An Analysisnof oil price volatilitynand its effect on the Kuwaiti stock exchange

Data and Empirical results:

The study objective is to investigate the link between oil prices and stock index in Kuwait. The study do not use daily data, in order to avoid time-difference problems with international markets. The empirical investigation is based on 240 monthly observations which are sampled from November 1995 to October 2015 due to unavailability . Data on oil prices are obtained from the Energy Information Administration (EIA).This is used as our primary proxy for the Kuwait price of crude oil, whereas Kuwaiti oil is traded internationally. The stock return obtained from Kuwait Stock Exchange Market Index (KSE).


  • Descriptive statistics.

Table 1-Bera statistics of the Brent Blend oil spot price and the KSE index.


Table 1. Descriptive Statistics

Statistics BB KSE
Mean 3.800357 8.391069
Median 3.896133 8.689314
Maximum 4.888242 9.645765
Minimum 2.284421 7.200276
Std. Dev. 0.709130 0.747907
Skewness -0.188072 -0.289298
Kurtosis 1.772682 1.650523
Jarque-Bera 16.4772682 21.55862
Probability 0.000264 0.000021
Observation 240 240

The construction and definition of each variable will be discussed in Appendix.


1.2. The unit root test results

Figure 1 depict the historical time-paths of the log prices of crude oil and KSE index. Their evolution are broadly indicative of the long-term dependencies that may exist between them. Accordingly, the study examined each individual series using the ADF unit root test (Dickey and Fuller, 1981) based on the Schwarz information criteria. And Phillip-Perron (PP). Table (2) lists the result of the application of the ADF and PP test of the BB oil spot price, and the KSE index. The standard econometric practice for the analysis of financial time series data is to start with an examination of the unit roots. The ADF and PP test is used to test for all prices under the null hypothesis of the unit root against the hypothesis of stationarity. The study test for unit root in the level and the test indicates that all level series are non-stationary table (2). The ADF and PP test for first difference series shows that the test yields negative values in all case levels with the result that the individual price series reject the null hypothesis at the significance level. The values are stationary for each BB spot price and KSE index.


Figure 1

Oil price and stock market index (in logarithm)


Table 2. Unit root test result

  Level   First difference
BB -1.480145 -1.584799 -13.26724   -13.26724
KSE -1.568397 -1.1528895 -9.758477   -9.962297

Note: (1) statistically significant at the 1, 5 and 10%, respectively.


1.3 The estimate result of the ARCH Model.

Lagrange multiplier (LM) test is used to test for the presence of ARCH effects. First to test for first order ARCH, regress the squared regression residual on their lags:

+ +



Since the LM statistics for the two series is significant see table (3). The study reject the null hypothesis that there is no first order ARCH effect.


Table (3) the result of ARCH effect.

Heteroscedasticity Test: Arch

F-statistics 293.800 3874.945
Obs*R-squared 221.1248 225.2248
Prob.F(1,237) 0.000 0.0000
Prob.Chi-square (1) 0.0000 0.0000


In the next step the study will implement asymmetric GARCH, GARCH, GARCH-in-Mean, and threshold GARCH.



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