Tunisian revolution and stock market volatility: evidence from FIEGARCH model

2015 ◽  
Vol 41 (10) ◽  
pp. 1112-1135 ◽  
Author(s):  
Ahmed Jeribi ◽  
Mohamed Fakhfekh ◽  
Anis Jarboui
Keyword(s):  
Stock Market ◽  
Financial Services ◽  
Stock Exchange ◽  
Leverage Effect ◽  
Stock Market Volatility ◽  
Political Uncertainty ◽  
Content Type ◽  
Stock Indices ◽  
Tunisian Revolution ◽  
The Impact

Purpose – Previously elaborated research works, dealing with the political uncertainty effect on stock market, have been primarily concerned with such political events as terrorist attacks, elections, wars, natural catastrophes and financial crashes. Such little research has been concerned with civil uprisings and revolutionary movements, as crucial sources of political uncertainty. The purpose of this paper is to study the impact of political uncertainty (resulting from the Tunisian Revolution) on the volatility of major sectorial stock indices in the Tunisian Stock Exchange (TSE). Design/methodology/approach – The authors apply the fractionally integrated exponential generalized autoregressive conditional heteroscedasticity model (FIEGARCH), which helps maintain a direct shock-persistence as well as a shock asymmetric volatility measurement. This model is applied to the daily returns relevant to nine sectorial stock indices and to the Tunisian benchmark index (TUNINDEX) with respect to three sub-periods (before, during and follows the Tunisian Revolution). Findings – The reached findings suggest that the shock impact throughout the Revolution period on construction, industries, consumer services, financial services, financial companies indices’ sectorial and the TUNINDEX return volatilities have proven to be permanent, while its persistence on the other indices has been discovered to be transitory. In addition, the achieved results appear to reveal a low leverage effect on all indices. This result seems to be very important since the Tunisian Revolution turns out to have a very important effect on the TSE. Originality/value – The paper’s empirical contribution lies in using the FIEGARCH approach to model the Tunisian sectorial indices’ volatility dynamics, persistence degree and leverage effect. This contribution goes a long way in helping regulators and international investors to further recognize the extent to which political instability does participate in affecting the TSE.

scholarly journals The Impact Of Political Instability Driven By The Tunisian Revolution On Stock Market Volatility: Evidence From Sectorial Indices

2018 ◽  
Vol 34 (2) ◽  
pp. 339-354 ◽  
Author(s):  
Salma Zaiane
Keyword(s):  
Stock Exchange ◽  
Bad News ◽  
Good News ◽  
Stock Market Volatility ◽  
Asymmetric Effect ◽  
Political News ◽  
Stock Indices ◽  
Terrorist Acts ◽  
Tunisian Revolution ◽  
The Impact

The aim of this paper is to study the impact of political uncertainty, driven by the Tunisian Revolution, on return and volatility of major sectorial stock indices in the Tunisian Stock Exchange. We specifically use EGARCH (1.1) model from 01/12/2010 to 31/08/2016. This model is applied to the daily returns relevant to ten sectorial stock indices and to the Tunisian benchmark index (TUNINDEX). To test the impact of political news on returns and volatility, we divided them into two groups (good and bad news). Our results show that both of good and bad news have increased the volatility of major selected indices, including the TUNINDEX. However, the return of all indices are not affected by the political news. We then examined the impact of terrorism on the behavior of indices return and volatility. Results show that the Tunisian market responds significantly to terrorist acts. Hence, the return declines and the volatility increase the day of terrorist attacks. Furthermore, results confirm that bad news have stronger effect on the volatility than good news, which reveal the asymmetric effect of volatility.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals Financial contagion: The impact of the volatility of global stock exchanges on the Lima-Peru Stock Exchange

2020 ◽  
Vol 1 (1) ◽  
pp. 13-27
Author(s):  
Pedro Pablo Chambi Condori
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Exchange ◽  
Market Volatility ◽  
Financial Contagion ◽  
Stock Market Volatility ◽  
Important Indicator ◽  
Dynamic Correlation ◽  
High Volatility ◽  
The Impact

What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.

scholarly journals Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Hana Ghaneei
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Return ◽  
Stock Exchange ◽  
Markov Switching ◽  
Stock Index ◽  
Var Model ◽  
Content Type ◽  
Oil Market ◽  
The Impact

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

scholarly journals Impact of gold and oil prices on the stock market in Pakistan

2020 ◽  
Vol 25 (50) ◽  
pp. 279-294
Author(s):  
Aiza Shabbir ◽  
Shazia Kousar ◽  
Syeda Azra Batool
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Structural Break ◽  
Oil Prices ◽  
Current Data ◽  
Small Sample ◽  
Data Set ◽  
Content Type ◽  
The Impact

Purpose The purpose of the study is to find out the impact of gold and oil prices on the stock market. Design/methodology/approach This study uses the data on gold prices, stock exchange and oil prices for the period 1991–2016. This study applied descriptive statistics, augmented Dickey–Fuller test, correlation and autoregressive distributed lag test. Findings The data analysis results showed that gold and oil prices have a significant impact on the stock market. Research limitations/implications Following empirical evidence of this study, the authors recommend that investors should invest in gold because the main reason is that hike in inflation reduces the real value of money, and people seek to invest in alternative investment avenues like gold to preserve the value of their assets and earn additional returns. This suggests that investment in gold can be used as a tool to decline inflation pressure to a sustainable level. This study was restricted to use small sample data owing to the availability of data from 1991 to 2017 and could not use structural break unit root tests with two structural break and structural break cointegration approach, as these tests require high-frequency data set. Originality/value This study provides information to the investors who want to get the benefit of diversification by investing in gold, oil and stock market. In the current era, gold prices and oil prices are fluctuating day by day, and investors think that stock returns may or may not be affected by these fluctuations. This study is unique because it focusses on current issues and takes the current data in this research to help investment institutions or portfolio managers.

