An Impact of the U.S. and the U.K. Return Volatility for the Hong Kong and the Japan's Stock Market Returns: A DCC and Bivariate AGARCH Model

2010 ◽  
Author(s):  
Horng Wann-Jyi ◽  
Hu Tien-Chung
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Return Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
The U.S

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

A GARCH Modelling of Volatility and M-GARCH Approach of Stock Market Linkages of North America

2018 ◽  
Vol 19 (6) ◽  
pp. 1538-1553 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda
Keyword(s):  
North America ◽  
Stock Market ◽  
Stock Markets ◽  
Market Integration ◽  
Return Volatility ◽  
Sample Period ◽  
Market Returns ◽  
Stock Market Returns ◽  
Current Period ◽  
Highly Sensitive

The present study attempts to capture the return volatility and the extent of dynamic conditional correlation between the stock markets of North America region. The data contain weekly stock market returns spanning from the second week of 1995 to the fourth week of June 2016. Using univariate ARCH and GARCH approaches, the study finds evidence of return volatility and its persistence within the region. Mexican stock market neither reacts intensely to immediate market fluctuations nor the part of the realized past volatility spill over to the current period, whereas the stock markets of Canada and USA experience high persistence of return volatility and Bermuda stock market returns are highly sensitive to the immediate market fluctuations. Using MGARCH-DCC, this article finds that emerging markets are less linked to the developed market in terms of return and that there also exists a weak co-movement between the stock markets. There is no evidence of market integration throughout the sample period. Correlations tend to spread out equally throughout the sample period, but the co-variances were found to be more volatile during 2008–2010. This article reveals that changes in co-movement are not due to a change in the correlations between markets but is simply due to volatility.

scholarly journals Testing the volatility spillover between crude oil price and the U.S. stock market returns

2019 ◽  
pp. 1221-1230 ◽  
Author(s):  
Mehmet Kondoz ◽  
Ilhan Bora ◽  
Dervis Kirikkaleli ◽  
Seyed Alireza Athari
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Oil Price ◽  
Volatility Spillover ◽  
Crude Oil Price ◽  
Market Returns ◽  
Stock Market Returns ◽  
The U.S

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.

scholarly journals Optimism in Financial Markets: Stock Market Returns and Investor Sentiments

2019 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Chiara Limongi Concetto ◽  
Francesco Ravazzolo
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Spillover Effect ◽  
Investor Sentiment ◽  
European Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
Investor Sentiments ◽  
The U.S

This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power on stock market returns. Concerning the European market instead, investigation provides weak results. Moreover, comparing the two markets, where investor sentiment of U.S. market tries to predict the European stock market returns, and vice versa, the analyses indicate a spillover effect from the U.S. to Europe.

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.

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