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.        

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 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.

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

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