Heavy-Tailed Distributions, GARCH Model and the Stock Market Returns in South Korea

2017 ◽  
Author(s):  
Yoon Hong ◽  
Ji-chul Lee ◽  
Ding Guoping
Keyword(s):  
Stock Market ◽  
South Korea ◽  
Garch Model ◽  
Market Returns ◽  
Stock Market Returns ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed

scholarly journals A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

2017 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
John Oden ◽  
Kevin Hurt ◽  
Susan Gentry
Keyword(s):  
Stock Market ◽  
Global Economy ◽  
Heavy Tails ◽  
Financial Sector ◽  
Superior Performance ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering ◽  
Empirical Performance ◽  
Heavy Tailed

As the fourth largest economy over the world, Germany’s financial sector plays a key role in the global economy. As one of the most important components of the financial sector, the equity market played a more and more important role. Thus, risk management of its stock market is crucial for welfare of its market participants. To account for the two stylized facts, volatility clustering and conditional heavy tails, we take advantage of the framework in Guo (2016) and consider empirical performance of the GARCH model with normal reciprocal inverse Gaussian distribution in fitting the German stock return series. Our results indicate the NRIG distribution has superior performance in fitting the stock market returns.

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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