Volatility Dynamics in an Emerging Economy: Case of Karachi Stock Exchange

Ismail

2020

AJPAS

The stock market in an emerging country like Pakistan has been volatile from the earliest times. This paper studies the volatility of Pakistan Stock Exchange (PSX) (using Karachi Stock Exchange 100 Index (KSE-100) as a proxy) through the application of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family models. The sample period consists of 4831 daily observations for the 19 year trading period (from 2000 to 2019). Symmetric GARCH (2, 1), asymmetric EGARCH (1, 1), GJR-GARCH (1, 1) and APARCH (1, 1) models were used under Gaussian distributional assumptions. The results validate the empirical findings of previous studies conducted in Pakistan that log returns of KSE-100 Index are characterized by volatility clustering, time-variability, leptokurtic distribution with dominant ARCH and GARCH effects. An interesting feature of Pakistan Stock Exchange revealed by asymmetric models (used in the study) is that PSX is more volatile to good news than bad news. Moreover EGARCH (1, 1) outperforms all other models of the study on the basis of AIC/BIC criterion. However the comparison of correlations of variances predicted by three asymmetric models reveal that correlations among them are very high, with minimum correlation being 98%. This essentially means that all three asymmetric models provide a good fit to PSX.

Section: Asymmetric Modelssupporting

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Time Series Analysis of Stock Market Volatility in Pakistan

Ismail

2020

AJPAS

“…Zafar, Urooj, and Durrani (2008) applied GARCH (1, 1) model on data set from 2002-2006. Mahmud and Mirza (2011) tested EGARCH model on the data from 2004 to 2009 i.e. before and after the financial crisis.…”

Section: Motivations For Asymmetry and Leverage Effectmentioning

confidence: 99%

“…Theoretically, this study adds to the existing literature in three different ways. Firstly, very limited literature is available that empirically investigates the relationship between macroeconomic factors and stock return volatility and its dynamics, particularly in the context of developing economies (Mahmud & Mirza, 2011;Qayyum & Anwar, 2011;Ho & Lyke, 2017). Hence, the current study addresses this deficiency.…”

Section: Significance Of the Studymentioning

confidence: 99%

Return Volatility and Macroeconomic Factors: A Comparison of US and Pakistani Firms

Jan¹,

Khan²

2018

BER

This study explores the effect of selected economic factors on stock return volatility along with asymmetry and leverage effects on comparative basis of the New York Stock Exchange (NYSE) and Pakistan Stock Exchange (PSX). These dimensions are further investigated with respect to size and age of the firms. The daily stock returns of all the firms on both NYSE and PSX and macroeconomic factors are considered for the period 2000-2015. The results from GARCH (1, 1) revealed that all the economic factors have proven their significance in determining the stock returns volatility in both the markets with respect to firm's size and age. More precisely, a negative relationshi p is observed for market returns (MR), exchange rate (EXR), and oil returns (OIL) with stock return volatility for majority of the firms in both stock markets. Furthermore, risk-free rate (RFR) showed positive and negative effect on the stock return volatility of majority of the firms in NYSE and PSX markets respectively. However, with respect to size and age effects, firms in both the markets exhibited entirely different behavior for all the macroeconomic factors. Further, using EGARCH model, an evidence of asymmetry and leverage effect (with negative coefficient) is found in NYSE and partial evidence (both negative and positive coefficients) in PSX is observed. Again, these results vary with respect to firms' features in both the markets. Therefore, the results of the current study clearly show that there are significant differences in both markets and the investors can diversify their investments and shape their liquidity positions in both markets in order to exploit the maximum benefits from the market and firms specific factors.

Economic Research-Ekonomska Istraživanja

“…Ross, Dalton, and Sertyesilisik (2013), Chen, Chen, and Wei (2011), Gupta, Ye, and Saco (2013), Nazarko and Kononiuk (2013), Yazdani-Chamzini, Yakhchali, Volungevičienė, and Zavadskas (2012), Catik and Karacuka (2012), Mahmud and Mirza (2011) among other authors recently applied different mathematical models for forecasting of dynamics of the various phenomena.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the demand for blue-collar workers in the construction sector in 2020: the case of Lithuania

Vilutienė

Podvezko

Ambrasas

et al. 2014

After 2020 all new buildings must be energy efficient, which means that the construction industry workforce must be prepared and up-skilled. Formal and informal continuing education must respond to these changes, which means the curriculum needs to be updated and supplemented with the necessary knowledge and skills. This article provides the forecasts for blue-collar worker demand by 2020 in the construction industry and the number of construction workers who should be up-skilled or have extra training to be able to build energy efficient buildings and to install the renewable energy systems. The need for blue-collar workers in the construction sector up to the year 2020 was estimated by the use of mathematical models and expert evaluation, as well as taking into account the Lithuanian economic growth forecasts for the year 2020, trends of employment changes in the construction sector, the impact of EU support to the national economy and other factors having significant impact on employment in the construction sector.