scholarly journals Stock market volatility during dividend announcement a case of selected scripts

2012 ◽  
Vol 1 (3) ◽  
pp. 184-190
Author(s):  
POORNIMA S ◽  
CHITRA V
Keyword(s):  
Information Technology ◽  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Daily Data ◽  
Time Period ◽  
The Individual ◽  
Autoregressive Conditional Heteroscedasticity ◽  
The Empirical Analysis ◽  
Made In

An attempt has been made in this paper to explain the stock market volatility at the individual script level and at the aggregate indices level. The empirical analysis has been done by using Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model. It is based on daily data for the time period from January 2007 to December 2009. The analysis reveals the same trend of volatility in the case of aggregate indices and three different sectors such as Banking,Information Technology and Cement. The GARCH (1,1) model is persistent for all the five aggregate indices and individual company.

An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility

2014 ◽  
Vol 14 (01) ◽  
pp. 1550001 ◽  
Author(s):  
S. Lahmiri ◽  
M. Boukadoum
Keyword(s):  
Stock Market ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Market Volatility ◽  
Ensemble Model ◽  
Stock Market Volatility ◽  
Portfolio Risk ◽  
Backpropagation Algorithm ◽  
The Individual ◽  
Autoregressive Conditional Heteroscedasticity

Accurate forecasting of stock market volatility is an important issue in portfolio risk management. In this paper, an ensemble system for stock market volatility is presented. It is composed of three different models that hybridize the exponential generalized autoregressive conditional heteroscedasticity (GARCH) process and the artificial neural network trained with the backpropagation algorithm (BPNN) to forecast stock market volatility under normal, t-Student, and generalized error distribution (GED) assumption separately. The goal is to design an ensemble system where each single hybrid model is capable to capture normality, excess skewness, or excess kurtosis in the data to achieve complementarity. The performance of each EGARCH-BPNN and the ensemble system is evaluated by the closeness of the volatility forecasts to realized volatility. Based on mean absolute error and mean of squared errors, the experimental results show that proposed ensemble model used to capture normality, skewness, and kurtosis in data is more accurate than the individual EGARCH-BPNN models in forecasting the S&P 500 intra-day volatility based on one and five-minute time horizons data.

scholarly journals FINANCIAL STABILITY AND INCOME GROWTH IN EMERGING MARKETS

2020 ◽  
Vol 23 (2) ◽  
pp. 201-220
Author(s):  
Bashir T. Mande ◽  
Afees Salisu ◽  
Adeola N. Jimoh ◽  
Fola Dosumu ◽  
Girei H. Adamu
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Financial Stability ◽  
Banking Sector ◽  
Financial System ◽  
Market Volatility ◽  
Income Growth ◽  
Stock Market Volatility ◽  
The Individual ◽  
The Impact

In this paper, we examine the extent to which financial stability matters for income growth in emerging markets. Using dynamic panel estimation techniques, we explore both the stock market and banking sector dimensions of the financial system to show that both stock market volatility and non-performing loans are detrimental to income growth in these markets. We, however, find the magnitude of the impact to be relatively more pronounced when the underlying source of instability in the financial system is stock market volatility. Overall, we find the impact of financial stability on income growth to be more statistically relevant when measured using the individual indicators of financial instability as compared to their composite indicator.

scholarly journals THE INTER-RELATIONSHIP BETWEEN COMMODITY ENERGY PRICES AND STOCK MARKET VOLATILITY IN SAUDI-ARABIA

2020 ◽  
Vol 5 (1) ◽  
pp. 270-293 ◽  
Author(s):  
Manal Alsufyani ◽  
Tamat Sarmidi
Keyword(s):  
Saudi Arabia ◽  
Stock Market ◽  
Market Volatility ◽  
Energy Price ◽  
Stock Market Volatility ◽  
Energy Prices ◽  
Exogenous Variables ◽  
Policy Makers ◽  
Stock Market Return ◽  
Autoregressive Conditional Heteroscedasticity

Background and Purpose: The present study examines the inter-relationship that exists between commodity energy price as well as stock market volatility in Saudi-Arabia. The focus of the study is to test if changes in commodities energy prices (oil related) cause significant changes in the stock market volatility of Saudi Arabia.   Methodology: This study made use of a generalized autoregressive conditional heteroscedasticity model which has exogenous variables (GARCH-X), thus able to employ the commodity energy price inform of an exogenous so as to test the conditional variance of the Saudi-Arabia stock market return.   Findings: The findings from the estimated model provide evidence that only the ARCH and GARCH parameters are significant while the exogenous variables are insignificant. It is concluded that other factors affect the volatility of the Saudi-Arabia stock market, but not the commodity energy price.   Contributions: This study recommends that, policy makers, investors, and regulators should give emphasis on macro-economic variables and volatility interdependence with other correlated markets, especially during energy price shock that affected the volatility of Saudi-Arabia stock market.    Keywords: Energy price, GARCH-X, Saudi Arabia, stock market, volatility.   Cite as: Alsufyani, M., & Sarmidi, T. (2020). The inter-relationship between commodity energy prices and stock market volatility in Saudi-Arabia. Journal of Nusantara Studies, 5(1), 270-293. http://dx.doi.org/10.24200/jonus.vol5iss1pp270-293

scholarly journals Election Cycles and Stock Market Reaction: International Evidence

