^{1}

^{*}

^{1}

The purpose of this paper is to examine the relationship between financial development—bank and stock market—and economic growth in Zimbabwe. Using data during the period from 2005 to 2013, the study employed a VECM for the short run Controls variables. This offers a possibility of applying VAR in order to use integrated multivariate time series and avoid spurious regression as the interest rates appear to have long run positive impact on economic growth. This means that banking sector performs better than the stock markets if the interest rate is positively related to economic growth. The findings suggest a positive relationship between efficient stock market and economic growth both in short run and long run. Interest rates have a negative effect, while market capitalisation has a positive effect on growth. It is concluded that financial sector is important in the process of sustainable economic development in Zimbabwe.

This study seeks to assess the cointegration and causal relationship between financial system development and economic growth, from a Zimbabwean perspective, for the period 2005-2013. Traditional theorists believed that financial market in general has no correlation with economic growth. This proposition aroused studies on finding the effect of financial market on growth. Ample studies have debunked the traditionalists and established association between financial market and economic growth.

The development in the financial system is identified as factor which played a critical role in industrializing most European countries [

While a developed financial system is regarded as a pre-requisite for economic growth, there has been a general argument as to the level and comparative contribution to economic growth by the various segments of the financial system, namely banking sector and stock markets. [

Some studies find no relationship between financial system development and economic growth. They argue that the role of financial systems, whether banking or stock markets, is over emphasised in both theory and empirical findings. Policies that are pro-growth are enough in stimulating economic growth.

[

Through general inspection, the Stock markets appear more efficient than the banking sector since there is no directed channelling of resources compared to directed lending that is common in the banking sector [

The comparison between stock markets and banking sector contribution to economic growth has attracted a lot of attention largely as a result of policy implications embedded in the comparisons’ outcome. Stock markets are linked to contractual savings hence associated with long term savings and hence long term capital injection into the various markets of an economy leading to economic growth [

While there have been a number of studies analysing economic growth and financial sector development, most of these focused on the causality between economic growth and financial development in general, for example [

In light of the various economic reforms done in Zimbabwe since the early 1990s which have yielded no significant results, it is imperative to investigate the role played by banking sector and stock markets so as to come with appropriate policy prescriptions.

The problems surrounding the financial system in Zimbabwe have been signalled since 1993 when UMB (United Merchant Bank) collapsed and the government failing to save it despite the fact that most of its exposures were linked to the government. From thereon, the public did not lose confidence in only the financial system but the government as well whose policies were being “doctored” to suite or exclude certain individuals or companies. Inflation started accelerating in 2003. This and the collapse of Asset Management Firms in early 2004 led to a total loss of confidence in the financial services sector finally leading to the instability of the whole sector. Confidence in the financial sector is still very low, although it improved slightly in 2009-2010 when the economy was under the Unity Government.

Despite its existence for over 140 years, both under the colonial government and the 35 years of independence, the financial sector has remained fragile. Regulation of the sector has remained fairly comparable with other countries in the region in terms of regulatory institutions albeit under inconsistent policies.

Historically, the colonial era maintained a very “lean” banking sector structure with only four banks in operation until 1990. The first Economic Structural Adjustment Programme of 1993, which left the economy in a worse off situation, forced the government to liberalise the financial system in 1997 as it sought a quick recovery through the banking sector [

While Zimbabwe generally led financial sector development within the SADC region both before and after independence, the post-Independence development was not in line with economic growth. Economic decline in GDP terms between 1997 and 2003 saw annual worsening from, from −2.6% to −6.1%. This was surprisingly accompanied by increased banking lending, from 63% total banking assets to 78% in 2003, a realisation of consumptive lending increases within that period [

While financial liberalisation was introduced to induce a bank-led economic growth, most of the funds went to consumptive borrowing. The increase in the number of financial has still failed under the multi-currency regime in absence of policies to arrest consumptive borrowing. Real GDP declined by more than 50% between 2000 and 2008 while hyperinflation reached triple figures in 2007. There was however some recovery in GDP particularly in 2009 under reforms, of the Inclusive Government (IG) which helped to restore macroeconomic stability. In response to the Short-Term Emergency Recovery Program (STERP) a 5.7% growth in GDP was realised in 2009. In 2010, GDP grew by about 8% 2010. This was a strong performance compared with a decline of about 14% in 2008 pointing towards the fact of lack of policy coherence prior to the IG. Total bank deposits grew from USD276 million in January 2009 to USD1.35 billion and USD2.34 billion by December 2009 and December 2010 respectively, again indicating a rather improved confidence in the policies of the government in place at that time [

The Zimbabwe Stock Exchange was closed during the hyper inflationary period in November 2008, and resumed trading in February 2009 after the dollarization of the economy, and for the first time, the shares were denominated in US dollars and trading was done in US dollars. From 2009 Zimbabwe’s economy has been recovering from a significant hyperinflationary period. The introduction of the US Dollar in February 2009 brought relative currency stability to the economy which in turn resulted in increased investment and investor confidence.

