scholarly journals Estimation of causal relationship between world gold prices and KSE 100 index: Evidence from Johansen cointegration technique

2018 ◽  
Vol 68 (1) ◽  
pp. 51-77
Author(s):  
Rizwan Raheem Ahmed ◽  
Jolita Vveinhardt
Keyword(s):  
Stock Prices ◽  
Time Series Data ◽  
Equity Returns ◽  
Stock Index ◽  
Series Data ◽  
Sample Period ◽  
Full Sample ◽  
The Third ◽  
Johansen Cointegration ◽  
The Impact

The aspiration of this research paper is to investigate the impact of international gold prices on the equity returns of Karachi Stock Index (KSE100 index) of Pakistan Stock Exchange. The daily observations from January 1, 2000 – June 30, 2016 have been divided into three sub-periods along with the full sample period on the basis of structural breaks. Descriptive analysis used to calculate the average returns, which showed significant returns of KSE100 for the full sample, the first and the third sample periods as compared to gold returns. Standard deviation depicted the higher volatility in all the sample periods. Correlation analysis has shown an inverse relationship amid equity returns and gold returns, whereas, Philips-Perron and Augmented Dickey-Fuller tests have been employed, and time series data became stationary after taking the first difference. Johansen cointegration results have shown that the series are cointegrated in the full-sample and the first sample periods. Thus, this has demonstrated the long run association amid equity returns and gold returns in the first sub-sample and the full-sample periods. However, the second and the third sub-sample periods do not exhibit long-term association amid equity returns of KSE100 and gold returns. The outcomes of Granger causality approach identified bidirectional causation amid equity returns and gold returns in the full sample period in lag 2, and unidirectional causality has been observed from gold prices to stock prices in the full sample and the first sub-sample periods in lag 1 and lag 2 respectively.

scholarly journals Causal Correlation between Exchange Rate and Stock Index: Evidence from VN-Index

2016 ◽  
Vol 12 (8) ◽  
pp. 43
Author(s):  
Tri Dinh Nguyen ◽  
Quang Hung Bui ◽  
Tan Thanh Nguyen
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Sample Period ◽  
Integration Test ◽  
Adf Test

This paper will examine the causal correlation of exchange rates and stock prices in Vietnam. The data is collected daily from March 1<sup>st </sup>2007 to March 1<sup>st</sup> 2014. The whole sample period is divided into two sub-groups as before the stock market bottom, after stock market bottom and full sample period. Unit root tests are employed for checking the stationary of time series data such as ADF test, PP test and KPSS test. This paper employs the co-integration test and Granger causality test to identify the causal correlation between two variables. The results of paper prove that there is no causal correlation between exchange rate and stock price. It means that the stock price has no effect on exchange rate and vice versa. However, after stock market bottom from February 25<sup>th </sup>2009 to March 1<sup>st </sup>2014, this research finds that it has a long-run co-movement between these variables by applying the Johansen test.

Terrorism and Stock Market Linkages: An Empirical Study from a Front-line State

2017 ◽  
Vol 18 (2) ◽  
pp. 365-378 ◽  
Author(s):  
Imtiaz Arif ◽  
Tahir Suleman
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Front Line ◽  
Long Run ◽  
Factor Index ◽  
The Impact

This article investigates the impact of prolonged terrorist activities on stock prices of different sectors listed in the Karachi Stock Exchange (KSE) by using the newly developed terrorism impact factor index with lingering effect (TIFL) and monthly time series data from 2002 (January) to 2011 (December). Johansen and Juselius (JJ) cointegration revealed a long-run relationship between terrorism and stock price. Normalized cointegration vectors are used to test the effect of terrorism on stock price. Results demonstrate a significantly mixed positive and negative impact of prolonged terrorism on stock prices of different sectors and show that the market has not become insensitive to the prolonged terrorist attacks.

