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 Stock Market Volatility, Recurrence Interval and Memory Effect

2017 ◽  
Vol 3 (3) ◽  
pp. 14
Author(s):  
Zhang Chi
Keyword(s):  
Stock Market ◽  
Extreme Events ◽  
Risk Estimation ◽  
Loss Probability ◽  
Recurrence Interval ◽  
Market Volatility ◽  
Stock Index ◽  
Extreme Value Statistics ◽  
Stock Index Futures ◽  
Stock Market Volatility

The stock market volatility in 2015 has caused serious damage to investors and the market has also suffered severely and immensely. During the last year, large volatility and extreme events were increasingly frequent than before, which made great theoretical and practical significance to gain a deep understanding of extreme value statistics of the volatility. Extreme events also bring huge risk to financial market, therefore the risk prevention, estimation and prediction are of necessity.The research uses 1-min high-frequency datasets of Shanghai 50 Stock Index Futuresin 2015. The data comes from Tongdaxin Database. The paper is the first one to study Shanghai 50 Stock Index Futures and a relationship between recurrence interval and risk estimation has been constructed. We find the recurrence interval of stock volatility can be fitted with stretched exponential function and the recurrence interval decreases when the threshold decreases. Then we demonstrate the existence of short-term and long-term correlations in recurrence intervals. We further construct a hazard function and define a loss probability to evaluate risk and find a crossover point in the loss probability plot. The study would enable one to improve risk estimation andthere are some shortcomings and need to be perfect in the future.

scholarly journals Influence of non-monetary information signals of the USA on the Ukrainian stock market volatility

2019 ◽  
Vol 16 (1) ◽  
pp. 319-333 ◽  
Author(s):  
Roman Pavlov ◽  
Tetiana Pavlova ◽  
Anna Lemberg ◽  
Oksana Levkovich ◽  
Iryna Kurinna
Keyword(s):  
United States ◽  
Stock Market ◽  
Price Index ◽  
Consumer Price Index ◽  
The United States ◽  
Market Volatility ◽  
Stock Index ◽  
Stock Market Volatility ◽  
Economic Sectors ◽  
The Usa

The Ukrainian PFTS stock index volatility reaction as a whole and its constituent economic sectors (“Basic Materials”, “Financials”, “Industrials”, “Oil & Gas”, “Telecommunications”, “Utilities”) to seven non-monetary US information signals (“Consumer price index”, “Personal spending”, “Unemployment rate”, “Gross domestic product”, “Industrial production”, “Consumer confidence”, “Housing starts”) was carried out for the period 2000–2017 on the basis of closing stock quotations in the trading day format. To assess the “surprise” component direct influence nature of the USA selected non-monetary information signals on the PFTS stock index, an AR-GARCH econometric modelling device was used. The results achieved clearly indicate the presence of some PFTS stock index economic sectors heterogeneous reaction to the United States individual non-monetary information signals announcement. For example, such economic sectors as “Basic Materials”, “Financials”, and “Oil & Gas” volatility response to the US non-monetary information signal “Consumer price index” “surprise” components the opposite of the overall PFTS stock index reaction. It can also be concluded that the United States non-monetary information signals influence on the Ukrainian stock market volatility depends not only on the financial cycle phase and data frequency, but also on the PFTS stock index economic sector.

Web-Based Investor Fear Gauge and Stock Market Volatility: An Emerging Market Perspective

2019 ◽  
Vol 19 (2) ◽  
pp. 127-153 ◽  
Author(s):  
Michael Graham ◽  
Jussi Nikkinen ◽  
Jarkko Peltomäki
Keyword(s):  
Stock Market ◽  
Emerging Market ◽  
Investor Sentiment ◽  
Jel Classification ◽  
Market Volatility ◽  
Stock Index ◽  
Stock Market Volatility ◽  
Web Based ◽  
Emerging Stock Markets ◽  
Global Investors

This article considers web-based global investors’ crash fears as a gauge of global investors’ fears, and examines its effect on stock market volatility in a sample of emerging stock markets. We show that an increase in global investors’ crash fears significantly affects the volatility of stock index returns in emerging markets. The results are robust to the inclusion of the conventional investor sentiment/fear gauge measure, VIX. Thus broadening the set of measures of global investors’ fears is important when explaining emerging market volatilities. JEL Classification: F30, G11, G15

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.

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