# Exchange Rate Misalignment and Capital Flight from Botswana: A Cointegration Approach with Risk Thresholds

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

**H**

_{1}.**H**

_{2}.#### 3.1. Time Series Properties and Estimators

#### 3.2. Data Diagnostics

## 4. Results

#### 4.1. The Causes of Capital Flight from Botswana

#### 4.1.1. The Impact of Overvaluation on Capital Flight

#### 4.1.2. The Impact of Undervaluation on Capital Flight

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Botswana’s outward capital flight (KF). Positive values indicate resources leaving Botswana to other economies and less preference for domestic assets (outward capital flight). Negative values signal inward capital flight and less preference for foreign assets.

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1 | Estimated using EViews version 10. |

2 | Zhang et al. (2019) used 25% and 50% thresholds for their early warning system while Kaminsky et al. (1998) used a 10% threshold for the signal extraction early warning system. However, these thresholds are too high to capture misalignment in this study. The 5% threshold is selected because it is optimal for detecting significant REER misalignment. When the threshold was set to 3%, 4% and 5% the results were similar with minimum disparities between the coefficients. However, an increase in overvaluation beyond the 5% threshold causes high outward capital flight. Therefore, the 5% percent threshold is also an early warning indicator for high capital flight from Botswana. The maximum threshold value accommodated by $MISREER$ observations was 8%. |

3 | Capital flight can be either massive capital leaving a particular country to other nations (outward capital flight) or massive capital from other nations entering the domestic economy (inward capital flight). This study evaluates the magnitude of capital flight from Botswana to other economies. |

4 | $\mathrm{KF}$ was calculated as $\left(\frac{{\mathrm{VKF}}_{\mathrm{t}}}{{\mathrm{GDP}}_{\mathrm{t}}}\right)$% where VKF is the volume of capital flight measured using the residual method and GDP is Botswana’s total GDP. |

5 | In this study, $IRD$ is the difference between Botswana and South Africa’s real interest rate. An increase in $IRD$ is expected to reduce capital flight (δ _{4} < 0). However, if $IRD$ is taken as the difference between South Africa and Botswana’s real interest rate, an increase in $IRD$ will encourage capital flight (δ_{4} > 0). |

6 | Kganetsano (2007) argue that macroeconomic changes in South Africa are likely to affect Botswana, given the large size of the South African economy and the volume of its exports to Botswana. |

7 | For the exchange rate fundamentals, the value of the regressors $k$ was greater than 7, therefore only Pesaran et al. (2001) critical values were followed. |

8 | A variable ${x}_{t}$ is long-run forcing if there is no feedback from the long-run equilibrium relationship on the change of ${x}_{t}$. Consequently, there will be no information on the marginal process for ${x}_{t}$ about the parameters of the relationship (Pesaran et al. 2001). |

9 | According to the International Monetary Fund (2007), South Africa’s competitive advantage overshadows Botswana’s competitive advantage, given the large domestic market and abundant labour supply in South Africa. Consequently, the International Monetary Fund (2007) recommends that Botswana should import from South Africa rather than produce goods domestically. |

10 | SACU was formed in 1910 to promote cross-border movement of goods produced by the member states without import tariffs or import quotas. Other goals of the organisation include promotion of regional integration, poverty reduction and stable democratic governments. Botswana is also a member of SADC, which promotes socioeconomic cooperation and political stability. |

11 | In addition to the pula, Botswana’s foreign reserves are held in the form of US dollars and the SDR. Bank of Botswana (2017) is responsible for the management of foreign reserves to ensure liquidity and return on reserve assets. |

12 | In early warning systems, a sharp decline in foreign reserves is an indicator of an imminent crisis (see Kaminsky et al. 1998). |

