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Objective 2

Chapter 3 Research Methodology

3.2 Objective 2

To establish the short-run and long-run relationship among net capital flow volatility, financial deepening and capital market performance

This study examines the short- and long-run relationships among net capital flow volatility, financial deepening and capital market performance in low-income SADC countries. The period of study spans from the first quarter of 2000 (2000:Q1) to the fourth quarter of 2015 (2015:Q4).

Preliminary tests carried on the data indicated that the variables are stationary after first differencing and are also co-integrated. Although capital markets mainly comprise of equity and debt markets, this study focuses solely on equity markets due to the unavailability of data and lack of organized debt markets in the selected SADC countries.

3.2.1 DATA DESCRIPTION AND DATA SOURCES

The major variables of interest to fulfill this objective are net FPI volatility, net FR volatility, capital market performance (measured by the change in the main index of each stock market) and

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financial deepening measured by the widely accepted M2/GDP (Rahman and Mustafa, 2015, Sackey and Nkrumah, 2012, Giuliano and Ruiz-Arranz, 2009). Data was obtained from the World Bank data bank and the IMF’s International Financial Statistics. In line with the existing literature, the study uses stock market index data for each country as an indicator or proxy for capital market performance (Kyereboah-Coleman and Agyire-Tettey, 2008, Liu and Sinclair, 2008, Egly et al., 2010) or log natural of ( 𝑆𝑑/π‘†π‘‘βˆ’1). It uses quarterly data spanning the period from 2000:Q1 to 2015:Q4.

This investigation is modelled around four critical stages (Mahadevan and Asafu-Adjaye, 2007).

Firstly, a panel unit root test is conducted to find out whether or not the variables under study are stationary. Secondly, cointegration analysis is applied to check for the existence of long relationships among the variables in the panel data. The third procedure is to establish the existence of the long-run equilibrium relationship among the variables. Finally, the causal relationships among these variables are established.

3.2.2 PANEL UNIT ROOTS

Panel unit root tests are conducted to establish if the dependent and predictor variables in the equation are stationary in order to avoid spurious regressions. Based on recent econometric studies, unit root tests centered on panel data are more robust than tests based on individual time series data (Hsiao, 2014, Baltagi, 2008, Gurajati, 2004). According to Baltagi (2008), this is a result of information in cross section data that enhances the information contained in time series (Rahman and Mustafa, 2015). In addition, panel unit root tests lead to statistics with normal distribution in the limit. There are a number of ways of conducting panel data unit root tests. These include Levin, Lin and Chu (2002) – LLC; Augmented Dickey Fuller (ADF) and Im, Pesaran and Shin (2003) – IPS. All the tests are applied so that the results can be differentiated and checked for accuracy as well as to maintain consistence. In all three cases, the null hypothesis for the benchmark model is that the instruments have unit root (i.e., are non-stationary). In this investigation, panel unit root tests show that the variables in the model are stationery after first differencing and that they are also cointegrated.

3.2.3 PANEL CO-INTEGRATION TESTS

After determining the stationarity (no unit root) of the panel data, there is need to check for the existence of long-run relationships among capital flow volatility, capital market performance

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(π‘π‘šπ‘) and financial deepening (𝑓𝑑) in the selected low-income SADC economies. The study employs the Pedron (2004) and Kao Co-integration test to explain the long-run relationship between these variables as the Johansen and Juselius (1992) procedure is not feasible on dynamic panel data. The analysis uses the two tests to ensure robustness and to compare the results. Because these variables exhibit long-run relationships, it is crucial to establish the equilibrium relationship.

The ordinary least squares (OLS) estimator is a biased and inconsistent estimator of co-integrated panel data and hence, cannot be used. However, Pedron (2004) suggests a dynamic ordinary least squares (DOLS) method which provides more flexibility in the presence of heterogeneity.

Following Mahadevan and Asafu-Adjaye (2007), the empirical model for conducting co- integration tests is based on the following equations that were estimated individually using the vector error correction method.

π‘πΉπ‘ƒπΌπœŽπ‘π‘‘= 𝛼𝑖+ 𝛿𝑑+ 𝛽𝑓𝑑𝑐𝑑 + πœ†π‘π‘π‘šπ‘π‘‘+ 𝑒𝑐𝑑………3.3 π‘πΉπ‘…πœŽπ‘π‘‘= 𝛼𝑖+ 𝛿𝑑+ 𝛽𝑓𝑑𝑐𝑑 + πœ†π‘π‘π‘šπ‘π‘‘+ πœ‡π‘π‘‘β€¦β€¦β€¦β€¦..………..3.4 Where;

𝛼𝑖 and 𝛿𝑑 are country and time fixed effects, respectively.

𝑓𝑑𝑐𝑑 is financial deepening in country c at time t.

π‘πΉπ‘ƒπΌπœŽπ‘π‘‘ is net foreign portfolio investment volatility in country c at time t.

