scholarly journals What Causes Stock Market Volatility in Pakistan? Evidence from the Field

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Bushra Ghufran ◽  
Hayat M. Awan ◽  
Aftab Khan Khakwani ◽  
Muhammad Azeem Qureshi
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Risk Attitude ◽  
Market Volatility ◽  
Primary Data ◽  
Individual Investors ◽  
Herd Behavior ◽  
Stock Market Volatility ◽  
Egarch Model ◽  
High Volatility

We examined the presence of volatility at the Karachi Stock Exchange (recently changed the name to Pakistan Stock Exchange) (KSE) by fitting Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to 25 years’ index data. We found that the ARCH effects are present in the data indicating the stock market cluster volatility during the period under study. We found persistent high volatility in the stock market and presence of negative leverage effect. Moreover, we tried to identify the factors causing stock market volatility by collecting and analyzing the primary data obtained from 246 individual investors of stock market and 28 brokers listed with KSE. Our results show that investors consider political situation as the most important factor causing turbulence in the stock market. Interviews with the brokers also confirmed this. The second most important factor identified by investors is the herd behavior among investors that results in over- and underpricing of stocks and the overall market shows a volatile behavior. Our findings suggest that individual investor’s behavioral dimensions of involvement, risk attitude, and overconfidence are significantly associated with factors causing market volatility.

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.

Day Of The Week And Its Effect On Stock Market Volatility: Evidence From An Emerging Market

2013 ◽  
Vol 29 (6) ◽  
pp. 1727 ◽  
Author(s):  
Omar Farooq ◽  
Mohammed Bouaddi ◽  
Neveen Ahmed
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Emerging Market ◽  
International Markets ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Foreign Investors ◽  
High Volatility ◽  
Day Of The Week

This paper investigates the day of the week effect in the volatility of the Saudi Stock Exchange during the period between January 7, 2007 and April 1, 2013. Using a conditional variance framework, we find that the day of the week effect is present in the volatility. Our results show that the lowest volatility occurs on Saturdays and Sundays. We argue that due to the closure of international markets on Saturdays and Sundays, there is not enough activity in the Saudi Stock Exchange. As a result, the volatility is the lowest on these days. Our results also show that the highest volatility occurs on Wednesdays. We argue Wednesday, being the last trading day of the week, corresponds with the start of four non-trading days (Thursday through Sunday) for foreign investors. Fearing that they will be stuck up with stocks in case some unfavorable information enters the market, foreign investors tend to exit the market on Wednesdays. As a result of excessive trading, there is high volatility on Wednesdays.

scholarly journals Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Approximate Entropy ◽  
Market Volatility ◽  
Comparative Approach ◽  
Stock Market Volatility ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

Price Limit and Volatility in Taiwan Stock Exchange: Some Additional Evidence from the Extreme Value Approach

2007 ◽  
Vol 10 (01) ◽  
pp. 51-61 ◽  
Author(s):  
Aktham I. Maghyereh ◽  
Haitham A. Al Zoubi ◽  
Haitham Nobanee
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Extreme Value ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Price Limits ◽  
Stock Volatility ◽  
Price Limit ◽  
Taiwan Stock Exchange

We reexamine the effects of price limits on stock volatility of Taiwan Stock Exchange using a new methodology based on the Extreme-Value technique. Consistent with the advocates of price limits, we find that stock market volatility is sharply moderated under more restrictive price limits.

scholarly journals Which Model Performs Better While Forecasting Stock Market Volatility? Answer for Dhaka Stock Exchange (DSE)

2018 ◽  
Vol 08 (14) ◽  
pp. 3203-3222
Author(s):  
S. M. Abdullah ◽  
Mohammod Akbar Kabir ◽  
Kawsar Jahan ◽  
Salina Siddiqua
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Market Volatility ◽  
Stock Market Volatility

Examining the Dynamic and Non-linear Linkages between Crude Oil Price and Indian Stock Market Volatility

2017 ◽  
Vol 18 (2) ◽  
pp. 388-401 ◽  
Author(s):  
Rakesh Kumar
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Exchange ◽  
Price Change ◽  
International Market ◽  
Oil Price ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Crude Oil Price ◽  
Indian Stock Market

The present study is an attempt to examine the dynamic impact of crude oil price variations in the international market on the Indian stock market volatility. For the purpose, the study uses crude oil monthly price expressed in dollar per barrel, Bombay Stock Exchange (BSE)-listed index BSE Sensex and National Stock Exchange (NSE)-listed CNX Nifty prices for the period from January 2001 to December 2014. GARCH (1,1) model with net crude oil price change as exogenous variable is used to estimate the impact of net oil price change in international market on the conditional volatilities of both the indices. The findings report that net oil price change has a significant impact upon the conditional volatility of both the indices. These findings show that investors redesign their portfolios in response to crude oil price variations in the international market. They can use crude oil price as an important exogenous variable in forecasting models of stock returns and risk in the Indian stock market.

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