13 | At the 3% threshold, the coefficient for $OVER$ is 8.7746. The coefficient for OVER is 8.0067 at the 4% and 5% thresholds. When the threshold is 6%, 7%, and 8% the coefficient for OVER is three times greater (24.0354, 24.0354, and 24.2009) than the coefficient at the 5% threshold. This shows that the higher the overvaluation, the higher the expectations of devaluation resulting in large capital flight. Policymakers should tolerate overvaluation only up to 5%. The coefficient for LNOPENNESS increases from 49.4755 at the 5% level to 57.5182 at the 6% and 7% thresholds. There was less variation in the coefficients of RESERVES when the threshold was altered. |

14 | At the 3% threshold, the coefficient for $UNDER$ is 3.3463. The coefficient is 4.0487 at the 4% threshold. The coefficient increases to 12.4325 at the 8% threshold level. There was less variation in the coefficients of LNOPENNESS and RESERVES when the threshold was altered. |

15 | Narayan (2005) critical values were further used to determine the long-run equilibrium relationship between $\mathrm{KF}$ and the regressors (unrestricted constant without the time trend). The computed F-statistic for F(KF│LNOPENNESS, RESERVES) is 5.1459 which is significant at the 5% level. The computed F-statistic for F(KF│OVER, LNOPENNESS, RESERVES) is 14.0664 which is significant at the 1% level. The computed F-statistic for F(KF│UNDER, LNOPENNESS, RESERVES) is 5.9052 which is significant at the 5% level. |

**Table 1.**Results of the F-test for the long-run relationship between the real effective exchange rate (LNREER) and its determinants.

Model Specification (k = 8) | Optimal Lag | RC | UC | UC + UT |
---|---|---|---|---|

$F\left(\mathrm{LNREER}|\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 0 | 3.2037 ** | 3.5175 ** | 3.2349 * |

$F\left(\mathrm{LNTOT}|\mathrm{LNREER},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 1 | 6.4683 *** | 7.1756 *** | 6.7281 *** |

$F\left(\mathrm{LNGOV}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{GDP},\mathrm{FDI},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 1 | 3.9829 *** | 4.7406 *** | 4.3554 ** |

$F\left(\mathrm{GDP}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{FDI},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 0 | 6.9034 *** | 7.6376 *** | 7.1384 *** |

$F\left(\mathrm{FDI}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 2 | 15.3922 *** | 16.8090 *** | 15.8756 *** |

$F\left(\mathrm{AID}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{LNOPENNESS},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 2 | 7.7685 *** | 8.1055 *** | 11.1441 *** |

$F\left(\mathrm{LNOPENNESS}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{AID},\mathrm{LNDEBT},\mathrm{LNCAPITAL}\right)$ | 1 | 6.4728 *** | 7.1814 *** | 6.8454 *** |

$F\left(\mathrm{LNDEBT}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{LNOPENNESS},\mathrm{AID},\mathrm{LNCAPITAL}\right)$ | 2 | 4.4928 *** | 4.9825 *** | 8.0778 *** |

$F\left(\mathrm{LNCAPITAL}|\mathrm{LNREER},\mathrm{LNTOT},\mathrm{LNGOV},\mathrm{GDP},\mathrm{FDI},\mathrm{AID},\mathrm{LNOPENNESS},\mathrm{LNDEBT}\right)$ | 1 | 1.3175 | 1.4493 | 1.5992 |

Model: SBIC-ARDL (1, 0, 0, 0, 0, 0, 0, 1, 0) | ||
---|---|---|

Regressor | Long-Run Coefficient | t-Statistic (p-Value) |

Constant | 5.1731 *** | 7.1801 (0.0000) |

$\mathrm{LNTOT}$ | −0.3551 *** | −3.9642 (0.0004) |

$\mathrm{GDP}$ | −0.0038 * | −1.7363 (0.0931) |

$\mathrm{AID}$ | −0.0193 *** | −5.4407 (0.0000) |

$\mathrm{LNOPENNESS}$ | 0.2374 ** | 2.1781 (0.0377) |

$\mathrm{LNDEBT}$ | −0.0553 *** | −2.8405 (0.0082) |

$\mathrm{LNCAPITAL}$ | 0.0462 | 1.1897 (0.2438) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNTOT}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 15.7152 (0.0001) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{GDP}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 3.0146 (0.0825) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{AID}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 29.6016 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 4.7440 (0.0294) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNDEBT}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 8.0683 (0.0045) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNCAPITAL}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 1.4154 (0.2342) |