The ZSE has two indices namely the Industrial Index and Mining Index. The industrial index is a stock index derived from the values of the industrial stocks on ZSE. It consists of all companies except mining companies. It is the main index on ZSE and is composed of 63 companies. The top 9 companies on the industrial index by market capitalisation are Delta Corporation Limited, Econet Wireless Zimbabwe Limited, Innscor Africa Limited, British American Tobacco Zimbabwe Limited, OK Zimbabwe Limited, Hippo Valley Estates Limited, Seed Co Limited, National Foods Holdings Limited, and Old Mutual Plc (www.zimbabwe-stock-exchange.com). It consists of companies from various sectors including Agriculture, engineering, banking and finance, insurance, property, retail, beverages, food and Pharmaceuticals and Chemicals.

The stock market provides a low cost way of companies to raise capital to finance their business. The capital is raised by equities, depository receipts and debentures. This leads to growth of the industry and commerce of the country, thus economic growth. The stock market also provides an opportunity for investors to invest their surplus funds and have capital gain. Thus, the overall development of the economy is a function of how well the stock market performs and empirical evidence has proved that development of the capital market is essential for economic growth [

The stock market is expected to lead to economic growth by directing funds from the public investors to efficient companies, increasing the liquidity of financial assets, disseminating information to promote better investment decisions, make company managers to work harder for shareholders interests as the value of wealth of the shareholders depend upon the share price. It also leads to economic growth by providing a platform where foreign investors can come and invest in the local economy. The stock market act as a mediator between borrowers and savers by mobilising funds from many small investors and channelling them to efficient companies.

Zimbabwe economy experienced a decade of contraction from 1998 to 2008, and extreme hyperinflation from 2004 to 2008. The economy started recovering from 2009 after the formation of an inclusive government and the introduction of multi-currency system with the US$ being the predominant one. Dollarization reversed inflation, permitting the banking system to stabilize and the economy to resume slow growth after 2009. In 2012, inflation averaged about 5.0%. However dollarisation also had negative impacts including high real interest rates due to lack of capital. Zimbabwe’s economy recorded real growth of more than 9% per year in 2010-11, before slowing to 5% in 2012, partly due to a poor harvest and low diamond revenues .However the economy continues to experience structural challenges emanating from the limited sources and high cost of capital; uncertainties arising from policy inconsistencies, especially with respect to economic empowerment and indigenisation regulations; dilapidated infrastructure and obsolete technologies [

Time series is considered as stationary if a series is mean-reverting, that is, the series repeatedly returns back to its mean and does not have a tendency to drift. Therefore, if the mean and variance of the series are constant overtime, while the value of the covariance between the two periods depends only on the gap between the periods and not on the actual time at which the covariance is considered, then the series is stationary. But, if one or more of the above mentioned conditions are not fulfilled, then the series is non-stationary [

One of the most important data characteristic that must be determined before applying econometric methods is the order of integration. If the applied data does not have the correct order of integration, spurious regressions or wrong test statistics are the consequences and can make the analysis useless. For cointegration analysis to be valid all series must be integrated of the same order usually of order one [

There have been a variety of proposed methods for implementing stationarity tests (for example, [

Augmented Dickey-Fuller (ADF) test is an extension of Dickey-Fuller test. The ADF test entails regressing the first difference of a variable y on its lagged level, exogenous variable(s) and k lagged first differences. The following equation of ADF test, which include both a drift and linear time trend, checks the stationarity of time series data:

Δ Y t = α + β T + ρ Y t − 1 + ∑ i = 1 k γ i Δ Y t − 1 + ε t (1)

where Y t is the variable in period t, T denotes a time trend, α , β , ρ are constants, Δ is the first difference operator, ε t is an error term disturbance with mean zero and variance δ 2 , and k represents the number of lags of the differences in the ADF equation. This augmented specification is then used to test the hypothesis below. The null hypothesis ( H 0 ) is that the variable is not stationery while the alternative hypothesis ( H 1 ) is that the variable is stationery:

