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Get your digital subscription/issue of News behind the News-July 01, 52 issues starting from NbN 06 January EXPERT ANALYSES: SERIOUS WATER CRISIS IN THE MAKING: BEYOND APPOINTS SURENDRA AHUJA AS BOEING DEFENCE INDIA LEADER November 11, THE WEEK.
Table of contents
- Resolved complaints | About the ABC
- Resolved complaints
- 1 Introduction
- Previous Events
- Upcoming Events
Methodology section describes the methodology used for estimating stock returns and volatility. Findings section discusses the empirical findings. Finally, Conclusions section presents the conclusion. For instance, Kim and Mei [] investigated the impact of political risk on the Hong Kong equity market. The study adopted an event study methodology to examine the direct impact of political changes in security returns. The study also constructed Components—Jump model in order to quantify the market volatility reaction to political events. The primary sample covered the period from to , and data utilized are daily, weekly, and quarterly stock returns data derived from the Hang Seng Index.
The results from the study indicated that political events occurred during the sample period have significantly impacted movements in the index of Hang Seng. Moreover, volatility effects of the political risk variables were also significant.
Resolved complaints | About the ABC
The study covered the period between and Domestic and foreign political news data were collected from the Reuters Turkish language news service. The study resulted that political news significantly increased both the mean and variability of ISE returns. Furthermore, key political news such as the news about Iraq war, Cyprus peace negotiations, and November elections had significantly impacted the intra-daily ISE index returns.
Beaulieu et al. The study also used the GARCH model to estimate the volatility of stock returns and assess whether the significance of abnormal returns is influenced by variable volatility. The results showed that uncertainty regarding the outcome of the referendum has had an effect on the stock returns of the Quebec firms.
Resolved complaints
The study also indicated that Quebec firms exposed to political risk are the firms most affected by the referendum. The sample included elections took place in the period between and An event study methodology was employed to gauge the impact of elections on the second moment of return distribution, while GARCH models served as a benchmark for measuring abnormal volatility and for isolating the country-specific component of variance.
Index data consisted of daily closing prices and ranged from to Suleman selectively collected political news items. Results of the study indicate that good and bad political news have different impact on stock return and volatility, precisely, good news has positive impact on stock returns, and decreased volatility, while bad news has negative impact on returns and increased volatility. Moreover, the results confirmed that bad news has a stronger impact on the volatility than good news.
Kulwarothai [] conducted a study examining the effect of political risk measured through political news on the return volatility of Thai Stock Exchange SET between and Results demonstrated that political news significantly impacts stock return volatility, and negative shocks derived from unfavorable news have greater effect on the volatility than positive shocks derived from favorable news. The findings of the study were obtained using a quantitative research methodology. The statistical technique used in the paper is univariate time series analysis. The research design of this paper is based on the following two hypotheses; H 1 : There is a significant effect of political news releases on both stock market returns and volatility, H 2 : Bad political news has greater effect on stock market returns and volatility than good news.
Stock market data cover the period from January to December and include observations. The main sample was also divided into two subperiods to insulate the dominating impacts of both the Global Financial Crisis and Federal Reserve Tapering on the overall Turkish economy, and especially on BIST index.
Thus, the first subperiod spans the time from January to May and includes observations, while the second subperiod runs from June to December and includes observations.
1 Introduction
During the overall study period, a total of out of almost political news headlines were carefully selected, categorized, and analyzed. Table 1 provides an example and additional explanation of the categorizing criteria. The study is based on secondary data analysis. To examine the impact of political news on stock market returns and volatility, BIST index was used as a capitalization-weighted index which represents the Turkish national market companies, other than investment trusts.
Previous Events
The reason behind choosing the Guardian as a foreign source of news was its selectivity and universality, especially for the growing number of foreign investors investing in Borsa Istanbul during the last years. The study is focusing on the parametric models, as they are more suitable to analyze nonlinear relationships. Different nonlinear volatility models symmetric and asymmetric GARCH type models were used to test the two hypotheses, since they have the ability to explain major features common to financial market data such as leptokurtosis, volatility clustering, long memory, and leverage effects [ Brooks, ].