Model: SBIC-ARDL (1, 0, 0, 0, 0, 0, 0, 1, 0) | ||
---|---|---|

Regressor | Short-Run Coefficient | t-Statistic (p-Value) |

Constant | −0.0021 | −0.3118 (0.7576) |

$\Delta \mathrm{LNTOT}$ | −0.2154 ** | −2.0713 (0.0480) |

$\Delta \mathrm{GDP}$ | −0.0031 * | −1.8999 (0.0682) |

$\Delta \mathrm{AID}$ | −0.0192 *** | −3.7559 (0.0008) |

$\Delta \mathrm{LNOPENNESS}$ | 0.2167 | 1.6919 (0.1022) |

$\Delta \mathrm{LNDEBT}$ | −0.0673 ** | −2.4281 (0.0221) |

$\Delta \mathrm{LNCAPITAL}$ | 0.0258 | 0.6765 (0.5045) |

$ec{m}_{t-1}$ | −0.6869 *** | −3.8045 (0.0007) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNTOT}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 4.2903 (0.0383) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{GDP}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 3.6098 (0.0574) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{AID}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 14.1070 (0.0002) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 2.8624 (0.0907) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNDEBT}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 5.8954 (0.0152) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNCAPITAL}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.4576 (0.4987) | |

${\mathrm{H}}_{0}$: Coefficient on $ec{m}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 14.4740 (0.0001) ** | |

${R}^{2}$ | 0.5124 | |

Adjusted ${R}^{2}$ | 0.3860 | |

Standard error of the regression | 0.0386 | |

Observations | 35 |

Hypothesis | ${\mathit{\chi}}^{\mathbf{2}}$ | p-Value | Causality |
---|---|---|---|

$\mathrm{LNCAC}$ does not Granger-cause $\mathrm{MISREER}$ | 7.6138 | 0.0222 ** | $\mathrm{LNCAC}\to \mathrm{MISREER}$ |

$\mathrm{MISREER}$ does not Granger-cause $\mathrm{LNCAC}$ | 6.4705 | 0.0393 ** | $\mathrm{LNCAC}\leftarrow \mathrm{MISREER}$ |

$\mathrm{LNDEBTC}$ does not Granger-cause $\mathrm{MISREER}$ | 0.6962 | 0.7060 | No |

$\mathrm{MISREER}$ does not Granger-cause $\mathrm{LNDEBTC}$ | 0.2162 | 0.8991 | No |

$\mathrm{EMS}$ does not Granger-cause $\mathrm{MISREER}$ | 0.2431 | 0.8856 | No |

$\mathrm{MISREER}$ does not Granger-cause $\mathrm{EMS}$ | 0.7286 | 0.6947 | No |

$\mathrm{LNRGDPC}$ does not Granger-cause $\mathrm{MISREER}$ | 2.7267 | 0.2558 | No |

$\mathrm{MISREER}$ does not Granger-cause $\mathrm{LNRGDPC}$ | 1.1045 | 0.5757 | No |

Model: SBIC-ARDL (1, 0, 1) | ||

Regressor | Long-Run Coefficient | t-Statistic (p-Value) |

Constant | −283.4200 *** | −4.1406 (0.0002) |

$\mathrm{LNOPENNESS}$ | 62.3474 *** | 4.1962 (0.0002) |

$\mathrm{RESERVES}$ | −0.7727 *** | −5.0693 (0.0000) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 17.6084 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 25.7000 (0.0000) ** | |

