R^{2}\) of only 0.41.

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Acknowledgements

We thank Philipp Schuster and the anonymous referee for helpful comments and suggestions and gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (Grant No. UH 107/3–1).

Author information

Correspondence to Stefan Fiesel or Marliese Uhrig-Homburg.

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Fiesel, S., Uhrig-Homburg, M. Illiquidity Transmission in a Three-Country Framework: A Conditional Approach. Schmalenbach Bus Rev 17, 261–284 (2016). https://doi.org/10.1007/s41464-016-0016-5

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Keywords

  • Liquidity Spillover
  • Multi-Country
  • Slow-Moving Capital
  • Regime-Switching
  • Drivers of Liquidity

JEL-Classification

  • G01
  • G12
  • G15