GARCH Models with the Heavy-Tailed Distributions and the Hong Kong Stock Market Returns

2017 ◽  
Author(s):  
Zi-Yi Guo
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Garch Models ◽  
Market Returns ◽  
Stock Market Returns ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Hong Kong Stock Market

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.

Impact of the Pandemic on Information and E-Commerce Stock Market Returns: The Case of Hong Kong

2021 ◽  
Author(s):  
Jiangsheng Zhao ◽  
Zhibin Xu ◽  
Jiansong Zheng ◽  
Binglin Tang ◽  
Yaoxi Jin
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Market Returns ◽  
Stock Market Returns

scholarly journals The relationship between mutual fund flows and stock market returns: A comparative empirical analysis

2010 ◽  
Vol 8 (1) ◽  
pp. 785-799
Author(s):  
B. Yangbo ◽  
Jayasinghe Wickramanayake ◽  
John R. Watson ◽  
Stan Tsigos
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Mutual Fund ◽  
Fund Flows ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Difference ◽  
Mutual Fund Flows ◽  
The Relationship ◽  
Equity Mutual Fund

This paper examines the relationship between aggregate equity mutual fund flows and excess stock market returns in Hong Kong and Singapore. Our findings demonstrate that, in Hong Kong, two-way causality exists between aggregate equity mutual fund flows and stock market returns. In comparison, despite their close proximity and reputation as global hubs no such finding is reported in the case of Singapore. We find that in Singapore, neither aggregate equity mutual fund flows Granger-cause subsequent excess stock market returns nor excess stock market returns Granger-cause subsequent aggregate equity mutual fund flows. The difference in findings is attributed to the degree of openness for each country. Additionally, for both Hong Kong and Singapore, we find that contemporaneous aggregate unexpected equity mutual fund flows positively affect excess stock market returns and vice versa. The study contributes to the literature by providing support with what is already known in regards investor heuristics, that excess stock market returns has a positive effect on aggregate equity mutual fund flows.

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