Terrorism and Wine Tourism: The Case of Museum Attendance

2018 ◽  
Vol 13 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Olivier Gergaud ◽  
Florine Livat ◽  
Haiyan Song
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Terrorist Attacks ◽  
Series Data ◽  
Regression Estimation ◽  
Sample Period ◽  
Wine Tourism ◽  
The Impact ◽  
The City ◽  
Jel Classifications

AbstractIn this article, we use attendance data from La Cité du Vin, a wine museum in the city of Bordeaux, to assess the impact of the recent wave of terror that affected France on wine tourism. We use recent count regression estimation techniques suited for time series data to build a prediction model of the demand for attendance at this museum. We conclude that the institution lost about 5,000 visitors over 426 days, during which 14 successive terrorist attacks took place. This corresponds to almost 1% of the total number of visitors in the sample period. (JEL Classifications: L83, Z30)

scholarly journals Analysis of Integration of the Indonesian Sharia Stock Index with Developing Countries

2021 ◽  
Vol 9 (2) ◽  
pp. 347
Author(s):  
Budiandru Budiandru ◽  
Deni Nuryadin ◽  
Muhammad Dika Pratama
Keyword(s):  
Developing Countries ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Policy Reforms ◽  
Vector Error Correction ◽  
Trade Relations ◽  
Vector Error ◽  
The Impact

<p><em>Globalization is rapidly causing an integration of economic and financial systems worldwide, resulting in shocks to the Islamic stock index and reducing the benefits of diversification for investors. Therefore, this study analyzes the integration, influence, response, and contribution of shocks to each developing country’s Islamic stock index. Specifically, analyzing the effect of developing country sharia stock index shocks on Indonesia's sharia stock index. The study uses monthly time series data for 2011-2021 with samples from Indonesia, Turkey, Malaysia, Pakistan, Kuwait, and India using the Vector Error Correction Model (VECM) method. The results showed cointegration or a long-term relationship in the developing countries’ sharia stock index. The Malaysian Islamic Stock Index and the Indian Islamic Stock Index influence the Indonesian Islamic Stock Index. Furthermore, the Indonesian Islamic Stock Index stabilized the fastest in response to the Turkish Islamic Stock Index shocks. However, the Malaysian Islamic Stock Index shock contributes the most to the Indonesian Islamic Stock Index. Developing countries could improve the infrastructure of the Islamic stock index and policy reforms. This would minimize the impact of international stock index shocks and accelerate integration. Investors should consider the dominant economic strength, geographical factors, and trade relations in determining portfolio diversification in global economic conditions.</em></p><div class="notranslate" style="all: initial;"> </div>

scholarly journals Impact of Global Oil Shocks on the Russian Stock Market

2021 ◽  
Vol 92 ◽  
pp. 07037
Author(s):  
Igor Lukasevich ◽  
Ludmila Chikileva
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Value Added ◽  
Stock Index ◽  
Series Data ◽  
Var Model ◽  
Oil Shocks ◽  
Russian Market ◽  
Russian Stock Market ◽  
The Impact

Research background: The study focuses on modeling assessment of oil shocks impact on the Russian stock market. Purpose of the article: The purpose of the study is to determine the impact of oil prices abrupt changes on the Russian stock market, its quantitative and temporal specifications. The study consists of two interrelated sections. The first section includes the results of statistical processing of initial data, calculation of their key characteristics and preliminary analysis. The second section of the study is devoted to modeling the assessment of the impact of oil shocks on the behavior of the Russian market RTS stock index. Methods: Based on an extensive sample of daily price values for Brent North sea oil and the Russian stock index RTS for the period from 1997 to May 2020, the study was conducted using models vector auto regression (VAR-model). Findings &Value added: The VAR model was developed and tested to assess the impact of oil shocks on the Russian stock market. Unlike the results of other studies, it is shown that the Brent oil price variance explains only about 10% of the RTS index yield variance in long-term time intervals. The low correlation of time series data and time limit of the impact of oil shocks on the Russian market have been revealed. According to the results of the study, the market recovery takes about 2 months, then the stock index returns to the ‘historical’ range of average ± standard deviation.

