On Modeling the Volatility of Nigerian Stock Returns Using GARCH Models

Onwukwe, C. E.; Bassey, B. E. E.; Isaac, I. O.

doi:10.5539/jmr.v3n4p31

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…Student's-t distributional assumptions estimates was found to give best result in tandem with the findings of (Atoi 2014) for the period before the meltdown while the generalized error distribution gives the best estimate after the meltdown. The APARCH model provides the overall best estimate for all the periods which is in agreement with Atoi 2014, Rahman, Rahman andHossain (2013), but contrast the findings of Su (2010), Alberg, Shalit and Yosef (2008), Coffie (2015), Onwukwe, Bassey and Isaac (2011). In general the findings follows the assertion of Osarumwense (2015) that impact of good or bad news on return volatility do not only depend on the asymmetric model but also the choice of the error distribution matters.…”

Section: Discussion Of Findingssupporting

confidence: 83%

Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence

Nageri

2019

Studia Universitatis Babes-Bolyai Oeconomica

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The Nigerian stock market, prior to the 2007-09 global financial crisis witnessed growth but the market encountered sharp reversal from 2007 due to the global financial crisis. This study evaluates good and bad news on the Nigerian stock market with regards to the policy responses as a result of the meltdown. The study used the TGARCH, EGARCH and PGARCH models under three error distributional assumptions for data covering January 2010 to December 2016 using the All Share Index to generate the return series. Findings shows that good news impact return more than negative news of the same magnitude before the meltdown while bad news insignificantly impact return more than positive news after the meltdown. The study concludes that there is information asymmetry in the Nigerian stock market. Thus, it is recommended that on-line real time access to share price movement for investors should be introduced to improve liquidity level and enhance free flow of relevant securities information.

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Section: Discussion Of Findingssupporting

confidence: 83%

Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence

Nageri

2019

Studia Universitatis Babes-Bolyai Oeconomica

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“…This model is identical to the GJR-GARCH except that the latter specifies conditional standard deviation rather than conditional variance. The effectiveness of these models to effectively capture the properties of time series has been substantiated by (Aggarwal, Inclan, & Leal, 1999;Alberg, Shalit, & Yosef, 2008;Onwukwe, Bassey, & Isaac, 2011) in their empirical studies.…”

Section: Review Of Literaturementioning

confidence: 99%

Modeling South African Stock Market Volatility Using Univariate Symmetric and Asymmetric Garch Models

Rusere

Kaseke

2021

IJFB

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Contemporary empirical literature is rich in studies that have modelled and forecasted the nature and behavior of volatility of equity returns in both emerging and advanced stock markets. Modelling and estimating volatility is crucial in dynamic risk management, equity valuation and portfolio diversification. However, South African financial markets have not received ample attention in this regard. It is against this backdrop that we sought to determine the nature and behavior of volatility inherent in the South African stock market. Furthermore, we examined the effect of the 2014 global oil crisis on the volatility spillover in this market. The FTSE/JSE Top 40 index of the Johannesburg Stock Exchange has been selected as the study sample. Sample data for the period spans from

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“…These findings have crucial implications for domestic portfolio selection and management through the hedging opportunities available in the NSE sectors. Onwukwe et al (2011) investigated the time series behaviour of daily stock returns of four firms listed in the Nigerian Stock Market from 2nd January, 2002 to 31st December, 2006, using three different models of heteroscedastic processes, namely: GARCH (1,1), EGARCH (1,1) and GJR-GARCH models respectively. The four firms whose share prices were used in this analysis are UBA, Unilever, Guinness and Mobil.…”

Section: Empirical Review Of Nigeria Stock Exchangementioning

confidence: 99%

Modeling Volatility of Nigeria Stock Exchange Using Multivariate GARCH Models

Adenomon¹,

Mbuk²,

Yahaya³

2019

Preprint

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The aim of this research work was to provide model for predicting stock volatility in Nigeria Stock market. To achieve this, monthly data for Nigerian stock exchange, Exchange rate, Share index and inflation rate was collected for a period of January 1990 to December 2016.The descriptive statistics revealed these variables to exhibit volatility as a characteristics of financial time –varying series. DCC Model was fitted, were the coefficients for all the parameters and that of the correlation-Targeting (rho_21) are both negative and positive and tend very close to 1 and -1, indicating that high persistence in the conditional variances. The Model DCC, satisfied the properties of a good model of conditional mean and variance of the confidential Interval (C.I) of 1 and -1, that is, the conditional variances are finites and their series are strictly stationary. This therefore implies that the Nigerian Stock Exchange, Exchange rate, share index and Inflation rate will experience a non-steady shock in the Stock market. However Each of these variables have different length of recovery (volatility half- life)  ranging from 1.5month, 6.5months, 6months to 2,4months   for stock exchange, exchange rate, share index and inflation rate respectively. By implication, the volatility of these variables had a long memory, persistence and mean-reverting.

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On Modeling the Volatility of Nigerian Stock Returns Using GARCH Models

Cited by 9 publications

References 31 publications

Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence

Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence

Modeling South African Stock Market Volatility Using Univariate Symmetric and Asymmetric Garch Models

Modeling Volatility of Nigeria Stock Exchange Using Multivariate GARCH Models

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