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The impact of the Brexit vote on UK financial markets: a synthetic control method approach

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Abstract

We estimate how the UK financial markets would have evolved if the Remain camp had won the referendum. To construct the counterfactual, we use the synthetic control method. Our results suggest that there would not have been any significant change in the development of the FTSE 100 Index in the medium to long term if there had not been a referendum. On the other hand, we find a significantly negative effect of 1.2 percentage points on the 10-year bond yield. Given the geopolitical circumstances in mid 2016, financial agents investing in the pound could have sought safer investment options represented by longer-term government bonds, which consequently could result in lower bond yields.

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Notes

  1. The presidential election in the USA, the Syria crisis and the EU immigration policy discussion.

  2. See Firpo and Possebom (2017) for a rich list of studies using the SCM.

  3. The start period was chosen due to missing data.

  4. See Table 2 for descriptive statistics of the variables.

  5. See Abadie et al. (2010), where it is proved that \({{\hat{\upsilon }}_{it}}\) is an unbiased estimator of \({\upsilon _{it}}\).

  6. See Table 8 in Appendix for the relative importance of our covariates.

  7. See Abadie et al. (2011), which describes other approaches for choosing the weights \({\{v_{1},\ldots ,v_{k}\}}\)

  8. The RMSPE has the following formula: \({RMSPE=\left( \dfrac{1}{{T_0}}\sum _{t=1}^{T_0} (Y_{1t}-\sum _{j=2}^{J+1}w^{*}_jY_{jt})^{2}\right) ^{\dfrac{1}{2}}}\)

  9. See section 3 in Firpo and Possebom (2017) for the details.

  10. We can choose random periods prior to the intervention.

  11. The formula mentioned in the footnote in Sect. 2.2.

  12. For constant in time intervention effects, they exclude the term \((t-T_{0})\) from Eq. 7.

  13. The inference procedure is referred to as the first method.

  14. See Table 9 in Appendix for quantitative details about the result in the period after the Brexit vote.

  15. Additionally, we run SCM with all OECD countries in the donor pool. The results are not different from our original findings (Fig. 11b and Table 7).

  16. See Table 4 in Appendix for details.

  17. Additionally, we run the SCM with all OECD countries in the donor pool. The result confirms our original findings (Fig. 11a and Table 7).

  18. Given this finding, Fig. 9a shows the result for statistical significance without the USA in the control group. The confidence interval includes the zero function, therefore the result is not statistically significant.

  19. We thank to anonymous referee for this suggestion.

  20. See Table 6 in the Appendix for details.

  21. We run the SCM with all OECD countries in the donor pool. The results are not different from our original findings (Fig. 11c and Table 7).

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Acknowledgements

This project has received funding from GAUK No. 1250218 and from project SVV 260 463. The author is grateful to Tomas Havranek and Vaclav Broz for their valuable comments and suggestions.

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Authors and Affiliations

  1. Institute of Economic Studies, Faculty of Social Science, Charles University, Prague, Czech Republic

    Matej Opatrny

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  1. Matej Opatrny
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Appendices

Appendix 1: Descriptive statistics

See Tables 1 and 2.

Table 1 UK trade.
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Table 2 Descriptive statistics of the variables used for the SCM computation.
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Appendix 2: Robustness checks

2.1 Stock index robustness checks

See Fig. 7 and Table 3

Fig. 7
figure 7

Source: Author’s computation based on SCM

Stock Index—changing control group.

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Table 3 Country weights computed by SCM—Stock Index.
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2.2 10-year bond yield robustness checks

See Figs. 8, 9 and Tables 4, 5.

Fig. 8
figure 8

Source: Author’s computation based on SCM

Changing control group for 10-year bond yield results.

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Fig. 9
figure 9

Source: Author’s computation based on SCM

Changing control group for 10-year bond yield results.

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Table 4 Country Weights Computed by SCM—10Y Bonds.
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Table 5 Country weights computed by SCM—10Y Bonds, Period from 01/2013.
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2.3 REER robustness checks

See Fig. 10 and Table 6

Fig. 10
figure 10

Source: Author’s computation based on SCM

REER—changing control group.

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Table 6 Country weights computed by SCM—REER.
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2.4 All OECD countries robustness check

See Fig. 11 and Table 7

Fig. 11
figure 11

Source: Author’s computation based on SCM

10-year bond yield results.

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Table 7 Country weights computed by SCM—All OECD Countries.
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2.5 Augmented SCM robustness check

In all cases we use ridge regression as the outcome model with no covariates (Ben-Michael et al. 2018).

See Figs. 12 and 13

Fig. 12
figure 12

Source: Author’s computation based on SCM

ASCM, All OECD Countries.

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Fig. 13
figure 13

Source: Author’s computation based on SCM

ASCM, Non-European OECD countries.

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Appendix 3: Covariates weights

See Table 8.

Table 8 Weights for covariates computed by SCM.
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3.1 Numerical results

See Table 9

Table 9 Synthetic outcome results after the brexit vote.
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Opatrny, M. The impact of the Brexit vote on UK financial markets: a synthetic control method approach. Empirica 48, 559–587 (2021). https://doi.org/10.1007/s10663-020-09481-7

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