Chapter 5 Capital Volatility, Financial Deepening and Capital Market Performance: …
5.5 Granger Causality Estimation
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power of the regression model that contains different numbers of predictors. In this study, low adjustment R square implies that the variables have weak explanatory power and cannot be relied upon. The F-statistic value of 131.5974 is greater than the table value at F0.05 (5%), showing that the model is fitted and statistically significant; hence, it is adequate and reliable for establishing the short-run and long-run relationships among capital flow volatility, financial deepening and capital market performance in low-income SADC economies.
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D(CPI) 58.82963 2 0.0000
D(FD) 3.066986 2 0.2158
D(INT) 1.639606 2 0.4405
D(NFR) 848.1291 2 0.0000
All 873.8358 10 0.0000
Dependent variable: D(CPI)
Excluded Chi-sq Df Prob.
D(CMP) 11.40047 2 0.0033
D(RGDP) 0.583398 2 0.7470
D(FD) 34.96996 2 0.0000
D(INT) 0.009734 2 0.9951
D(NFR) 32.35562 2 0.0000
All 95.66215 10 0.0000
Dependent variable: D(FD)
Excluded Chi-sq Df Prob.
D(CMP) 12.13974 2 0.0023
D(RGDP) 0.098922 2 0.9517
D(CPI) 21.52882 2 0.0000
D(INT) 0.022897 2 0.9886
D(NFR) 26.10128 2 0.0000
All 48.73614 10 0.0000
Dependent variable: D(INT)
Excluded Chi-sq Df Prob.
D(CMP) 0.651598 2 0.7220
D(RGDP) 0.101803 2 0.9504
D(CPI) 1.262323 2 0.5320
D(FD) 0.722883 2 0.6967
D(NFR) 0.784974 2 0.6754
All 2.069756 10 0.9958
Dependent variable: D(NFR)
Excluded Chi-sq Df Prob.
D(CMP) 85.77827 2 0.0000
D(RGDP) 79.36714 2 0.0000
D(CPI) 19.35011 2 0.0001
D(FD) 17.63937 2 0.0001
D(INT) 2.792856 2 0.2475
All 171.3397 10 0.0000
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Source: Author’s computation from output result from the regression analysis
Note that “***” represents rejection of Ho at 1%, “**” represents rejection of Ho at 5% and “*”
represents rejection of Ho at 10%. Note also: CPM (Capital Market Performance), RGDP (Real Gross Domestic Product), FD (Financial Deepening), CPI (Consumer Price Index), INT (Interest Rates), NFR (Net Foreign Remittance) & NFPI (Net Foreign Portfolio Investment).
In the same vein, Table 5.9 below shows the pairwise Granger causality between these variables.
The pairwise Granger causality test was conducted following the empirical study of Duruechi, Ojiegbe, and Chigbu (2014) who established the causal relationship between variables using F- statistics and their respective probability values.
Table 5.9: Pairwise Granger Causality Test
Null Hypothesis: Obs F-Statistic Prob. Decision Type of Causality DRGDP does not Granger Cause DCMP 549 5.95145 0.0028 Reject DRGDP↔DCMP DCMP does not Granger Cause DRGDP 4.42192 0.0124 Reject DCMP↔DRGDP DCPI does not Granger Cause DCMP 549 137.800 4.E-49 Accept No causality DCMP does not Granger Cause DCPI 0.64593 0.5246 Accept No causality DFD does not Granger Cause DCMP 549 124.099 4.E-45 Accept No causality DCMP does not Granger Cause DFD 1.66738 0.1897 Accept No causality DINT does not Granger Cause DCMP 549 0.58989 0.5547 Accept No causality DCMP does not Granger Cause DINT 0.74999 0.4729 Accept No causality DNFR does not Granger Cause DCMP 549 35.5987 3.E-15 Accept No causality DCMP does not Granger Cause DNFR 27.2979 5.E-12 Accept No causality DCPI does not Granger Cause DRGDP 549 1.2E-08 1.0000 Accept No causality DRGDP does not Granger Cause DCPI 5.4E-08 1.0000 Accept No causality DFD does not Granger Cause DRGDP 549 0.64366 0.5258 Accept No causality DRGDP does not Granger Cause DFD 2.41608 0.0902 Reject DRGDP→DFD DINT does not Granger Cause DRGDP 549 2.16032 0.1163 Accept No causality DRGDP does not Granger Cause DINT 0.74267 0.4763 Accept No causality DNFR does not Granger Cause DRGDP 549 112.646 1.E-41 Accept No causality DRGDP does not Granger Cause DNFR 6.84776 0.0012 Reject DRGDP→DNFR DFD does not Granger Cause DCPI 549 24.9036 4.E-11 Accept No causality DCPI does not Granger Cause DFD 34.7407 6.E-15 Accept No causality DINT does not Granger Cause DCPI 549 1.30393 0.2723 Accept No causality DCPI does not Granger Cause DINT 0.20548 0.8143 Accept No causality DNFR does not Granger Cause DCPI 549 1.38189 0.2520 Accept No causality DCPI does not Granger Cause DNFR 6.21029 0.0022 Reject CPI→DNFR
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DINT does not Granger Cause DFD 549 4.73166 0.0092 Reject DINT→DFD DFD does not Granger Cause DINT 1.61193 0.2005 Accept No causality DNFR does not Granger Cause DFD 549 3.29761 0.0377 Reject DFD↔DNFR DFD does not Granger Cause DNFR 4.77082 0.0088 Reject DNFR↔DFD DNFR does not Granger Cause DINT 549 0.17719 0.8377 Accept No causality DINT does not Granger Cause DNFR 0.12935 0.8787 Accept No causality Source: Author’s computation from output result from the regression analysis
Note that “***” represents rejection of Ho at 1%, “**” represents rejection of Ho at 5% and “*”
represents rejection of Ho at 10%. Note also: CPM (Capital Market Performance), RGDP (Real Gross Domestic Product), FD (Financial Deepening), CPI (Consumer Price Index), INT (Interest Rates), NFR (Net Foreign Remittance) & NFPI (Net Foreign Portfolio Investment). denotes unidirectional causality and denotes bi-directional causality.