Modeling volatility on the Karachi Stock Exchange, Pakistan

2016 ◽  
Vol 10 (3) ◽  
pp. 253-275 ◽  
Author(s):  
Shahan Akhtar ◽  
Naimat U. Khan
Keyword(s):  
Financial Crises ◽  
Stock Exchange ◽  
Small Sample ◽  
Leverage Effect ◽  
Data Series ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Modeling Volatility ◽  
The Impact

Purpose The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods. Design/methodology/approach This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model. Findings The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels. Originality/value Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

scholarly journals Analyzing the leverage effect in stock indexes with autoregressive generalized variance models

2017 ◽  
Vol 7 (1) ◽  
pp. 178-197
Author(s):  
Ilhami Karahanoglu ◽  
Harun Ercan
Keyword(s):  
New York ◽  
Stock Market ◽  
Stock Exchange ◽  
General Structure ◽  
Leverage Effect ◽  
Developed Economies ◽  
Stock Indices ◽  
Financial Events ◽  
The Right ◽  
Stock Indexes

In developing and developed economies understanding the movement of stock market is extremely important to understand the riskiness of the investment, general behavior of the economy and taking the right position against the forthcoming financial events. In this study, the volatility of Turkish, Brazilian, German, London and New York Stock Indices are analyzed with ARCH type of modeling and the leverage effect is researched for the period between 04.01.2011 to 26.05.2015. There are two interesting results of this study. Firstly; it was seen that in all of those stock markets there is a leverage effect which means the negative movement in volatility is stronger than the positive one. Secondly the general structure of the ARCH type of modeling which explains the leverage effect shows similarity between those developed and developing markets.   Keywords: GARCH, TGARCH, leverage, stock exchange.

Foresight for stock market volatility – a study in the Indian perspective

foresight ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Jitendra Kumar Dixit ◽  
Vivek Agrawal
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Investment Decision ◽  
Market Volatility ◽  
Future Research ◽  
Stock Market Volatility ◽  
Content Type ◽  
Constant Variance ◽  
Investment Decision Making

Purpose Volatility is a permanent behavior of the stock market around the globe. The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors and creates hesitancy among risk averse investors as high volatility means high return with high risk. Investors always consider market volatility before making any investment decisions. Random fluctuations are termed as volatility of stock market. Volatility in financial markets is reflected because of uncertainty in the price and return, unexpected events and non-constant variance that can be measured through the generalized autoregressive conditional heteroscedasticity family models and that will give an insight for investment decision-making. Design/methodology/approach Daily data of the closing value of Bombay Stock Exchange (BSE) (Sensex) and National Stock Exchange (NSE) (Nifty) from April 1, 2011 to March 31, 2017 is collected through the web-portal of BSE (www.bseindia.com) and NSE (www.nseindia.com) for the analysis purpose. Findings The outcome of the study suggested that P-GARCH model is most suitable to predict and forecast the stock market volatility for both the markets. Research limitations/implications Future research can be extended to other stock market segments and sectoral indices to explore and forecast the volatility to establish a trade-off between risk and return. Originality/value The results of previous studies available are not conducive to this research, and very limited scholarly work is available in the Indian context, so required to be re-explored to identify the appropriate model to predict market volatility.

scholarly journals Spillover effects in the financial year cycle for Indian markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Parul Bhatia
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Year Effect ◽  
Autoregressive Conditional Heteroskedasticity

PurposeThe stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.Design/methodology/approachThe oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.FindingsThe findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.Research limitations/implicationsThe study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.Originality/valueThe paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

scholarly journals Conditional volatility nexus between stock markets and macroeconomic variables

2018 ◽  
Vol 45 (1) ◽  
pp. 77-99 ◽  
Author(s):  
Ghulam Abbas ◽  
David G. McMillan ◽  
Shouyang Wang
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Macroeconomic Factors ◽  
Stock Market Returns ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Volatility Transmission ◽  
The Impact

Purpose The purpose of this paper is to analyse the relation between stock market volatility and macroeconomic fundamentals for G-7 countries using monthly data over the period from July 1985 to June 2015. Design/methodology/approach The empirical methodology is based on two steps: in the first step, the authors obtain the conditional volatilities of stock market returns and macroeconomic variables through the GARCH family of models. The authors also incorporate the impact of early 2000s dotcom and the global financial crises. In the second step, the authors estimate multivariate vector autoregressive model to analyze the dynamic relation between stock markets return and macroeconomic variables. Findings The overall results for G-7 countries indicate a weak volatility transmission from macroeconomic factors to stock market volatility at individual level but the collective impact of volatility transmission is highly significant. Although, the results of block exogeneity indicate a bidirectional causality except UK, but the causal linkage is quite weak from stock market to macroeconomic variables. Moreover, the local financial variables excluding interest rate are closely integrated, and the volatility of industrial production growth and oil price are identified as the most significant macroeconomic factors that could possibly influence the directions of stock markets. Originality/value This research establishes the nature of the links between stock market and macroeconomic volatility. Research to date has been unable to satisfactorily establish the empirical nature of such links. The authors believe this paper begins to do this.

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