Error-Correction Model | ||

Regressor | Short-Run Coefficient | t-Statistic (p-Value) |

Constant | −0.9393 | −0.7753 (0.4445) |

$\Delta \mathrm{LNOPENNESS}$ | 9.9306 | 0.5315 (0.5991) |

$\Delta \mathrm{RESERVES}$ | 0.5243 *** | 5.5751 (0.0000) |

${\Delta \mathrm{RESERVES}}_{t-1}$ | −0.6448 *** | −6.7326 (0.0000) |

$ec{m}_{t-1}$ | −0.4705 *** | −3.3708 (0.0021) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.2825 (0.5951) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 31.0817 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on ${\Delta \mathrm{RESERVES}}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 45.3285 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $ec{m}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 11.3623 (0.0007) ** | |

${R}^{2}$ | 0.8550 | |

Adjusted ${R}^{2}$ | 0.8350 | |

Standard error of the regression | 7.0378 | |

Observations | 34 | |

Additional Diagnostics | ||

BG LM test for serial correlation | F (1, 28) = 0.9245 | |

BPG test for heteroskedasticity | F (4, 29) = 0.5875 | |

Ramsey RESET | F (1, 28) = 0.1716 | |

Normality test | JB (1.3224) = 0.5162 | |

Stable | Yes |

Model: SBIC-ARDL (1, 0, 0, 1) | ||

Regressor | Long-Run Coefficient | t-Statistic (p-Value) |

Constant | −232.1514 ** | 2.5855 (0.0145) |

$\mathrm{OVER}$ | 8.0067 | 1.2929 (0.2053) |

$\mathrm{LNOPENNESS}$ | 49.3755 ** | 2.5362 (0.0163) |

$\mathrm{RESERVES}$ | 0.2439 | 1.1604 (0.2545) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{OVER}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 1.6715 (0.1961) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 6.4321 (0.0112) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 1.3465 (0.2549) | |

Error-Correction Model | ||

Regressor | Short-Run Coefficient | t-Statistic (p-Value) |

Constant | −0.5780 | −0.3316 (0.7425) |

$\Delta \mathrm{OVER}$ | 4.4302 | 1.3719 (0.1803) |

$\Delta \mathrm{LNOPENNESS}$ | 27.7653 | 0.9965 (0.3270) |

$\Delta \mathrm{RESERVES}$ | 0.7909 *** | 6.1610 (0.0000) |

$ec{m}_{t-1}$ | −0.6547 *** | −4.6701 (0.0001) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{OVER}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 1.8822 (0.1701) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.9931 (0.3190) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 37.9577 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $ec{m}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 21.8099 (0.0000) ** | |

${R}^{2}$ | 0.6804 | |

Adjusted ${R}^{2}$ | 0.6378 | |

Standard error of the regression | 10.2731 | |

Observations | 35 | |

Additional Diagnostics | ||

BG LM test for serial correlation | F (1, 31) = 0.0896 | |

BPG test for heteroskedasticity | F (3, 32) = 0.0534 | |

Ramsey RESET | F (1, 31) = 0.2794 | |

Normality test | JB (0.0224) = 0.9889 | |

Stable | Yes |

Model: SBIC-ARDL (1, 0, 0, 1) | ||

Regressor | Long-Run Coefficient | t-Statistic (p-Value) |

Constant | −291.5128 *** | −4.1595 (0.0002) |

$\mathrm{UNDER}$ | 3.8561 | 0.6723 (0.5064) |

$\mathrm{LNOPENNESS}$ | 64.0550 *** | 4.2140 (0.0002) |

$\mathrm{RESERVES}$ | −0.8190 *** | −4.8613 (0.0000) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{UNDER}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.4519 (0.5014) | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 17.7577 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $\mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 23.6320 (0.0000) ** | |

