Modeling Volatility of Nigeria Stock Market Returns using Garch models An Ranking Method

2018 ◽  
Vol 5 (1) ◽  
pp. 13-27
Author(s):  
U. Usman ◽  
Y. Musa ◽  
H. M. Auwal
Keyword(s):  
Stock Market ◽  
Ranking Method ◽  
Garch Models ◽  
Market Returns ◽  
Stock Market Returns ◽  
Modeling Volatility

scholarly journals GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

2017 ◽  
Vol 12 (9) ◽  
pp. 28 ◽  
Author(s):  
Zi-Yi Guo
Keyword(s):  
Risk Management ◽  
Hong Kong ◽  
Stock Market ◽  
Gaussian Distribution ◽  
Inverse Gaussian Distribution ◽  
Garch Models ◽  
Inverse Gaussian ◽  
Market Returns ◽  
Stock Market Returns ◽  
Hong Kong Stock Market

As one of the world’s largest securities markets, the Hong Kong stock market plays a significant role in facilitating the development of Chinese economy. In this paper, we investigate a suite of widely-used models, the GARCH models in risk management of the Hong Kong stock market returns. To account for conditional volatilities, we consider a new type of fat-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), and compare its empirical performance with two other popular types of fat-tailed distribution, the Student’s t distribution and the normal inverse Gaussian distribution (NIG). We show that the NRIG distribution performs slightly better than the other two types of distribution. Also, our results indicate that it is important to introduce both GJR-terms and the NRIG distribution to improve the models’ performance. Our results illustrate that the asymmetric GARCH NRIG model has practical advantages in quantitative risk management, and serves as a very useful tool for industry participants.

scholarly journals Structural Breaks and Volatility Persistence of Stock Returns: Evidence from the US and UK Equity Markets

2018 ◽  
Vol 5 (6) ◽  
pp. 76
Author(s):  
Chikashi Tsuji
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Structural Breaks ◽  
Garch Models ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Persistence ◽  
The Us ◽  
Parameter Values

This paper quantitatively investigates the effects of structural breaks on stock return volatility persistence by using the US and UK stock market index return data. Applying two kinds of representative univariate GARCH models of standard GARCH and EGARCH models, we derive the following interesting findings. (1) First, we find that for both the US and UK stock market returns, the volatility persistence parameter values of standard GARCH models decrease when structural breaks are taken into account. (2) Second, we further reveal that for both the US and UK stock market returns, the volatility persistence parameter values of EGARCH models again decline when structural breaks are taken into consideration.

scholarly journals Modeling Stock Market Monthly Returns Volatility Using GARCH Models Under Different Distributions

2020 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Rama Krishna Yelamanchili
Keyword(s):  
Stock Market ◽  
Gaussian Distribution ◽  
Garch Model ◽  
Information Criterion ◽  
Conditional Variance ◽  
Garch Models ◽  
Leverage Effect ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering

This papers aims to uncover stylized facts of monthly stock market returns and identify adequate GARCH model with appropriate distribution density that captures conditional variance in monthly stock market returns. We obtain monthly close values of Bombay Stock Exchange’s (BSE) Sensex over the period January 1991 to December 2019 (348 monthly observations). To model the conditional variance, volatility clustering, asymmetry, and leverage effect we apply four conventional GARCH models under three different distribution densities. We use two information criterions to choose best fit model. Results reveal positive Skewness, weaker excess kurtosis, no autocorrelations in relative returns and log returns. On the other side presence of autocorrelation in squared log returns indicates volatility clustering. All the four GARCH models have better information criterion values under Gaussian distribution compared to t-distribution and Generalized Error Distribution. Furthermore, results indicate that conventional GARCH model is adequate to measure the conditional volatility. GJR-GARCH model under Gaussian distribution exhibit leverage effect but statistically not significant at any standard significance levels. Other asymmetric models do not exhibit leverage effect. Among the 12 models modeled in present paper, GARCH model has superior information criterion values, log likelihood value, and lowest standard error values for all the coefficients in the model.        

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