H 0 : ρ = 0 vs H 1 : ρ < 0 (2)

which is evaluated using the conventional t ratio for ρ :

t ρ = ρ ^ ( s e ( ρ ^ ) ) (3)

where ρ ^ is the estimate of ρ and ( s e ( ρ ^ ) ) is the coefficient standard error. The ADF is restricted by its number of lags. It decreases the power of the test to reject the null of a unit root, because the increased number of lags necessitates the estimation of additional parameters and a loss of degree of freedom. The number of lags is being determined by minimum number of residuals free from auto correlation. In this study, the number of lags will be determined by the Schwarz information criterion (SIC). The test for a unit root is conducted on the coefficient of Y t − 1 in the regression. If the coefficient is significantly different from zero (less than zero) then the hypothesis that y contains a unit root is rejected. Rejection of the null hypothesis denotes stationarity in the series.

[

Δ Y t = α + β T + ρ Y t − 1 + ε t (4)

where the variables and parameters are the same as defined in the ADF test. The hypothesis is the same as that in ADF test, but it’s evaluated using the t statistic below:

t ¯ ρ = t ρ ( ψ 0 f 0 ) 1 / 2 − T ( f 0 − ψ 0 ) ( s e ( ρ ^ ) ) 2 f 0 1 / 2 s (5)

where ρ ^ is the estimate of ρ and t ρ the t-ratio of ρ , ( s e ( ρ ^ ) ) is the coefficient standard error, s is the standard error of the test regression, ψ 0 is a consistent estimator of the error variance and f 0 is an estimator of the residual spectrum at frequency zero.

Various econometrics time series data like exports and GDP, consumption and income share theoretical long run relationships. It’s also known that these time series data evolve over time such that their mean and variance are not constant [

The two main cointegration techniques used in literature are the [

The Johansen method of cointegration can be written as the following vector autoregressive (VAR) framework of order p.

X t = A 0 + ∑ j = 1 p B j X t − j + ε t (6)

where X t is an n × I vector of non stationery I(1) variables, A 0 is an n × 1 vector of constants, p is the maximum lag length, B j is an n × n matrix of coefficients, and ε t is an n × 1 vector of white noise terms. To use Johansen method, the equation above has to be turned into a vector error correction model (VECM) which can be written as

Δ X t = A 0 + ∑ j = 1 p − 1 Γ j Δ X t − j + Π X t − p + ε t (7)

where Δ is the first difference operator, Γ j = − ∑ i = j + 1 p B j I, Π = − I + ∑ i = j + 1 p B j and

I is an n × n identity matrix.

To test for cointegration between the X s , the rank of the Π matrix is observed via its eigenvalues. The rank of the matrix is equal to its characteristic roots that are different from zero. The hypothesis is H 0 : Π = α β ′ where α and β are n × n loading matrices of eigenvectors. The matrix β gives the cointegrating vectors, while α is known as the adjustment parameters that gives the amount of each cointegration entering each equation of the VECM. The aim is to test the number of r cointegrating vector such as β 1 , β 2 , ⋯ , β r . The Johansen approach has two likelihood ratio statistics to examine the rank of matrix Π . These are the trace and maximum eigenvalues tests which are given by the following formulas:

λ trace ( r ) = − T ∑ i = r + 1 n ln ( 1 − λ ^ i ) (8)

λ max ( r , r + 1 ) = − T ln ( 1 − λ ^ r + 1 ) (9)

where T is the sample size, λ ^ i the eigenvalues from the π matrix or the characteristic roots from the π matrix. For the trace test, the null hypothesis is that the number of cointegrating vectors is less than or equal to r while the alternative hypothesis is that they are more than r. For the maximum eigenvalue test the null hypothesis is that the number of cointegrating vectors is less than or equal to r against the alternative of r + 1. For both tests if the test statistic is more than the critical value, the null hypothesis is rejected. Testing is conducted as a sequence under the null, r = 0 , 1, ⋯ until the null is no longer rejected. When r = 0 failing to reject H 0 will complete the test, otherwise the test continues until the null is no longer rejected.

There are so many advantages for employing Vector Error Correction Model (VECM) for the short run Controls variables such as market capitalisation and interest rate which are included in our study. Among them is that the VECM offers a possibility of applying Vector Autoregressive Model (VAR) in order to use integrated multivariate time series and therefore avoid spurious regression.

