The Liquidity and Volatility Impacts of Day Trading by Individuals in the Taiwan Index Futures Market

2012 ◽  
Author(s):  
Robin K. Chou ◽  
George H. K. Wang ◽  
Yun-Yi Wang
Keyword(s):  
Futures Market ◽  
Index Futures ◽  
Day Trading

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

Day-trading of Nikkei 225 Index Futures based on Chaos Theory

2007 ◽  
pp. 1161-1170
Author(s):  
Tadashi Iokibe ◽  
Takashi Kimura ◽  
Yasunari Fujimoto ◽  
Yasuyuki Kuratsu
Keyword(s):  
Chaos Theory ◽  
Index Futures ◽  
Day Trading

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.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conghua Wen ◽  
Fei Jia ◽  
Jianli Hao
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Trading Behavior ◽  
Volatility Forecasting ◽  
Content Type ◽  
Index Futures ◽  
Intraday Data

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

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