POOLED REGRESSION ANALYSIS

Pooled regression analysis is used to test Hypothesis 3 and 4 where the study assesses the impact of political risk on the aggregated stock market returns. To test Hypothesis 3, the study aggregates returns across markets to form three portfolios. These three portfolios contain samples of emerging markets, developed markets, and a combined markets grouping. The study then forms two further portfolios in order to test Hypothesis 4. These two portfolios include the sample of Pacific Basin emerging markets and all other emerging markets.

The study begins by testing a primary regression model, derived from the International Market Model (IMM), which firstly removes the global influence from the return series. According to Aggarwal, Inclan, and Leal (1999), this procedure needs to be carried out since most volatility shocks in emerging markets are due to local market events. Furthermore, Bilson et al., 2002 assert that political risk is more likely to be a local influence and therefore have a greater impact on the local component of stock market returns. Thus, following Bilson

et al., 2002, the first regression model specification is as follows:

Rt = a + b Rwt + rt where [3.1]

Rt is the pooled monthly return across markets at time t; Rwt is the monthly return on world index; and

rt is the pooled residual local return at time t

Once the global influence is removed from the stock return series, the residual term (rt) can be seen at this point as the pooled ‘local stock returns’. This is consistent with the theory

71 underlying the IMM which assumes that the residual term (rt) should contain only country- specific influences.

In order to rule out other possible influences on local returns, additional control variables such as exchange rate, dividend yield, and local return volatility are subsequently included into the regression model [3.1]. The choice of these three additional control variables is justified, as follows: According to Adler and Dumas (1983) and Dumas and Solnik (1995), exchange rate risk should be explicitly included in international asset pricing models. They assert that, within the setting of imperfect purchasing power parity (PPP), exchange rate risk could be an important factor in pricing local stock returns. This is due to the different level of pricing when stocks are measured in the same currency and are found to be different from the PPP theoretical values. Such differences pose the real risks to investors and, hence, exchange rate risk should be priced accordingly. Bailey and Chung (1995) also assert that exchange rate risk could be a significant price factors in emerging markets. Accordingly, Harvey (1995) finds that substantial exposures to exchange rate risk on equity returns are primarily negative, particularly for emerging markets. Partly, this is because the depreciation of domestic currency reduces the competitiveness of the country and therefore causes a negative effect on the domestic stock market (Ma and Kao, 1990).

For dividend yield, it is well established by studies such as Fama and French (1988), Kothari and Shanken (1992), and Hodrick (1992) that there is an association between equity returns and dividend yield for developed markets. As for emerging markets, Bekaert and Harvey (1995, 2000) document that dividend yield is one of the main causes of local equity market variation and that it can proxy for capital market liberalization. Harvey (1995) also asserts that the addition of local dividend yield helps to significantly improve the explanatory power of various models conduct on emerging markets. Moreover, Bilson et al. (2002) report that dividend yields are negatively related to equity returns. This is because dividend yield does not only forecast the future changes in dividend yield itself when expected returns vary through times but it also forecast the future returns. Hence, the contemporaneous dividend yield is positively related to future returns. However, the shock to expected returns is negatively related to current stock prices. Such evidence points to the need to include dividend yield as one of the control variables.

72 With regards to local return volatility, Bekaert and Harvey (1995) state that, in completely segmented markets, the expected local stock market returns would be a function of its covariances with local risk factors. However, since this is a cross-country study, it is indeed difficult to identify and capture all the local risk factors as these can vary greatly across markets. In such a case where the study examines stock market-wide returns, Bilson et al. (2002) suggest that the covariance between the individual assets and the local risk factors should be aggregated and the resulting variance terms could be proxied by the local return variance. As a result, stock market returns in the following regression model [3.2] are related to local volatility remaining after the risks from global influence, exchange rate, market size, and dividend yields are removed. Thus, the local return variance represents a residual control variable, which in this sense it proxies for omitted factors.

One further control variable in the model is related to the stock market development and the economic integration. Bekaert and Harvey (2000), for example, also control for this factor in their study which examines the impact of foreign speculators in emerging equity markets. The proxy that represents the stock market development is the ratio of stock market capitalization divided by nominal gross domestic product (GDP). This proxy for stock market development, or what other researchers refer to as the stock market size, is used extensively in cross-country studies such as those by Demirguc-Kunt and Levine (1995), Levine and Zervos (1996), Levine (2002), and Fush-Schüdeln and Funke (2003). One of the main reasons for including this control variable is attributable to the fact that the level of stock market development can differ considerably even within the emerging markets portfolio.