The causality test is a statistical hypothesis test used to determine whether one time series is significant in forecasting another. This test aims at determining whether past values of a variable help to predict changes in another variable. The most widely used test is the Granger causality test. But according to [

The short run causal relationships between the variables should be examined in a VECM frame work. With X and Y integrated of order 1, the Vector Error Correctional Model (VECM) can be represented as

Δ X t = δ i + ∑ i = 1 p a i Δ X t − 1 + ∑ i = 1 p β i Δ Y t − i + γ 1 ε ^ 1 t − 1 + v 1 t (10)

And

Δ Y t = λ i + ∑ i = 1 p d i Δ X t − 1 + ∑ i = 1 p c i Δ Y t − i + γ 2 ε ^ 2 t − 1 + v 2 t (11)

where ε ^ 1 t − 1 and ε ^ 2 t − 1 are the error correction terms obtained from the long run model lagged once, which can be interpreted as the deviation of X and Y and from their long run equilibrium values, respectively. Including the error correction terms represents the short-run dynamics necessary to reach the long run equilibrium and opens a channel to detect Granger causality [

VAR’s impulse response function examines how the dependent variables react to shocks from each independent variable. A shock to the i t h variable not only directly affects that variable but is also transmitted to all of the other endogenous variables through the dynamic structure of the VAR. An impulse response function traces the effect of a one-time shock to one of the innovations on current and future values of the endogenous variables. Thus the impulse response function is a useful tool for determining the magnitude, direction, and the length of time that the variables in the system are affected by a shock to another variable. To estimate the impulse response functions, the VAR model needs to be transformed into a Vector Moving Average (VMA) representation. [

[ Y t z t ] = [ Y ¯ Z ¯ ] + ∑ i = 0 ∞ A i 1 − b 12 b 21 [ 1 − b 12 − b 21 1 ] [ ε Y t − 1 ε Z t − 1 ] (12)

[ Y t z t ] = [ Y ¯ Z ¯ ] + ∑ i = 0 ∞ [ θ 11 i θ 12 i θ 21 i θ 22 i ] [ ε Y t − i ε Z t − i ] (13)

and

X t = μ + ∑ i = 0 ∞ θ i ε t − 1 (14)

where θ i is the impulse response functions of the disturbances. The impulse response function is found by reading off the coefficients in the moving average representation of the process. For each variable, a unit shock is applied to the error term and its effects upon the system are noted. If the innovations ε t − 1 are contemporaneously uncorrelated, the interpretation of the impulse response is straightforward. For example, the i t h innovation of ε t is simply a shock to the i t h endogenous variable in the system. Innovations, however, are usually correlated, and may be viewed as having a common component which cannot be associated with a specific variable.

Forecast Error Variance decompositions trace out the proportion of movements in the dependent variables that are due to their own shocks versus shocks to the other variables. They indicate the relative importance of each structural shock to the variables in the system. It separates the variation in an endogenous variable into the component shocks to the VAR. Thus, the variance decomposition provides information about the relative importance of each random innovation in affecting the endogenous variables in the VAR. Thus, variance decompositions can be considered to be similar to R 2 values associated with the dependent variables in different horizons of shocks. Below is an equation as shown by Enders (2004) [

X t + n − E t X t + n = μ + ∑ i = 0 n − 1 θ i ε t + n − i (15)

Considering Y t , the first element of the X t + n matrix in equation 12 to 14 the variance of the n-step-ahead forecast error can be calculated as :

Y t + n − E t Y t + n = θ 11 ( 0 ) ε Y t + n + θ 11 ( 1 ) ε Y t + n − 1 + ⋯ + θ 11 ( n − 1 ) ε Y t + 1 + θ 12 ( 0 ) ε Z t + n + θ 12 ( 1 ) ε Z t + n − 1 + ⋯ + θ 12 ( n − 1 ) ε Y t + 1 (16)

or

σ y ( n ) 2 = σ y 2 [ θ 11 ( 0 ) 2 + ⋯ + θ 11 ( n − 1 ) 2 ] + θ z 2 [ θ 12 ( 0 ) 2 + θ 12 ( 1 ) 2 + ⋯ + θ 12 ( n − 1 ) 2 ] (17)

where σ y ( n ) 2 and σ z ( n ) 2 represent the n step ahead forecast error variance of Y t + n and Z t + n respectively. The first part of equation 16 ad 17 shows the proportion of variance due to the variables own shock, Y t , while the second part of equation 16 shows the proportion of variance due to the other variables shock, z t . It is typical for a variable to explain almost all of its forecast error variance at a short horizon and smaller proportions at longer horizons (Enders, 2010) [

There are certain assumptions for the VAR and VECM models which include absence of heteroskedasticity and autocorrelation in the residuals and normality of the residuals. These are tested for by the methods outlined below.

