Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy

2017 ◽  
Vol 471 ◽  
pp. 169-180 ◽  
Author(s):  
Junjun Ma ◽  
Xiong Xiong ◽  
Feng He ◽  
Wei Zhang
Keyword(s):  
Stock Market ◽  
Investment Strategy ◽  
Chinese Stock Market ◽  
Statistical Property ◽  
Directional Change

scholarly journals Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402199065
Author(s):  
Wei Huang ◽  
Meng-Shiuh Chang
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Investment Strategy ◽  
Chinese Stock Market ◽  
Safe Haven ◽  
Copula Models ◽  
Government Bonds ◽  
Market Turbulence ◽  
Vine Copula

We examine whether gold and China’s government bonds are safe-haven assets against the turbulence of the Shanghai Stock Exchange Composite Index by employing vine copula models during the 2003 to 2015 period. We find that either bonds or gold can be a weak safe haven but only gold can be a strong safe haven. Our simultaneous analysis advises against a joint safe-haven strategy of gold and bonds, given the high- to low-tail correlation. This result highlights an investment strategy of using a single safe-haven asset against the Chinese stock market turbulences.

Who is King in Factor Zoo? Case of the Chinese Stock Market

2020 ◽  
Vol 14 (2) ◽  
pp. 77-102
Author(s):  
Simon M. S. So
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Returns ◽  
Factor Model ◽  
Five Factor Model ◽  
Investment Strategy ◽  
Capital Asset Pricing ◽  
Chinese Stock Market ◽  
Fama And French ◽  
Three Factor

This paper aimed to evaluate and compare individual performances and contributions of seven well-known factors, selected from four widely cited asset pricing models: (1) the capital asset pricing model of Sharpe (1964), (2) the three-factor model of Fama and French (1993) the augmented four-factor model of Carhart (1997), (3) the five-factor model of Fama and French (2015), and (4) the illiquidity model of Amihud, et al. (2015) in capturing the time-series variation of stock returns and absorbing the 12 prominent anomalies. The anomalies were constructed by forming long-short portfolios, and regressions were run to examine their monthly returns from 2000 to 2019. We found that there is no definite and absolute “king” in the factor zoo in the Chinese stock market, and size is the relative “king” that can absorb the maximum number of anomalies. Evidence also indicates that the three-factor model of Fama and French may still play an important role in pricing assets in the Chinese stock market. The results can provide investors with a reliable risk factor and help investors form an effective investment strategy. This paper contributes to asset pricing literature in the Chinese market.G1

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