Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100

Aliyev, Fuzuli; Ajayi, Richard A.; Gasim, Nijat

doi:10.1016/j.jeca.2020.e00167

Cited by 35 publications

(31 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using univariate asymmetric GARCH models, Aliyev et al (2020) modelled and estimated the volatility of the Nasdaq-100 and found persistent volatility shocks on index returns, a leveraging effect on the index and asymmetric impact of shocks. Zivkov et al (2021) evaluated the multiscale bidirectional volatility spillover effect between national stocks and exchange rate markets among four African countries using the MS-GARCH model.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Wang

Xiang

Lei

et al. 2021

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

Volatility is integral for the financial market. As an emerging market, the Chinese stock market is acutely volatile. In this study, the data of the Shanghai Composite Index and Shenzhen Component Index returns were selected to conduct an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. We established the autoregressive moving average (ARMA)-GARCH model with t-distribution for the sample series to compare model effects under different distributions and orders. In contrast, we proposed threshold-GARCH (TGARCH) and exponential-GARCH (EGARCH) models to capture the features of the index. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE) and rootmean-squared error (RMSE). The results denote that the ARMA (4,4)-GARCH (1,1) model under Student's t-distribution outperforms other models when forecasting the Shanghai Composite Index return series. For the return series of the Shenzhen Component Index, ARMA(1,1)-TGARCH(1,1) display the best forecasting performance among all models. This study could provide an effective information reference for the macro decision-making of the government, the operation of listed companies and investors' investment decision-making.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Wang

Xiang

Lei

et al. 2021

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

show abstract

“…Thus, how to accurately predict the volatility of an asset is a very important issue in the actual investment process in the financial field. As to the issue of volatility forecasting, most of literatures used the generalized autoregressive conditional heteroskedasticity (GARCH) family models, a parametric volatility forecasting approach, to predict the volatility of an asset [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Because this type of model can capture most common features of financial assets such as both the linear dependence and strong autoregressive conditional heteroskedasticity (ARCH) effect subsisting on the return series, and both the volatility clustering and leverage effect usually existing at the volatility of financial asset returns series [ 8 , 20 , 21 , 22 , 23 ] (Volatility clustering means that large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding past literatures about volatility forecasting, they tried to use a more flexible model or a complex approach to increase the performance of volatility forecasting [ 15 , 16 , 17 , 19 ]. For example, Aliyev et al [ 12 ] used the GARCH, exponential GARCH (EGARCH) and the GJR-GARCH model of Glosten, Jagannathan and Runkle [ 24 ] models with normal distribution (i.e., the GARCH-N, EGARCH-N and GJR-GARCH-N) to estimate the volatility of the Nasdaq-100. Chun et al [ 14 ] used the above three models, the GARCH-N, EGARCH-N and GJR-GARCH-N, to forecast the volatility in the KOSPI200 in the Korean market.…”

Section: Introductionmentioning

confidence: 99%

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

2021

Entropy

View full text Add to dashboard Cite

This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote the performance of volatility forecasting, and which one of the sixteen models possesses the best volatility forecasting performance. The eight parametric volatility forecasts models are composed of the generalized autoregressive conditional heteroskedasticity (GARCH) or GJR-GARCH volatility specification combining with the normal, Student’s t, skewed Student’s t, and generalized skewed Student’s t distributions. Empirical results show that, the performance for the composed volatility forecasting approach is significantly superior to that for the parametric volatility forecasting approach. Furthermore, the GJR-GARCH volatility specification has better performance than the GARCH one. In addition, the non-normal distribution does not have better forecasting performance than the normal distribution. In addition, the GJR-GARCH model combined with both the normal distribution and a neural network approach has the best performance of volatility forecasting among sixteen models. Thus, a neural network approach significantly promotes the performance of volatility forecasting. On the other hand, the setting of leverage effect can encourage the performance of volatility forecasting whereas the setting of non-normal distribution cannot.

show abstract

“…A plethora of literature has studied volatility in some time series variable such as crude oil price (Suleiman et al, 2015Alhassan & Kilishi, 2016Muhammed & Faruk, 2018;Zhang et al, 2019;Nguyen & Walther, 2020;;Boitumelo et al, 2020), exchange rate (Kuhe & Agaigbe, 2018;Abdullah et al, 2017;Okoro & Osisiogu, 2017;Dritsaki, 2019), stock prices (Al Rahahleh & Kao, 2018;Iwada et al, 2018;Lin, 2018;Aliyev et al, 2020), inflation (Fwaga et al, 2017;Nyoni, 2018;Iddrisu et al, 2019) amongst others. Modeling PVCO by some authors has reported varying outcomes over the years.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations

Ojirobe

Ahmad

David

2021

JOSMA

View full text Add to dashboard Cite

Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student’s t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.

show abstract

Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100

Cited by 35 publications

References 34 publications

Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations

Contact Info

Product

Resources

About