[

y t = b 1 + b 2 x t + b 3 z t + e t (18)

where b ′ i s are the estimated parameters and e the residual. The test statistic is then based on the auxiliary regression:

e t 2 = α 0 + α 1 x t + α 2 z t + α 3 x t 2 + α 4 z t 2 + α 5 x t z t + v t (19)

The null hypothesis of the LM test is that there is no serial correlation up to lag order p, where p is a pre-specified integer. The local alternative is ARMA (r, q) errors, where the number of lag terms p = max(r, q). This alternative includes both AR (p) and MA (p) error processes, so that the test may have power against a variety of alternative autocorrelation structures. The test statistic is computed by an auxiliary regression as follows. If you have estimated the regression:

y t = X t β + ε t (20)

where β ′ s are the estimated coefficients and ε are the errors. The test statistic for lag order is based on the auxiliary regression for the residuals e = y − X β ^ :

e t = X t γ + ( ∑ s = 1 p α s e t − s ) + v t (21)

The test statistic is the Breusch-Godfrey LM test statistic. This LM statistic is computed as the number of observations, times the (uncentered) R 2 from the test regression. Under quite general conditions, the LM test statistic is asymptotically distributed as a χ 2 ( p ) .

It reports the multivariate extensions of the Jarque-Bera residual normality test, which compares the third and fourth moments of the residuals to those from the normal distribution. The multivariate test uses a factorization of the k residuals that are orthogonal to each other. Let P be a k × k factorization matrix such that:

v t = P u t → N ( 0 , I k ) (22)

where u t is the demeaned residuals. Define the third and fourth moment vec-

tors m 3 = ∑ t v t 3 T and m 4 = ∑ t v t 4 T . Then:

T [ m 3 m 4 − 3 ] → N ( 0 , [ 6 I k 0 0 24 I k ] ) (24)

under the null hypothesis of normal distribution. Since each component is independent of each other, we can form a χ 2 statistic by summing squares of any of these third and fourth moments.

The graphs above show the behaviour of the interest rate, market capitalization and GDP for the period January 2005 to December 2013. From the plots the trends of the variables over the period can be observed.

Jarque-Bera is used for testing whether the series is normally distributed. The test statistic measures the difference of the skewness and kurtosis of the series with those from the normal distribution. The p-values which are associated with the Jarque-Bera statistics for market capitalisation and interest rates are significant. This shows that those two series are not normal. Skewness is a measure of asymmetry of the distribution of the series around its mean. Positive significant skewness for the market capitalization and interest rates shows that those variables have a long right tail. The p-values which are associated with the Jarque- Bera statistics for GDP is not significant. This shows that GDP exhibit normality.

The star (*) sign in the

Considering the p-values in

GDP | I_RATE | M_CAP | |
---|---|---|---|

Mean | 7.896528 | 3.105639 | 1.852056 |

Median | 6.110000 | 1.170000 | 0.790000 |

Maximum | 12.80000 | 35.61600 | 6.000000 |

Minimum | 4.420000 | 0.117000 | 0.152000 |

Std. Dev. | 2.882252 | 7.362028 | 1.937602 |

Skewness | 0.474748 | 3.490363 | 1.023896 |

Coeff. Variation | 0.365002 | 2.370535 | 1.046189 |

Kurtosis | 1.659222 | 14.36995 | 2.543962 |

Jarque-Bera | 4.048845 | 267.0095 | 6.602131 |

Probability | 0.132070 | 0.000000* | 0.036844* |

Sum | 284.2750 | 111.8030 | 66.67400 |

Sum Sq. Dev. | 290.7581 | 1896.981 | 131.4006 |

Observations | 36 | 36 | 36 |

Variable | Level t-statistic | P-value | First Difference t-statistic | P-value |
---|---|---|---|---|

GDP | −2.371463 | 0.03867* | −2.249553 | 0.4482 |

I_Rate | −2.995059 | 0.1478 | −5.968584* | 0.0001 |

M_Cap | −0.893537 | 0.9455 | −5.184580* | 0.0009 |

Variable | Level t-statistic | P-value | First Difference t-statistic | P-value |
---|---|---|---|---|

GDP | −1.775767 | 0.6949 | −5.457227* | 0.0005 |

I_Rate | −3.649280 | 0.1340 | −7.501036* | 0.0000 |

M_Cap | −0.873202 | 0.9480 | −5.135924* | 0.0011 |

variables become stationery after the first differencing. This implies that all the variables are integrated of order 1, i.e. they are I (1).

In this study, the optimum number of lags in our VAR was determined by the Schwarz information criterion. It suggested an optimum of one lag for the VAR model. Using this length of one lag produced no autocorrelation between the residuals of the VAR (1) model for up to 12 months as shown in

The other results revealed that the estimated residuals of the VAR (1) model behave like white noise. This supports the appropriateness of the VAR (1) model in determining the long term relationship between GDP and market capitalisation and stock market development.

