Transaction Costs and Trading Activity in the Index Futures Market: The Case of the Transaction Tax Reduction in Taiwan

2003 ◽  
Author(s):  
Huimin Chung ◽  
Mei-Ying Liu ◽  
Soushan Wu ◽  
Fu-Ju Yang
Keyword(s):  
Transaction Costs ◽  
Futures Market ◽  
Trading Activity ◽  
Index Futures ◽  
Tax Reduction ◽  
Transaction Tax

scholarly journals The Impact of Underlying Market Closure on Futures Market Liquidity: Evidence from China

2012 ◽  
Author(s):  
Wang Chun Wei ◽  
Alex Frino
Keyword(s):  
Futures Market ◽  
Market Liquidity ◽  
Stock Index ◽  
Stock Index Futures ◽  
Contagion Effect ◽  
Index Futures ◽  
Empirical Results ◽  
Open Session ◽  
The Impact ◽  
Closure Theory

This study investigates the trading activity of Chinese stock index futures, recently introduced at the open and close of the underlying trading. We document the impact of the underlying spot on the futures market liquidity as well as volatility as discussed in earlier works on market closure theory. Our empirical results support previous literature on the impact of the underlying, particularly during the open session, as a contagion effect, which is clearly at play. We find significant U-shaped patterns in liquidity factors and intraday volatility during open and close trades in the morning.  

The efficiency of the U.S. dollar index futures market

1990 ◽  
Vol 10 (5) ◽  
pp. 469-479 ◽  
Author(s):  
Giora Harpaz ◽  
Steven Krull ◽  
Joseph Yagil
Keyword(s):  
Futures Market ◽  
Index Futures ◽  
The U.S

The nonlinear characteristics of Chinese stock index futures yield volatility

2019 ◽  
Vol 10 (2) ◽  
pp. 175-196 ◽  
Author(s):  
Xuebiao Wang ◽  
Xi Wang ◽  
Bo Li ◽  
Zhiqi Bai
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Short Term ◽  
Content Type ◽  
Index Futures ◽  
Basic Characteristics

Purpose The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory. Design/methodology/approach This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry. Findings This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased. Research limitations/implications This paper has research limitations in variable measurement and data selection. Practical implications This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration. Originality/value The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

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