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Bilateral cross-border banking and macroeconomic determinants

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Abstract

This paper studies the bilateral determinants of the international asset positions of banks, and subsequent bilateral adjustment during the global financial crisis and ensuing recovery phase. We find empirical support for traditional gravity-type variables. Exploiting a comprehensive dataset of bilateral bank assets, combined with a cross-country database on capital controls and macroeconomic policies, empirical evidence is provided for the effects of macroeconomic tools on the portfolio reallocation of internationally active banks. Specifically, higher current account balances in recipient countries are associated with higher inflows in debt assets, while restrictions on asset inflows and higher central bank reserves are related to lower cross-border flows of bank investment during the crisis and post-crisis periods, with heterogeneous effects across asset type. Finally, stronger institutions in recipient countries are positively associated with the international investment of banks, with inflows to debt assets being the most sensitive asset category across the financial cycle.

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Notes

  1. See also Bruno and Shin (2015) and Forbes and Warnock (2012) among others.

  2. As highlighted by Galstyan and Lane (2013), these source/destination country fixed effects filter common trends and valuation effects out of portfolio allocation, so that what remains is the purely bilateral variation.

  3. See Aviat and Coeurdacier (2007), Daude and Fratzscher (2008), Lane and Milesi-Ferretti (2008), Martin and Rey (2004), Portes and Rey (2005), Stein and Daude (2007).

  4. See Obstfeld and Rogoff (2001), Aviat and Coeurdacier (2007).

  5. Observe that \(\beta _{j}\) also controls for asset price movements, since shifts in the dollar prices of assets in country j are common to all investors (Galstyan and Lane 2013).

  6. By the application of the Frisch-Waugh-Lovell theorem, the partial coefficient can be estimated by regressing the residual vector \({\hat{\varepsilon }}_{ij}\) from \(\Delta \ln (A_{ij,07-09})=\beta _{i}+\beta _{j}+ {\textit{\textbf{g}}}_{ij,07}\varvec{\eta }+\varepsilon _{ij}\) specification on the residual vector \({\hat{\epsilon }}_{ij}\) from Eq. (1) (Davidson and MacKinnon 2004).

  7. The logic is similar to that of footnote 5.

  8. While the original SUR method assumes a balanced panel, we have opted to use an unbalanced panel in order to maximize information by employing more observations. It is important to mention that in the case of unbalanced panel the SUR method cannot be proven to deliver a positive definite residual covariance matrix. We thank Christopher Baum for pointing this out and sharing his STATA code.

  9. We opt for pairwise correlations instead of bi-variate regressions as we find scatterplots more informative and visually appealing. We do, however, report statistical significance of correlations.

  10. The IMF commenced regular production of the CPIS in 2001, however, a once off limited version of the CPIS is also available for year 1997.

  11. For a detailed discussion of the newly available sectoral breakdowns of reporting countries bilateral asset holdings see Galstyan et al. (2016).

  12. Our dataset does not account for missing debt (Alfaro and Kanczuk 2019) or correct for statistical nuances arising from the residency based principle (Galstyan 2019). Hence, we use our dataset as it is.

  13. The CPIS forms the primary dataset for our analysis. All other datasets used in gravity-type regressions are matched against the bilateral identifiers of the CPIS. This also implies that for the correlation analysis, the CPIS partners constitute the primary identifiers against which the other datasets are merged.

  14. The number of countries varies by asset class. See the Appendix for a detailed list of countries.

  15. The significance of imports also highlights the general importance of bilateral linkages.

  16. See footnotes 5 and 6.

  17. For this purpose, we use only statistically significant fixed effects. Across all specification, the average of the fraction of statistically significant fixed effects to the total estimated number of fixed effects is 72%, the minimum and the maximum stand at 15 and 100%, while the median stands at 78%. In only one specification the fraction of statistically significant effects to the total number of estimated effects is below 60%.

  18. To re-iterate, our preference for this approach is broadly based on Pesaran and Smith (2014), who study the problem of interpreting the signs of estimated coefficients in multivariate time-series regressions and show that the sign of the total impact (both direct and indirect) can be obtained by using a bi-variate regression. Furthermore, we opt for pairwise correlations instead of bi-variate regressions as we find scatterplots more informative and visually appealing.

  19. For simplicity, we will refer to the extracted fixed-country characteristics as FE-asset.

  20. Due to the unbalanced nature of the extracted fixed effects we choose to use bivariate correlations instead of multivariate correlations with a smaller set of overlapping observations.

  21. These variables capture the state of policy at the beginning of period under consideration.

  22. Sectoral detail in the Coordinated Direct Investment Survey of the IMF would provide for complete bilateral positions of banks.

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Authors and Affiliations

  1. Central Bank of Ireland, Dublin, Ireland

    Mary Everett & Vahagn Galstyan

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  1. Mary Everett
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  2. Vahagn Galstyan
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Correspondence to Vahagn Galstyan.

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We thank Philip Lane for invaluable comments. The views expressed in this paper are personal and do not represent the views of the Central Bank of Ireland.

Appendix: Sample of reporting countries

Appendix: Sample of reporting countries

The reporters are Argentina, Australia, Austria, Bangladesh, Belarus, Belgium, Bolivia, Brazil, Bulgaria, Chile, Colombia, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Honduras, Hungary, Iceland, India, Indonesia, Israel, Italy, Japan, Kazakhstan, Korea, Kuwait, Latvia, Lithuania, Mexico, Mongolia, Netherlands, Norway, Portugal, Republic of Pakistan, Romania, Russian Federation, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Thailand, Turkey, Ukraine, United Kingdom, Venezuela.

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Everett, M., Galstyan, V. Bilateral cross-border banking and macroeconomic determinants. Rev World Econ 156, 921–944 (2020). https://doi.org/10.1007/s10290-020-00387-x

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