The residuals of the VAR are tested for normality using the Cholesky (Lutkepohl) orthogonalization. The Jarque-Bera test statistic is used for determining whether the residuals are normally distributed. The test statistic measures the difference of the skewness and kurtosis of the series with those from the normal distribution. The null hypothesis is that the residuals are multivariate normal. If the p-value is less than 0.05, then the null hypothesis is rejected and the conclusion is that the residuals are not normal. The results are shown in

The VAR residuals should not have heteroskedasticity, i.e. their variance

Lags | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

LM-Stat | 46.52 | 36.33 | 34.71 | 37.29 | 23.40 | 30.69 | 29.95 | 36.48 | 45.94 | 30.42 | 33.45 | 28.81 |

Prob | 0.11 | 0.45 | 0.53 | 0.41 | 0.95 | 0.72 | 0.75 | 0.45 | 0.12 | 0.73 | 0.59 | 0.80 |

Jarque-Bera | df | Prob. |
---|---|---|

14.08 | 12 | 0.2956 |

should be constant. The study uses the White Heteroskedasticity Test. The null hypothesis is that there is no heteroskedasticity, while there alternative is that there is heteroskedasticity. If the p-value is less than 0.05, then the null hypothesis is rejected and the conclusion will be that heteroskedasticity is present from

Since our variables are all I(1), Johansen cointegration test can be applied to the VAR(1) to find the long run relationship between the variables. The test is for identifying the number of cointegrating vectors and the corresponding cointegrating equations. The test has been carried out assuming a linear trend with an intercept in the cointegration equation. This is because the economic data which is being used in this study is assumed to have trends. The lag length used is 1 as determined by the Schwarz information criterion. The Johansen approach has two likelihood ratio statistics which are the trace and maximum eigenvalues tests. These tests are alternate tests. The null hypothesis is that the number of cointegrating vectors is equal to r while the alternative hypothesis is that they are greater than r. The tests are conducted at the 5% significance level. If the p-value is less than 0.05, then the null hypothesis is rejected and the iteration proceeds to test the next hypothesis that number of cointegrating vectors is equal to r + 1.

The star (*) sign in the tables above denotes significance at 5% probability level. The p-values are from [

As the results show one cointegrating vector, the study normalises the

Chi-sq | df | Prob |
---|---|---|

543.21 | 567 | 0.2740 |

Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob. | |
---|---|---|---|---|

None* | 0.745192 | 124.4907 | 95.75366 | 0.0001 |

At most 1 | 0.474678 | 58.86285 | 69.81889 | 0.2720 |

At most 2 | 0.272200 | 27.96318 | 47.85613 | 0.8153 |

At most 3 | 0.183175 | 12.71221 | 29.79707 | 0.9043 |

At most 4 | 0.059332 | 3.000340 | 15.49471 | 0.9669 |

At most 5 | 0.001341 | 0.064411 | 3.841466 | 0.7996 |

Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob. | |
---|---|---|---|---|

None* | 0.745192 | 65.62782 | 40.07757 | 0.0000 |

At most 1 | 0.474678 | 30.89967 | 33.87687 | 0.1089 |

At most 2 | 0.272200 | 15.25097 | 27.58434 | 0.7281 |

At most 3 | 0.183175 | 9.711872 | 21.13162 | 0.7712 |

At most 4 | 0.059332 | 2.935929 | 14.26460 | 0.9509 |

At most 5 | 0.001341 | 0.064411 | 3.841466 | 0.7996 |

cointegrating vector on Zimbabwe Industrial Index (ZII). This produces the below equation:

GDP = 5.538624 − 0.054678 i_Rate + 1.364816 M_Cap (1)

All the t-statistics in

a) There is a significant negative long run relationship between GDP and interest rate. This result is in line with economic theory. This is understandable since the study include data which was captured during the hyperinflationary period when the excessive inflation was driving the prices of everything upwards including that of stock prices. On the other hand, during the period of this study, 2009-2013, inflation was under control and it was single digit inflation. This negative relationship is consistent with the results of [

b) There is a positive significant long run relationship between GDP and market

Dependent Variable: GDP | ||||
---|---|---|---|---|

Method: Least Squares | ||||

Date: 04/13/15 Time: 18:42 | ||||

Sample: 2005Q1 2013Q4 | ||||

Included observations: 36 | ||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. |

