Research on the Relative Trading Volume by Remaining period and Investor Types in the KOSPI200 Index Futures market.

2020 ◽  
Vol 17 (5) ◽  
pp. 68-87
Author(s):  
Bong Soo Kim
Keyword(s):  
Trading Volume ◽  
Futures Market ◽  
Index Futures

Modelling Regulatory Change V's Volume, of Trading Effects in HSIF and HSI Volatility

2008 ◽  
Vol 11 (01) ◽  
pp. 47-59 ◽  
Author(s):  
Gerard Gannon ◽  
Siu Pang Au-Yeung
Keyword(s):  
Trading Volume ◽  
Structural Breaks ◽  
Structural Changes ◽  
Systematic Approach ◽  
Futures Market ◽  
Likelihood Ratio Tests ◽  
Market Volatility ◽  
Regulatory Change ◽  
Index Futures ◽  
Dummy Variables

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Volatility behaviour of stock index futures in China: a bivariate GARCH approach

2015 ◽  
Vol 32 (1) ◽  
pp. 128-154 ◽  
Author(s):  
Yang Hou ◽  
Steven Li
Keyword(s):  
Trading Volume ◽  
Futures Market ◽  
Stock Index ◽  
Garch Models ◽  
Stock Index Futures ◽  
Informational Efficiency ◽  
Conditional Correlation ◽  
Content Type ◽  
Index Futures ◽  
Volatility Transmission

Purpose – This paper aims to investigate the volatility transmission and dynamics in China Securities Index (CSI) 300 index futures market. Design/methodology/approach – This paper applies the bivariate Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models using high frequency data. Estimates for the bivariate GARCH models are obtained by maximising the log-likelihood of the probability density function of a conditional Student’s t distribution. Findings – This empirical analysis yields a few interesting results: there is a one-way feedback of volatility transmission from the CSI 300 index futures to spot returns, suggesting index futures market leads the spot market; volatility response to past bad news is asymmetric for both markets; volatility can be intensified by the disequilibrium between spot and futures prices; and trading volume has significant impact on volatility for both markets. These results reveal new evidence on the informational efficiency of the CSI 300 index futures market compared to earlier studies. Originality/value – This paper shows that the CSI 300 index futures market has improved in terms of price discovery one year after its existence compared to its early days. This is an important finding for market participants and regulators. Further, this study considers the volatility response to news, market disequilibrium and trading volume. The findings are thus useful for financial risk management.

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.  

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