Redistributive Politics and Stock Price Dispersion

2003 ◽  
Vol 33 (3) ◽  
pp. 367-395 ◽  
Author(s):  
FIONA McGILLIVRAY
Keyword(s):  
Political Institutions ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Political Change ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Price Changes ◽  
The Impact

Cross-sectional time-series data from fourteen stock markets, from 1973 to 1996, are used to study how political institutions compare in affecting party governments' incentives to enrich one group of industries at the expense of another. Using measures of cross-sectoral variance of price changes within stock markets as a proxy for change in redistributive policy, I show that political change is important in both proportional representation (PR) and majoritarian systems. As parties shift in and out of government, trade and industrial policy is redistributed to favour the parties' industrial supporters. Such changes in policy increase the cross-sectoral dispersion in price changes, with newly advantaged industries seeing their stock increase, while the price of those losing favourable policy declines. The temporal impact of redistribution differs across electoral systems, with the impact of political change being more immediate in majoritarian systems and the effect being more diffuse in PR systems. Majoritarian systems are also more responsive to economic shocks, while changes in economic conditions have few discernable effects on the dispersion of stock prices in PR countries. PR systems, however, experience overall higher levels of dispersion. I contrast these results with the dominant extant arguments of radical policy shifts in majoritarian systems and policy stability in PR systems.

scholarly journals Futures trading and the underlying stock volatility: A case of the FTSE/JSE TOP 40

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Mr. Rabson Magweva ◽  
Mrs. Magret Munyimi ◽  
Mr. Justine Mbudaya
Keyword(s):  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Stock Index ◽  
Futures Contracts ◽  
Series Data ◽  
Policy And Practice ◽  
Stock Volatility ◽  
Top 40 ◽  
The Impact

Purpose: This study analyzed the impact of listing and trading futures contracts on the underlying stock index volatility behavior. The FTSE/JSE TOP 40 index was the index of interest.Methodology: To capture the non-constant variance of the residuals, a modified Generalised Autoregressive Conditionally Heteroscedasticity (GARCH) model was adopted. This model was used was adopted given that financial time series data exhibited ARCH effects. The GARCH model was estimated after dividing the sample period into pre-and post-futures eras.Findings: The research findings point towards stabilization effects on underlying stock volatility and refute the suggestion that futures markets improve the dissemination of information to the corresponding spot markets. On the same note, the introduction of futures increased the volatility persistence of index returns.Unique contribution to theory, policy, and practice: This paper applied a modified-GARCH by incorporating a dummy variable to the traditional GARCH model. The study used an emerging economy as a case study which makes the results and conclusions more specific and applicable. On the same note, the study covered the pre-and post-global crisis of 2007/8 in a Sub-Saharan nation. In practice, stock markets are encouraged to introduce futures contracts on highly volatile spot market assets.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3519-3524
Author(s):  
Loh Chi Jiang ◽  
Preethi Subramanian
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Stock Index ◽  
Series Data ◽  
Autoregressive Integrated Moving Average

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data

2014 ◽  
Vol 18 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Hoang T. P. Thanh ◽  
◽  
Phayung Meesad ◽  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Text Mining ◽  
Stock Market ◽  
Stock Prices ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Support Vector ◽  
Data Set

Predicting the behaviors of the stock markets are always an interesting topic for not only financial investors but also scholars and professionals from different fields, because successful prediction can help investors to yield significant profits. Previous researchers have shown the strong correlation between financial news and their impacts to the movements of stock prices. This paper proposes an approach of using time series analysis and text mining techniques to predict daily stock market trends. The research is conducted with the utilization of a database containing stock index prices and news articles collected from Vietnam websites over 3 years from 2010 to 2012. A robust feature selection and a strong machine learning algorithm are able to lift the forecasting accuracy. By combining Linear Support Vector Machine Weight and Support Vector Machine algorithm, this proposed approach can enhance the prediction accuracy significantly above those of related research approaches. The results show that data set represented by 42 features achieves the highest accuracy by using one-against-one Support Vector Machines (up to 75%) and one-against-one method outperforms one-againstall method in almost all case studies.

Impact of exchange rate and oil price on the yield of sovereign bond and sukuk: Evidence from Malaysian capital market

2017 ◽  
Vol 5 (4) ◽  
pp. 27
Author(s):  
Huda Arshad ◽  
Ruhaini Muda ◽  
Ismah Osman
Keyword(s):  
Exchange Rate ◽  
Capital Market ◽  
Time Series Data ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Sovereign Bond ◽  
Long Run ◽  
Short Run ◽  
The Impact

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.

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