C | 5.538624 | 0.217210 | 25.49895 | 0.0000 |

I_RATE | −0.054678 | 0.019910 | −2.746338 | 0.0097 |

M_CAP | 1.364816 | 0.075647 | 18.04183 | 0.0000 |

R-squared | 0.918978 | Mean dependent var | 7.896528 | |

Adjusted R-squared | 0.914067 | S.D. dependent var | 2.882252 | |

S.E. of regression | 0.844911 | Akaike info criterion | 2.580484 | |

Sum squared resid | 23.55786 | Schwarz criterion | 2.712444 | |

Log likelihood | −43.44872 | Hannan−Quinn criter. | 2.626542 | |

F-statistic | 187.1479 | Durbin-Watson stat | 0.532203 | |

Prob(F-statistic) | 0.000000 |

capitalisation. This was expected. This is because GDP is being used as a proxy for real output. In times of high economic growth, companies will be able to increase production and sales hence higher turnover. Economies of scale may lead to higher profitability and also increased profits due to higher turnover. Hence higher expected cash flows and dividends, thus higher stock prices and ultimately a higher market capitalisation. This result is consistent with studies of [

[

In

Null Hypothesis | Wald Test: Chi-sq | df | Prob. |
---|---|---|---|

GDP does not ® I_Rate | 28.84 | 4 | 0.5494 |

I_Rate does not ® GDP | 3.05 | 4 | 0.0000* |

I_Rate does not ® M_Cap | 8.04 | 4 | 0.0901 |

M_Cap does not ® I_Rate | 8.06 | 4 | 0.0895 |

GDP does not ® M_Cap | 9.86 | 4 | 0.0428* |

M_Cap does not ® GDP | 10.51 | 4 | 0.0326* |

results show that there is unidirectional causality from interest rate to GDP, interest rate does not cause market capitalization and market capitalization does not cause interest rate. This means that interest rate can predict GDP in the short run, but the reverse is not true. There is a bidirectional effect on GDP and market capitalization.

To ascertain the results of the VECM causality tests, diagnostic tests are performed on the VECM. For statistical accuracy and efficiency, certain conditions should be fulfilled. There should not be serial correlation between the residuals. The serial correlation tests between residuals were done with the Autocorrelation Lagrange multiplier (LM) test. The test was performed up to lag 12. The null hypothesis is that there is no autocorrelation. If the p-value is less than 0.05, then the null hypothesis is rejected and there will be autocorrelation between the residuals. The probabilities of the LM test are from chi-square with 36 degrees of freedom. The results are shown in

The residuals of the VECM should be multivariate normal. The residuals of the VECM are tested for normality using the Cholesky (Lutkepohl) orthogonalization. The Jarque-Bera test statistic is used for determining whether the residuals are normally distributed. The test statistic measures the difference of the skewness and kurtosis of the series with those from the normal distribution. The null hypothesis is that the residuals are multivariate normal.If the p-value is less than 0.05, then the null hypothesis is rejected and the conclusion will be the residuals are not normal. The results are shown in

The VECM should be stable for the results to be valid. The inverse roots of the characteristic autoregressive polynomial are tested. The estimated VECM is stable if all roots have modulus not greater than one and do not lie outside the unit circle. Since no root lies outside the unit circle, it implies the VECM is stable, thus the VECM is appropriate.

There VECM residuals should not have Heteroskedasticity. That means their variance should be constant. The study uses the White Heteroskedasticity Test. The null hypothesis is that “there is no heteroskedasticity”. If the p-value is less than 0.05, then the null hypothesis is rejected and the conclusion will be that heteroskedasticity is present.

Impulse response functions are used to determine how the GDP respond to shocks in other economic variables. They track the response of the GDP over a period of time after the shock. They are carried out in the VAR (1) system, which was shown to be an appropriate equation (25). The response they show include the magnitude of the effect on GDP, the direction of the effect i.e. whether it’s positive or negative, and the length of time that the GDP is affected by that shock, while holding all the other factors constant. The impulse response functions analyses are carried out with a cholesky ordering of index, interest rate and market capitalization. The impulse responses in this study are used to track the response for up to 12 months. The results are shown in

The blue line in

Interest rates shocks cause the GDP to decrease for 5 months, then the effect settles to a permanent level. This contradicts the VECM causality results which

Lags | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

LM-Stat | 27.67 | 46.78 | 37.59 | 42.81 | 40.39 | 37.44 | 29.65 | 37.52 | 39.94 | 28.49 | 38.45 | 31.04 |

Prob | 0.839 | 0.108 | 0.40 | 0.216 | 0.282 | 0.403 | 0.763 | 0.398 | 0.299 | 0.809 | 0.359 | 0.703 |

Jarque-Bera | df | Prob. |
---|---|---|

4.35 | 12 | 0.9762 |

Chi-sq | df | Prob. |
---|---|---|

822.52 | 798 | 0.2662 |

showed that interest rates do not cause GDP in the short run. This however is in line with the results of the Johansen cointegration which showed a negative long run relationship between GDP and interest rates.

