Demanda de inconstitucionalidad contra el artículo 106 de

Since there are only nine Presidents for the Pakistani sample and 19 Prime Ministers for the Thai sample, such samples with few alternations between political regimes could potentially lead to a significant relationship between stock returns and political regimes when none actually exists. Moreover, the issue of stock market expectation may be another concern with this type of research18. That is, although the shifts from civilian to military regimes are naturally unexpected due to the nature of the coups and military takeovers, it is arguable that the shifts from military back to civilian regimes could be anticipated since general election dates are typically announced in advance.

Therefore to counteract such problems of small sample and market expectation, this study closely follows the approach taken by Santa-Clara and Valkanov (2003), which is the use of a randomisation-bootstrap procedure. This procedure is formally developed by Davison and Hinkley (1997) and Efron and Tibshirani (1998). It tests how likely such a difference in returns is observable across political regimes given that there is truly no association between regimes and returns. Such a procedure can be carried out regardless of the nature of stochastic disturbances or any time dependence of the data since these are eradicated through its reshuffling process (Kim, Nelson, and Startz 1991). Again, for this robustness check, the focus is on Pakistan and Thailand as these are the two markets with significant military returns premiums.

Following Santa-Clara and Valkanov (2003), the testing procedures are implemented in three steps. In the first step, a re-sampling experiment is carried out in order to find the small-

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145 sample distribution of the t-statistic, t. This is done by drawing: (1) a sample of 10,000 observations with replacement from the returns series; and (2) a sample of 10,000 observations of military and civilian dummy variables independently from the returns series. This procedure helps to ensure that military and civilian dummy variables are truly independent of returns.

In the second step, with 10,000 time series of ሼr_t+1, M_t , C_tሽ୘ _t=1, regression model [4.2] is re-estimated which yields a new estimate of Ƚෝ_ଵ௝, Ƚෝ_ଶ௝ and two new tj. This is repeated 10,000

times for j = 1, …, 10,000. Here, rt+1 denotes the real rate of returns at month t+1. Mt and Ct denote the political dummy variable, where M or C = 1 if a military or civilian government is in office at time t; M or C = 0, otherwise.

From step two, there are now series of α₁jand α₂j. The bootstrapped distributions of t are the distribution of the 10,000 draws of tj. The mean of the bootstrapped distributions of Ƚෝ₁ and

αො2are denoted by αത෠1 and αത෠2. Under the null hypothesis that returns during military and

civilian governments must be equal to each other and to the unconditional mean, this implies thatαത෠1 should equal to αത෠2 (αത෠1=αത෠2).

Therefore, in step three the two-sided bootstrapped p-value is computed by determining the percentage of situation where the bootstrapped tj is larger and smaller than the t calculated from the original sample. That is, Pboot=൫ #൛tj≥ tൟ + # ൛tj≤- tൟ )/10,000൯, where #൛tj≥ tൟ

denotes the number of bootstrapped tj’s that are higher than the computed t statistic; and

# ൛tj≤- tൟ denotes the number of bootstrapped tj’s that are lower than the computed - t

statistic. Table 4.20 presents the results from the bootstrap tests for the stock markets of Pakistan and Thailand.

146 Table 4.20

Robustness check: Randomisation-bootstrap test

The table presents the results from estimating randomisation-bootstrap test on Pakistan and Thailand. Mt and Ct

denote political dummy variables where M=1 or C=1 if a military or civilian government is in office at time t,

respectively,M=0 or C=0, otherwise. The sample period is from 1960 to 2007 and 1975 to 2007 for Pakistan and Thailand, respectively. The results are annualised and they are presented in percentage term. The numbers in square brackets are the mean of the corresponding parameter from the bootstrap samples. The last line represents the p-value from the bootstrap samples.

Country α 1 (Mt) α 2 (Ct) Difference (α 1 - α 2) = 0 Pakistan 6.974 [2.81] 0.06 -5.012 [2.81] 0.50 12.562 [0.00] 0.07 Thailand 35.962 [6.19] 0.04 2.155 [6.19] 0.49 33.161 [0.00] 0.08

The numbers in the first line represent the estimated value of the parameters. The numbers in square brackets are the mean of the parameter from the bootstrap samples. Noticeably, these numbers are identical across the two political regimes. This is because under the assumption that returns are independent of the political variables, the average returns under military and civilian governments should be equal to each other and to the unconditional mean. The numbers in the last line represent the bootstrapped p-value.

The null hypothesis of no military returns premium implies that Ƚෝ1 should equal to Ƚෝ2(Ƚෝ1 =

Ƚෝ₂). It is found that the difference between the two political regimes remains statistically significant for both Pakistan and Thailand, at the 10 percent level, using the randomisation- bootstrap method. The strength of the results from this robustness check thus supports earlier findings that the stock markets of Pakistan and Thailand perform better under military regimes.

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