Revista Tinerilor Economiºti (The Young Economists Journal)

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Business Statistics – Economic Informatics returns on investment of the S&P/ASX200 Index could affect weather conditions. Thus, the one way relationship between weather variables and return on investment of the S&P/ASX200 Index has been determined: ASX 200 t n = ∑ n iTE t −1 + ∑ β j ASX t − j + u1 t i= 1 j = 1 α (6) ASX 200 tS n = ∑ n iQTE t −1 + ∑ β j ASX t − j + u1 t i= 1 j = 1 α (7) ASX 200 t n = ∑ iWBTE n t −1 + ∑ β j ASX t − j + u1 t i= 1 j = 1 α (8) ASX 200 t n = ∑ n iQWBTE t −1 + ∑ β j ASX t − j + u1t i= 1 j = 1 α (9) ASX 200 t n = ∑ i HU n t −1 + ∑ β j ASX t − j + u 1 t i = 1 j = 1 α (10) ASX 200 t n = ∑ i PRE n t −1 + ∑ β j ASX t − j + u 1t i = 1 j = 1 α (11) ASX 200 t n = ∑ iVPRE n t −1 + ∑ β j ASX t − j + u 1t i = 1 j = 1 α (12) Where, n indicates lags and it is being assumed no relationship (white noise) and weather variables series error terms (which has not been given in the between t u1 equation) u2 t (Granger 1969). Granger Causality Test is based on F statistics which were proven by Wald (Işığıçok, 1994): ( ESS r − ESSur ) / n Fn; m− 2n = (13) ESSur /( m − 2n) Where, ESS indicates the sum of error terms squares, ur indicates the model which is unrestricted, while r indicates the model which is restricted. If the computed F value exceeds the critical F value at the chosen level of significance (α) with (n;m-2n) degrees of freedom, the null hypothesis (H0) that there is no causality between the two series should be rejected. So, coefficients (or coefficient) in the model are statistically significant. 4. EMPIRICAL ANALYSIS 4.1 Descriptive Statistics and Pre-estimation Analysis The descriptive statistics of the weather variables and S&P/ASX200 Index returns are presented in Table 1. The descriptive statistics for weather-related variables indicates that the average temperature in Australia is around 21oC, maximum is around 39oC and minimum is 8.5oC. The minimum of 2.2mb (millibars) and maximum of 62.0mb indicate high changes in atmospheric pressure, but moderate fluctuation in humidity. The minimum of -6.78 per cent and maximum of 5.89 per cent show that daily 167
Revista Tinerilor Economişti (The Young Economists Journal) losses on return on investment are slightly higher than gains, on a daily basis. Also, a positive mean and median indicate a positive return on average. The high positive kurtosis indicates the possibility of a severe market reaction to events. Temperature Quality Temperature Table 1. Descriptive Statistics Wet Bulb Temperature (WBT) Quality WBT Jarque-Bera (J-B) statistics have been used to test the null hypothesis that the data are from normal distribution. All J-B statistics are greater than the critical value 5.99. Thus, the null hypothesis of normality is rejected. The return’s kurtosis of 7.30 exceeds the value 3 of normal distribution, but skewness is less than + 1.00 due to the large sample size. 4.2.Augment Dickey-Fuller Test (ADF) The ADF test has been used to test if the data are stationary and co-integrated. Augment Dickey-Fuller Test Statistics results are presented in Table 2. Table 2. Augmented Dickey-Fuller Test Statistics for all variables Temperature Exogenous 1% level 5% level 10% level t-Statistic Prob.* Constant -3.431947 -2.862131 -2.567128 -5.840922 0.0000 Constant, Linear Trend -3.960542 -3.411031 -3.127332 -5.854211 0.0000 None -2.565593 -1.940910 -1.616641 -0.726992 0.4019 Quality Temperature Exogenous 1% level 5% level 10% level t-Statistic Prob.* Constant -3.431946 -2.862130 -2.567128 -6.948791 0.0000 Constant, Linear Trend -3.960541 -3.411030 -3.127331 -6.979036 0.0000 None -2.565593 -1.940910 -1.616642 -2.364890 0.0175 Wet bulb Temperature Exogenous 1% level 5% level 10% level t-Statistic Prob.* Constant -3.431946 -2.862131 -2.567128 -5.295731 0.0000 Constant, Linear Trend -3.960542 -3.411031 -3.127332 -5.294090 0.0000 None -2.565593 -1.940910 -1.616642 -0.984942 0.2912 168 Humidity Pressure Vapour Pressure ASX200 Return Mean 21.21 11.31 15.90 55.76 1010.77 14.24 25.96 0.0033 Median 21.00 12.00 15.90 56.00 1011.00 14.00 24.90 0.0002 Maximum 38.70 24.00 26.10 99.00 1031.80 62.00 68.80 0.0589 Minimum 8.50 -13.40 6.00 9.00 986.10 2.200 11.10 -0.0678 Std. Dev. 4.23 5.71 3.71 16.49 7.34 5.01 7.04 0.0077 Skewness 0.34 -0.51 0.03 -0.00 -0.25 0.40 1.13 -0.3369 Kurtosis 3.13 2.84 2.23 2.99 2.97 4.50 5.61 7.3037 Jarque-Bera 74.30 166.40 91.53 0.001 39.69 451.93 1836.17 2909.73 Probability 0.00 0.00 0.00 0.99 0.00 0.00 0.00 0.00 Sum 78074.3 41656.4 58536.8 205218 3719641. 52412.8 95557.7 1.2127 Sum Sq. Dev. 65896.2 120015.6 50765.9 1000850 198658.3 92355.3 182559.8 0.2209
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