All models and tests were run on EViews 9. Logarithmic returns were used to build returns time series, since they are time additive, so log return was defined as:. Therefore, in practice, one dummy variable is added to the mean and variance equations of the two models to indicate the presence or absence of political news.
The dummy variable is:. This model was developed independently by Bollerslev and Taylor []. The model proved its success in predicting conditional variances and had a good reputation of avoiding overfitting and being more parsimonious than high-order ARCH model [ Brooks, ]. Another important feature of the model is that it can capture risk not only by using the variance series but also by using the standard deviation of the series [ Asteriou and Hall, ]. That is to say, a positive risk premium value denotes that there is a positive relationship between the mean and the variance of asset return [ Rossi, ].
Asymmetric models were also employed to capture the leverage effects or the asymmetric responses to negative and positive shocks. In simple words, asymmetric models can help in determining whether the influence of a negative shock to the volatility of an asset is greater than that of a positive shock of the same magnitude [ Brooks, ].
The dummy variables are:.
The EGARCH model of Nelson [] is regarded as a remarkable structure with features to 1 allow good news and bad news to have a different impact on volatility and 2 allow big news to have a greater impact on volatility, whereas the standard, symmetric GARCH models do not [ Engle and Ng, ]. The model has a special modeling structure that adds a further term dummy variable to capture potential asymmetries in terms of negative and positive shocks.
The aim of this analysis is to check the competency of the model and the readiness of the data for the next analysis.
Upcoming Events
From Figures 1 to 3 , it is clearly seen that when the global financial crisis had struck in BIST prices witnessed a sharp decline and huge volatility. The other sharp decline occurred in the beginning of , when the Federal Reserve signaled that the tapering of asset purchases could begin very early. When returning back to the collected political news and flipping through the events happened in that two periods, it was concluded that none of the collected news at that time was at the same importance level as the news of both the global financial crisis and Fed tapering to be confirmed next section.
However, the period from mid to the end of was much influenced by Turkey-related political events. Citation: International Journal of Management and Economics 55, 2; Another important feature of the graphs is that volatility clustering is strongly discernible in the series and can be observed through the periods of high volatility followed by periods of high volatility and vice versa.
Thus, one can say that there is some empirical evidence that volatility is autocorrelated.
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Descriptive statistics can help in characterizing the main features of our data. Table 2 summarizes some statistical properties of the daily returns of the BIST index in the three periods studied. As can be noticed in the table, mean returns are all positive, but vary slightly across periods. The second subperiod appears to have a smaller mean return and standard deviation than the first subperiod. Moreover, skewness is negative in all periods, which means that the more of the returns lie on the left side of the average return.
The kurtosis is positive and extremely large more than three and this means there is a high probability for extreme values in the series. According to the Jarque—Bera value, the null hypothesis of normality can be rejected for the whole period and the subperiods, since its p value is less than 0.
Additional testing for normality can be done using graphical examinations such as the Quantile—quantile Q—Q plots. Figure 4 presents the results of this examination. Seemingly, all the three quantiles each appear to be non-normally distributed and have heavy tails, since the pattern is above, below, above, and below the reference line. Consequently, the blue points crossed the reference line three times, which indicates that our data are leptokurtic. In this section, augmented Dickey—Fuller ADF unit root test was utilized for testing whether the return series are stationary or not.
ADF test is the most widely used test for this purpose. Table 3 reports the results of the ADF test with an automatically selected maximum lag length used by the AIC of 26 for the full sample period, 22 for the first subperiod, and 21 for the second subperiod. Performing a test for conditional heteroscedasticity and nonlinearity is an essential step when we think of using volatility models. FTTC requires power to be provided from the premises to the kerb distribution point. NTDs provide user—network interface UNI connections for connection of in-premises devices, typically though multiple modular jacks.
The NTD cannot be used as a Layer 3 router for in-premises networking. The NBN network includes a range of connection technologies for both wired communication copper, fibre optic, and hybrid fibre-coaxial and radio communication satellite and fixed wireless. RSPs connect to these networks at points of interconnect see Points of Interconnect.
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