Furthermore, it should be noted that a lagged change in political risk variable is used herein this study. Erb et al. (1996) also employ such a lagged change in their study. However, their univariate regression analysis does not take into account other possible influences that might affect stock returns. It is asserted by Bilson et al. (2002) that the use of contemporaneous changes for exchange rate, dividend yield, and local volatility follows the assumption that these variables are available on a timely basis and have an immediate influence over the returns series. However, they argue that there are very limited empirical studies that support

73 such an assumption for a political risk variable. More importantly, they point out that political risk rating is futuristic in the sense that it is based on analysts’ forecast on the political condition of the next period. This therefore leads to a lagged relationship between political risk rating and stock returns. For that reason, it is believed that the lagged change in political risk rating should be used accordingly.

The pooled regression model to be tested is therefore as follows:

rt = α + β FXt + θ DYt+ γσrt2+ δ SZt+ λ PRt-1 + et where [3.2]

rt is the pooled residual returns from the world regression [3.1] at time t FXt is the pooled monthly percentage change in the exchange rate at time t DYt is the pooled monthly percentage change in the dividend yield at time t

σrt2 is the pooled variance of monthly local market returns at time t7

SZt is the pooled monthly ratio of market capitalization divided by nominal GDP at time t PRt-1 is the pooled monthly changes in political risk rating at time t-1

et is the residual of the pooled local stock returns at time t

One of the concerns with regression model [3.2] is the possible presence of multicollinearity. This is where the explanatory variables are highly correlated to each other. The aggregated average correlations across all markets for each local variable pair are presented in bracket as follow: political risk/dividend yield (0.005), political risk/local risk (0.009), political risk/size (0.161), exchange rate/dividend yield (0.008), exchange rate/political risk (0.004), exchange rate/local risk (0.012), dividend yield/local risk (0.014), size/exchange rate (0.166), size/dividend yield (0.175), and size/local risk (0.144). Given that all average correlations are close to zero, this indicates that multicollinearity should not be a concern for this model.

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Schwert (1989) asserts that the expected value of the absolute value of residual returns can be less than the standard deviation from a normal distribution. In such a case, the absolute residual returns have to be

multiplied by the constant √ (π/2). Thus, following the suggestions of Schwert (1989, 1990) and Bilson et al.

(2002) the variance of monthly returns is calculated by | rt| * √ (π/2), which is the absolute value of rt

multiplies by the square root of (π/2). Schwert (1989) then suggests that the obtained value (returns) from this

74 However, it is debatable that the differences in equity markets across the world do not necessarily result from only the political risk factor. There are a number of other country characteristics that can have a greater impact on the stock market as opposed to how political risk is in that country. These are, for example, economic and legal factors. Nonetheless, it is also very difficult to control for all these factors in a cross-sectional test. One of the ways to mitigate this problem is to correct for country fixed-effects in the pooled regression analysis. Bhattacharya, Daouk, and Welker (2003) suggest that by allowing each market to have a different intercept term in the regression analysis, this procedure allows for the likelihood that the stock returns are affected by country-specific factors that are not captured by the independent variables.

Furthermore, it is possible that political risk and stock markets returns are affected by a third variable which causes a spurious relationship between them. This is known as the endogeneity problem. It is suggested by Himmelberg, Hubbard, and Palia (1999) and Bhattacharya et al. (2003) that pooled regression analysis with fixed country effects can effectively help to eliminate such a problem causes by endogeneity. Hence, the method of correcting for country fixed-effects in the pooled regression analysis is applied in all regressions throughout this study.

Moreover, given that the Jarque-Bera test rejects the hypothesis of normality in stock returns for most of the sample markets and that the returns series also feature positive skewness, the issue of persistence in the returns series can be of a concern. Persistence occurs when the returns series are positively autocorrelated. Therefore, to address this issue, the Newey-West (1987) correction is applied in all regressions throughout the study. This procedure corrects for both autocorrelation as well as heteroskedasticity of an unknown form. Studies such as Bilson et al. (2002) assert that the use of this procedure yields similar effect and provides highly similar results to that of Cochrane-Orcutt method, which takes the first-difference on both sides of the regression equation in order to eliminate any nonstationary that causes persistence in returns series. Accordingly, the Newey-West (1987) method is applied in all regressions of this study to address this concern.

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