Emerging Equity Market Volatility

Ogum, George; Beer, Francisca; Nouyrigat, Geneviève

doi:10.1300/j156v06n01_08

Cited by 23 publications

(9 citation statements)

References 26 publications

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“…al., (2017), note that the investors demand a high return for taking more risk. The findings differ with those of Ogum et. al.…”

Section: Literature Reviewcontrasting

confidence: 99%

“…They found high volatility persistence and variability in the parameter estimates of GARCH (1,1) across all the subperiods. Ogum, Beer and Nouyrigat (2005) studied the daily emerging market volatility for Nigeria and Kenya for the period 1988-1998 using EGARCH model. They found that in Kenya, there was positive and significant asymmetric volatility, returns were predictable and there was volatility persistence.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Empirical evidence from the foregoing cross country studies implies that the Nairobi Securities Exchange (NSE) exhibit predictable returns, volatility persistence, and positive and significant asymmetric volatility. However, the question whether the market rewards increased risk is still not conclusive as Ogum et. al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the other hand, the empirical findings differ on a number of issue that include, volatility persistence, existence of a risk premium and the model(s) that are best suited to forecast stock market return volatility. According to Ogum et. al., 2005;Nyamongo and Misati, 2010;Kuttu, 2014;Chege et.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling stock market return volatility in the presence of structural breaks

Ndei

Muchina

Waweru

2019

IJRBS

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This study sought to model the stock market return volatility at the Nairobi Securities Exchange (NSE) in the presence of structural breaks. Using daily NSE 20 share index for the period 04/01/2010 to 29/12/2017, the market return volatility was modeled using different GARCH type models and taking into account four endogenously identified structural breaks. The market exhibited a non-normal distribution that was leptokurtic and negatively skewed and also showed evidence for ARCH effects, volatility clustering, and volatility persistence. We found that by considering structural breaks, volatility persistence was reduced, while leverage effects were found to lead to explosive volatility. In addition, investors were not rewarded for taking up additional risk since the risk premium was insignificant for the full period. However, during explosive volatility, investors were rewarded for taking up more risk. Moreover, we found that risk premium, leverage effects, and volatility persistence were significantly correlated. The GARCH (1,1) and TGARCH(1,1) models were found to be the best fit models to test for symmetric and asymmetric effects respectively. While the GARCH models were able to provide evidence for the stylized facts in the NSE, we conclude that the presence or absence of these features is period specific. This especially relates to volatility persistence, leverage effects, and risk premium effects. Caution should, therefore, be taken in using a specific GARCH model to forecast market return volatility in Kenya. It is thus imperative to pretest the data before any return volatility forecasting is done.

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“…al., (2017), note that the investors demand a high return for taking more risk. The findings differ with those of Ogum et. al.…”

Section: Literature Reviewcontrasting

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Modeling stock market return volatility in the presence of structural breaks

Ndei

Muchina

Waweru

2019

IJRBS

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“…This paper will focus on modelling and forecasting the conditional variance. [6] for Egypt and [7] for Kenya and Nigeria are other studies that focused on African markets.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using Garch Models

Magnus¹,

Oteng-Abayie²

2006

American J. of Applied Sciences

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This paper models and forecasts volatility (conditional variance) on the Ghana Stock Exchange using a random walk (RW), GARCH(1,1), EGARCH(1,1), and TGARCH(1,1) models. The unique 'three days a week' Databank Stock Index (DSI) was used to study the dynamics of the Ghana stock market volatility over a 10-year period. The competing volatility models were estimated and their specification and forecast performance compared with each other, using AIC and LL information criteria and BDS nonlinearity diagnostic checks. The DSI exhibits the stylized characteristics such as volatility clustering, leptokurtosis and asymmetry effects associated with stock market returns on more advanced stock markets. The random walk hypothesis is rejected for the DSI. Overall, the GARCH (1,1) model outperformed the other models under the assumption that the innovations follow a normal distribution.

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