Forecast Error Variance decompositions trace out the proportion of movements in the dependent variables that are due to their own shocks versus shocks to the other variables [

From the results in

Among the explanatory variables, after 12 months, GDP accounts for the greatest variation, followed by market capitalization. As explained before, the effect of market capitalisation on GDP is likely to be through its effect of causing investment. Real activity as proxied by interest rate, accounts for a very low variation in the GDP. Overally, the interest rate and market capitalisation explain for 40% of the variations in the GDP, which is quite a significant percent

Period | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

GDP | 100 | 89.17 | 74.53 | 69.59 | 70.70 | 63.99 | 62.65 | 61.70 | 61.11 | 60.49 | 60.05 | 59.71 |

I_Rate | 0.00 | 4.24 | 15.78 | 24.30 | 20.97 | 26.78 | 17.48 | 17.49 | 17.55 | 17.70 | 17.79 | 17.77 |

M_Cap | 0.00 | 6.59 | 9.68 | 6.45 | 8.06 | 9.10 | 19.34 | 19.55 | 19.79 | 19.95 | 20.05 | 20.10 |

age, supporting the Johansen cointegration results which showed a long run relationship between the Industrial index and macroeconomic variables.

The purpose of this study was to investigate the short run and long run relationship relationships between GDP, stock market development and banking sector development using quarterly data from January 2005 to December 2013. The study also sought to determine which one of the two has significant impact on GDP. Statistical and econometrics techniques were used to examine the short run and long run relationships. These techniques include the Johansen cointegration test, VECM causality tests, impulse response functions and variance decompositions.

Results of the long run analysis obtained from the Johansen cointegration test showed that the GDP and banking sector development (interest rate) and stock market development (market capitalisation) are cointegrated, implying that they share a long run relationship. The resulting cointegration equation showed the nature of the long run relationship. There is a significant negative long run relationship between GDP and stock market development and stock market development.

Further, the study shows that there is a significant negative long run relationship between the Zimbabwe stock prices and money supply. This is a surprising result, as it is expected that higher money supply will reduce the liquidity constraints companies are facing thus increase their profitability thus higher stock prices. This may be explained by noting that most companies, which are listed on the ZSE, are large companies, which makes it easier for them to raise capital, and also they have access to foreign borrowing, thus the liquidity crisis may not adversely affect them. The negative relationship is explained by noting that increase in money supply leads to inflation, which has been shown to be negatively associated with stock prices.

Furthermore, the study depicted that there is a negative significant long run relationship between interest rates and market capitalisation. This result was expected since the interest rates used were lending rates. Thus high interest rates lead to high cost of borrowing and hence a reduction in economic activity. This also affects corporate profit as higher cost of capital reduces the profits, reduces future cash flow of business and dividends. This causes a reduction of the stock prices. Higher interest rates also directly lead to the increase in the discount rate, thus a reduction in the present value of future dividends hence lower stock prices.

The results of the VECM short run analysis showed that past values of interest rate, and market capitalization can be used to predict the short run GDP. The impulse response functions showed that shocks to the interest rate and market capitalisation have a significant permanent effect on GDP. The variance decompositions showed that a significant percentage of variation in the GDP is explained by the stock market and banking sector variables.

Government Policy ImplicationsThough dollarization brought inflation down to single digits, policy makers should continue putting more importance to the keeping of interest rate under control. This is because it is currently the most important factor which adversely affects GDP. Policy makers should also encourage stock market expansion as it is a cheap source of money supply for development and investment.

Monetary policy should be designed in a way that keeps lending rates low. This is because high lending rates have a significant negative impact on the profitability of companies as they increase the cost of capital. This has an adverse effect on the stock prices. Policy makers should also design policies that increase the industrial production thus real output in the economy, as this leads to higher stock prices in the long run. When designing policies to stabilize the stock market, policy makers should take into consideration the performance of the banking sector as it has been shown to have a significant impact on the stock prices in both the short and long run.

Maposa, L. and Muma, F.M. (2017) The Impact of Financial Development on Economic Growth in Zimbabwe: Comparative Analysis of Stock Markets and Commercial Banks. Open Access Library Journal, 4: e3808. https://doi.org/10.4236/oalib.1103808