π‘πΉπ‘…πœŽπ‘π‘‘ is net foreign remittance volatility in country c at time t.

π‘π‘π‘šπ‘π‘‘ is capital market performance in country c at time t πœ‡π‘π‘‘ is the error term for country c at time t.

Pedron’s (1999, 2004) method accommodates heterogeneity across individual units of the panel.

It considers seven different test statistics, four of which are dependent on pooling the residuals of the regression along the within-dimension of the panel while the other three are based on pooling the residuals of the regression along the between-dimension of the panel. The analysis is based on the null hypothesis that there is no cointegration against an alternative hypothesis of cointegration.

111 3.2.4 ESTIMATION TECHNIQUE

In line with Rahman and Mustafa (2015), the study adopts the dynamic panel vector error correction model (P-VECM) to determine the direction of causality among net capital flow volatility, financial deepening and stock markets. VECM has the advantage of clearly separating long- and short-run relationships among model variables. Its ability to indicate the direction of causal relationships between variables is the most sought after in this analysis. The choice of VECM is also based on the fact that Eviews automatically converts data into first difference. The modeling of this analysis was guided by similar studies such as SentΓΌrk and Sataf (2015), Rahman and Mustafa (2015), and Lee and Chang (2008).

Therefore, suppose the inter-relationships among the variables in this study assume a dynamic trivariate P-VECM form as depicted in the following simultaneous equations:

βˆ†π‘“π‘π‘–πœŽπ‘–π‘‘ = πœ‹π‘–π‘‘+ ʎ1π‘–π‘’π‘–π‘‘βˆ’1+ βˆ‘π‘˜π‘—=1ʎ2π‘–π‘—βˆ†π‘“π‘π‘–πœŽπ‘–π‘‘βˆ’π‘—+ βˆ‘π‘˜π‘—=1ʎ3𝑖𝑗 βˆ†π‘“π‘‘π‘–π‘‘βˆ’π‘—+

βˆ‘π‘˜π‘—=1ʎ4π‘–π‘—βˆ†π‘π‘šπ‘π‘–π‘‘βˆ’π‘—+ πœ€π‘–π‘‘...3.5a

βˆ†π‘“π‘‘π‘–π‘‘ = πœ‹π‘–π‘‘+ ʎ1π‘–π‘’π‘–π‘‘βˆ’1+ βˆ‘π‘˜π‘—=1ʎ2π‘–π‘—βˆ†π‘“π‘‘π‘–π‘‘βˆ’π‘—+ βˆ‘π‘˜π‘—=1ʎ3𝑖𝑗 βˆ†π‘“π‘π‘–πœŽπ‘–π‘‘βˆ’π‘—+

βˆ‘π‘˜π‘—=1ʎ4π‘–π‘—βˆ†π‘π‘šπ‘π‘–π‘‘βˆ’π‘—+ πœ€π‘–π‘‘...3.5b

βˆ†π‘π‘šπ‘π‘–π‘‘ = πœ‹π‘–π‘‘+ ʎ1π‘–π‘’π‘–π‘‘βˆ’1+ βˆ‘π‘˜π‘—=1ʎ2π‘–π‘—βˆ†π‘π‘šπ‘π‘–π‘‘βˆ’π‘—+ βˆ‘π‘˜π‘—=1ʎ3𝑖𝑗 βˆ†π‘“π‘π‘–πœŽπ‘–π‘‘βˆ’π‘—+

βˆ‘π‘˜π‘—=1ʎ4π‘–π‘—βˆ†π‘“π‘‘π‘–π‘‘βˆ’π‘—+ πœ€π‘–π‘‘...3.5c Where;

βˆ† denotes a change dynamic operator;

π‘“π‘π‘–πœŽ, 𝑓𝑑 π‘Žπ‘›π‘‘ π‘π‘šπ‘ represent foreign portfolio investment (𝑓𝑝𝑖) volatility, financial deepening (𝑓𝑑) and capital market performance (π‘π‘šπ‘), respectively;

t represents time period;

πœ‹π‘–π‘‘ is a deterministic constant component of the model;

ʎ1𝑖…….ʎ4 are coefficients;

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𝑗 is the optimal lag length determined by AIC, SIC and HQC; we use the number of lags that minimize the criteria;

π‘’π‘–π‘‘βˆ’1 is the error correction term and it represents how far the variables are from the equilibrium relationship. The error-correction mechanism estimates how, in the event of an imbalance variables adjust towards parity so as to preserve the long-run relationship. If the set of estimated coefficients (ʎ2𝑖 to ʎ4𝑖) on lagged independent variables are non-zero, then there is short-run causality. If the ECM coefficient ʎ1𝑖 is negative and significant then there is long-run causality.

The same procedure is conducted involving net remittance volatility (π‘“π‘ŸπœŽ) in place of net foreign portfolio investment volatility (π‘“π‘π‘–πœŽ).

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