Error-Correction Model | ||

Regressor | Short-Run Coefficient | t-Statistic (p-Value) |

Constant | −0.9235 | −0.7760 (0.4443) |

$\Delta \mathrm{UNDER}$ | 0.3369 | 0.0950 (0.9250) |

$\Delta \mathrm{LNOPENNESS}$ | 12.3948 | 0.6706 (0.5079) |

$\Delta \mathrm{RESERVES}$ | 0.5214 *** | 5.6507 (0.0000) |

${\Delta \mathrm{RESERVES}}_{t-1}$ | −0.6333 *** | −6.6013 (0.0000) |

$ec{m}_{t-1}$ | −0.5149 *** | −3.7165 (0.0009) |

The Wald test for the hypothesis: | ${\chi}^{2}$ (p-value) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{UNDER}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.0090 (0.9243) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{LNOPENNESS}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 0.4498 (0.5024) | |

${\mathrm{H}}_{0}$: Coefficient on $\Delta \mathrm{RESERVES}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 31.9303 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on ${\Delta \mathrm{RESERVES}}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 43.5768 (0.0000) ** | |

${\mathrm{H}}_{0}$: Coefficient on $ec{m}_{t-1}=0$ | ${\chi}_{\left(1\right)}^{2}=$ 13.8124 (0.0003) ** | |

${R}^{2}$ | 0.8649 | |

Adjusted ${R}^{2}$ | 0.8408 | |

Standard error of the regression | 6.9135 | |

Observations | 34 | |

Additional Diagnostics | ||

BG LM test for serial correlation | F (1, 30) = 0.0770 | |

BPG test for heteroskedasticity | F (3, 31) = 0.7388 | |

Ramsey RESET | F (1, 30) = 0.4577 | |

Normality test | JB (3.2014) = 0.2018 | |

Stable | Yes |

Hypothesis | ${\mathit{\chi}}^{\mathbf{2}}$ | p-Value | Causality |
---|---|---|---|

$\mathrm{OVER}$ does not Granger-cause $\mathrm{KF}$ | 4.2343 | 0.0396 ** | $\mathrm{OVER}\to \mathrm{KF}$ |

$\mathrm{KF}$ does not Granger-cause $\mathrm{OVER}$ | 0.2481 | 0.6184 | No |

$\mathrm{RESERVES}$ does not Granger-cause $\mathrm{KF}$ | 28.5347 | 0.0000 *** | $\mathrm{RESERVES}\to \mathrm{KF}$ |

$\mathrm{KF}$ does not Granger-cause $\mathrm{RESERVES}$ | 0.3219 | 0.5705 | No |

$\mathrm{LNOPENNESS}$ does not Granger-cause $\mathrm{KF}$ | 0.5755 | 0.7500 | No |

$\mathrm{KF}$ does not Granger-cause $\mathrm{LNOPENNESS}$ | 6.4861 | 0.0390 ** | $\mathrm{KF}\to \mathrm{LNOPENNESS}$ |

$\mathrm{UNDER}$ does not Granger-cause $\mathrm{KF}$ | 1.4348 | 0.4880 | No |

$\mathrm{KF}$ does not Granger-cause $\mathrm{UNDER}$ | 0.3613 | 0.8347 | No |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bosupeng, M.; Dzator, J.; Nadolny, A. Exchange Rate Misalignment and Capital Flight from Botswana: A Cointegration Approach with Risk Thresholds. *J. Risk Financial Manag.* **2019**, *12*, 101.
https://doi.org/10.3390/jrfm12020101

**AMA Style**

Bosupeng M, Dzator J, Nadolny A. Exchange Rate Misalignment and Capital Flight from Botswana: A Cointegration Approach with Risk Thresholds. *Journal of Risk and Financial Management*. 2019; 12(2):101.
https://doi.org/10.3390/jrfm12020101

**Chicago/Turabian Style**

Bosupeng, Mpho, Janet Dzator, and Andrew Nadolny. 2019. "Exchange Rate Misalignment and Capital Flight from Botswana: A Cointegration Approach with Risk Thresholds" *Journal of Risk and Financial Management* 12, no. 2: 101.
https://doi.org/10.3390/jrfm12020101