2015 ◽  
Vol 30 (3) ◽  
pp. 23-40
Author(s):  
Park Cheol Beom ◽  
An Ji Youn
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Dynamic Pattern ◽  
International Evidence ◽  
Stock Market Reaction ◽  
Before And After ◽  
Using Data ◽  
Over Time ◽  
Made In

This study investigates movements of stock market volatility during election periods (the six months before and after an election) using data from 16 countries. The main findings of this study are (1) volatility declines over time as elections approach, (2) the level of volatility during election periods is lower than that during nonelection periods, and (3) volatility rises quickly during election months and immediately after the elections. The first and second findings confirm assertions made in previous studies, such as Pantzalis, Stangeland, and Turtle (2000) and Wisniewski (2009), regarding the dynamic pattern of stock market volatility during election years.

scholarly journals COUNTRY RATING AND STOCK MARKET VOLATILITY: AN EMPIRICAL ESTIMATION FOR KARACHI STOCK EXCHANGE

2014 ◽  
Vol 02 (01) ◽  
pp. 07-14
Author(s):  
Muhammad Bilal Saeed ◽  
◽  
Arshad Hassan ◽  
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Empirical Estimation ◽  
Market Returns ◽  
Stock Market Returns ◽  
Daily Data ◽  
Asymmetric Garch ◽  
The Relationship

This study is aimed to explore the relationship between country rating and volatility of Karachi Stock Exchange for the period 1999 to 2012. This study employs daily data of country ratings and stock market returns to investigate influence of rating on volatility of market. Univariate Asymmetric GARCH model is used to explore the relationship and results reveal that country rating has a significant role in explaining volatility in Karachi Stock Exchange.

scholarly journals Stock Market Volatility Measure Using Non-Traditional Tool Case of Germany

2018 ◽  
Vol 32 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Naeem Ahmed ◽  
Mudassira Sarfraz
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Index ◽  
Bench Mark ◽  
Stock Market Volatility ◽  
Time Period ◽  
German Stock ◽  
Cyclical Trend ◽  
German Stock Market ◽  
Sample Time

Abstract. This study examines the stock market volatility of German bench-mark stock index DAX 30 using logarithmic extreme day return. German stock markets have been analyzed extensively in literature. We look into volatility issue from the standpoint of extreme-day changes. Our analysis indicates the non-normality of German stock market and higher probability of negative trading days. We measure the occurrences of extreme-day returns and their significance in measuring annual volatility. Our time series analysis indicates that the occurrences of extreme-days show a cyclical trend over the sample time period. Our comparison of negative and positive extreme-days indicates that negative extreme-days overweigh the positive extreme days. Standard deviation, as measure of volatility used traditionally, gives altered ranks of annual volatility to a considerable extent as compared to extreme-day returns. Lastly, existence of extreme day returns can be explained by past period occurrences, which show predictability.

scholarly journals The impact of news events on the Tunisian stock market volatility: A Post-revolutionary study

2017 ◽  
Vol 14 (2) ◽  
pp. 230-237
Author(s):  
Salma Zaiane ◽  
Atef Ben Allita
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Composite Index ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Social Events ◽  
Daily Data ◽  
Egarch Model ◽  
Tunisian Revolution ◽  
The Impact

This study examines the impact of political, economic, social and terrorism events on market volatility over the period of the Tunisian revolution from December 1, 2010 to May 29, 2015. Our study is based on daily data of three variable: Tunindex the composite index of the Tunisian stock market, the financial companies’ index, and the exchange rate Eur/Tnd, in order to detect the influence of each type of event on these three selected variables. Using an EGARCH model, the empirical evidence highlights that the fourth types of events affect the Tunindex market volatility. In fact, the political, social and terrorism events increase the volatility of the index. However, the economic events diminish this volatility. Furthermore, we notice that only political and social events influence the market volatility of the financial companies. However, exchange rate Eur/Tnd was affected only by economic and social events.

Volatility in World Equity Markets

2003 ◽  
Vol 06 (03) ◽  
pp. 273-290
Author(s):  
Steven J. Cochran ◽  
Jean L. Heck ◽  
David R. Shaffer
Keyword(s):  
Stock Market ◽  
Relative Stability ◽  
Equity Markets ◽  
Past Research ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Recent Period ◽  
Time Period ◽  
The Us ◽  
Country Specific

Past research suggests that US stock market volatility was greater during the 1930s than in any other 10-year time period and the post-WWII era is a period of relative stability, despite slightly higher volatility levels during the 1970s and 1980s. More recent evidence suggests that volatility levels from 1998 to 2001 have more in common with 1930s levels than with any other time period. We extend this body of research to include the volatility experiences of seven equity markets in the US, Europe, and Asia. For each market, we compare the average monthly volatility of each five-year period, beginning with January 1923, with that for the most recent period in the study, January 1998 to August 2001. We find that when there are statistical differences between current and past levels of volatility, recent volatility is usually significantly greater than past volatility. In only a small number of cases do we find current volatility to be less than past volatility. This suggests that the 1998–2001 period was unusually volatile for most markets examined. We also find that volatility behavior tends to be country-specific and cannot be generalized on an aggregate basis.

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