Modeling Time Varying Volatility and Asymmetry of Karachi Stock Exchange (KSE)

Rehman

Economic Research-Ekonomska Istraživanja

et al. 2016

This study analyses risk-return trade-off and behaviour of various volatility dynamics including: volatility, its persistence, mean reversion and speed of mean reversion along with asymmetry and leverage effect on the Pakistani stock market by employing aggregate (aggregate market level) and disaggregate (sectoral level) monthly data for the period from 1998 to 2012. Three generalised autoregressive conditional heteroscedasticity models were applied: GARCH (1,1) for various volatility dynamics; EGARCH for asymmetric and leverage effect and GARCH-M for pricing of risk. The outcomes of the study are as follows: first, the volatility shocks are quite persistent but with varying degrees across the sectors. Both the ARCH effect (short-term effect) and GARCH effect (long-term effect) play their role in generating conditional future stock returns volatility. Further, overall the volatility process is mean reverting; however, the speed of mean reversion varies across the sectors. Secondly, the current study finds little evidence of asymmetry and leverage effect at both aggregate and disaggregates data. Thirdly, the pricing of risk (positive risk premium) is also evident, particularly at the disaggregate data in the Pakistani stock market. Finally, this research study sets the implications for both the policy makers and investors.

Section: Introductionmentioning

confidence: 97%

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Rehman

Economic Research-Ekonomska Istraživanja

et al. 2016

South African J Economics

“…For emerging markets, Poshakwale and Murinde (2001) used the GARCH‐M model to show that risk was not a priced factor for the Eastern Europe emerging market. Using the EGARCH‐M model, Karmakar (2007) and Saleem (2007) found significant evidence of positive risk premium for India and Pakistan, respectively. Using the same model, Yu and Hassan (2008) studied the Middle East and North Africa region, and found significant positive risk‐premia for Bahrain, Oman and Saudi Arabia, and significant negative risk‐premia for Egypt, Jordan, Morocco and Turkey.…”

Section: Introductionmentioning

confidence: 99%

Risk–return Trade‐off and Behaviour of Volatility on the South African Stock Market: Evidence From Both Aggregate and Disaggregate Data

Mandimika¹,

Chinzara²

2012

The study analyses the nature and behaviour of volatility, the risk–return relationship and the long‐term trend of volatility on the South African equity markets using aggregate level, industrial level and sectoral level daily data for the period 1995‐2009. By employing dummy variables for the Asian and the sub‐prime financial crises and the 11 September political shock, the study further examines whether the long‐term trend of volatility structurally breaks during financial crises and major political shocks. Three time‐varying generalised autoregressive conditional heteroskedasticity models were employed: one of them symmetric, and the other two asymmetric. Each of these models was estimated based on three error distributional assumptions. The findings of the study are as follows: First, volatility is largely persistent and asymmetric. Second, risk at both aggregate and disaggregate level is generally not a priced factor on the South Africa (SA) stock market. Third, the threshold autoregressive conditional heteroscedasticity (TARCH) model under the generalised error distribution is the most appropriate model for conditional volatility of the SA stock market. Fourth, volatility generally increases over time, and its trend structurally breaks during financial crises and major global shocks. The policy and investment implications of the findings are outlined.

“…The descriptive analysis was done by taking log returns as done by previous studies e.g. Saleem (2007) and Ahmad, Ahmed, Vveinhardt and Streimikiene (2016). The descriptive analysis shows that among the two indices PSX-100 has the highest mean return of 0.099%, followed by post-CPEC announcement mean return of PSX-100 of 0.018%.…”

Section: Discussionmentioning

confidence: 99%

Highlighting the Dynamics of the Game-Changer: Empirical Evidence from Stock Markets of the Key Players

Ahmad

Sharif

2019

IJELCS

China Pakistan Economic Corredor (CPEC), a predictive game-changer, was perceived to bring favourable changes in the economy of both its players. It was claimed that a boost shall be seen in each sector of the economy especially for Pakistan. Highlighting this key assumption, this study attempts to conduct a comparative analysis of PSX and Shanghai returns for pre and post announcement of CPEC. The time-series data of the two indices i.e. PSX-100 and Shanghai Stock Exchange Composite Index (SHCOMP) is selected ranging from 4th January 2010 to 2nd January 2019. The comparative analysis of both the economy indicates signif icant difference t(2220.237) = 2.872, p = 0.004 before CPEC-announcement and this difference becomes insignif icant post-CPEC announcement t(1654.132) = 0.315, p = 753. Whereas, individual analysis of returns of both the economies indicated a signif icant change for PSX-100 t(1038) = 2.050, p = 0.041 but for SHCOMP this change is not signif icant t(1008) = -0.697, p = 0.486. The f indings of the study reflect the potentials of CPEC to bring changes in Pakistani Stock market than a mature market of China.