An Analysisnof oil price volatilitynand its effect on the Kuwaiti stock exchnage index

  1. 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

StatisticsBBKSE
Mean3.8003578.391069
Median3.8961338.689314
Maximum4.8882429.645765
Minimum2.2844217.200276
Std. Dev.0.7091300.747907
Skewness-0.188072-0.289298
Kurtosis1.7726821.650523
Jarque-Bera16.477268221.55862
Probability0.0002640.000021
Observation240240

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
 ADFPPADF PP
      
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:

+ +

Whereas:

 

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

 BBKSE
F-statistics293.8003874.945
Obs*R-squared221.1248225.2248
Prob.F(1,237)0.0000.0000
Prob.Chi-square (1)0.00000.0000
   
   

 

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

 

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