From the pairwise test, real GDP Granger causes capital market performance at 5% level of significance; this conforms to the findings generated from the VECM Block Exogeneity Wald Test to reaffirm that in SADC countries, as GDP grows, capital market performance also grows. There is also a bidirectional relationship between capital market performance and real GDP, implying that when the capital market (the financial system that raises capital by dealing in shares, bonds, and other long-term investments) adheres to the required capital conservation, it will generate higher returns in the economy and if it operates with sufficient returns, it will be liquid to finance all the activities and requirements of the regulatory bodies of which capital markets and financial agents are key. Similar to the VEC Wald test, this study also finds causality running between real GDP and financial deepening as well as between real GDP and capital flow volatility. The findings also indicate that prices Granger cause capital flow volatility in low-income SADC economies. A rise in the CPI will lead to inflow of capital from foreign countries. This is in line with Aitken and Harrison’s (1999) finding that firms benefit from FDI as prices increase. In addition, there is causality running between interest rates and financial deepening. Interest rates are found to Granger cause financial deepening in the economy. A contractionary monetary policy (increase in interest rates) will negatively affect financial deepening, while expansionary monetary policy will strengthen the level of financial deepening in SADC countries. Finally, the study reveals a bidirectional relationship between financial deepening and foreign remittances flow volatility, which implies that when growth opportunities are not properly explored, in the long run, an increase in the provision of financial services (financial deepening), may lead to capital flow volatility due to anticipated instability in the economy.
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5.5.1 DIAGNOSTIC TESTS ON THE PVECM MODEL
Given that a model with 2-lags has been selected as the best PVECM model, further investigations are performed to check the model for autocorrelation, heteroskedasticity and normality in order to establish the appropriateness and robustness of the equation. The standard null hypotheses that are tested for serial correlation, heteroskedasticity and normality tests are:
▪ 𝐻0: 𝛼 = 1, there is no serial correlation, no heteroskedasticity and the residuals are normally distributed.
▪ 𝐻1: 𝛼 ≠ 1, there is serial correlation, heteroskedasticity and non-normality of residuals.
Table 5.10: Serial Correlation LM Test
Null Hypothesis: no serial correlation at lag order h
Lags LM-Stat Prob
1 60.64638 0.1062
2 46.55161 0.1120
3 112.0585 0.0900
4 342.1826 0.3001
5 77.71302 0.1001
6 16.16266 0.9982
Source: Author’s computation from output result from the regression analysis
“***” “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively.
Table 5.11: Heteroscedasticity Test
Heteroscedasticity Test: joint test Null Hypothesis: no Heteroscedasticity
Chi-sq Df Prob.
321.20 1218 0.3051
Source: Author’s computation from output result from the regression analysis
“***” “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively
157 Table 5.12: The PVECM Normality test
Com Skewness Kurtosis Jarque-Bera
Skew Chi-sq Df Prob Kurtosis Chi-sq Df Prob Jarque df Prob 1 0.993426 88.82049 1 0.0000 6.070090 212.0727 1 0.0000 300.8932 2 0 2 0.018115 0.029533 1 0.8636 8.052522 574.3794 1 0.0000 574.4090 2 0 3 -0.49319 21.89130 1 0.0000 9.563286 969.2264 1 0.0000 991.1177 2 0 4 0.123629 1.375572 1 0.2409 5.254361 114.3482 1 0.0000 115.7238 2 0 5 0.503333 22.80099 1 0.0000 8.846783 769.1595 1 0.0000 791.9605 2 0 6 -0.15992 2.301577 1 0.1292 8.339865 641.5686 1 0.0000 643.8702 2 0 Joint 137.2195 6 0.0000 3280.755 6 0 3417.974 12 0
Source: Author’s computation from output result from the regression analysis
“***” “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively
Table 5.10 indicates that there is no autocorrelation (similarity between observations) in the equation. Table 5.11 presents the heteroscedasticity test results for the model. The probability value confirms that the equation is heteroscedasticity (a system whereby the variability of a variable is uneven across the range of values that are estimated) free.
Finally, the normality test is conducted on the basis of the three known tests, which are skewness, kurtosis and Jarque-Bera. The results show that 90% of the variables in the model passed the normality test, both individually and jointly (see Table 5.12). The test results further show that the residuals are normally distributed and that the data sets are well modelled. This is shown by the probability values at 5% level of significance. The inference is that the data distribution and the residuals of the model for the SADC countries are normally distributed. Overall, the null hypothesis of no serial correlation, no heteroscedasticity and normality of the residuals cannot be rejected. These results show that the PVECM is consistent and favorable in establishing the short- and long-run relationships among capital flow volatility, financial deepening and capital market performance in low-income SADC economies.