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 Impact of Trading Volumes by Trader Types in the KOSPI200 Futures Market

2003 ◽  
Vol 11 (2) ◽  
pp. 1-26
Author(s):  
Chang Hyeon Yun ◽  
Lee Seong Gu
Keyword(s):  
Price Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Foreign Investors ◽  
Index Futures ◽  
Persistent Effect ◽  
Trading Volumes ◽  
The Impact

In this study we examine the relationships between trader-type-specific trading volumes and the price volatility of the KOSPI200 stock index futures over the period of July 1997 through December 2001. The principal findings of this study are that the changes in trading volumes by foreign investors are positively associated with the return and the volatility of the index futures market. When trading volumes are decomposed into expected and unexpected components, unexpected shocks have more persistent effect on the volatility of the market than expected component. Meanwhile, individuals and domestic commercial investor seem to follow the lead made by foreign investors.

scholarly journals Impact of the Adjustment of Maximum Order Volume on Pricing Efficiency of Stock Index Futures in China

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Liang Wang ◽  
Tingjia Xu ◽  
Longhao Qin ◽  
Xianyan Xiong
Keyword(s):  
Futures Market ◽  
Informed Trading ◽  
Stock Index ◽  
Stock Index Futures ◽  
Order Book ◽  
Maximum Order ◽  
Index Futures ◽  
Pricing Efficiency ◽  
Probability Of Informed Trading ◽  
The Impact

In April 2017, China Financial Futures Exchange adjusted the maximum order volume of single trading in stock index futures, and this paper conducts research on this event. Firstly, it analyzes the influence of the adjustment of maximum order volume on the characteristics of the limit order book with high-frequency data and the impact of ordering situation on the trading depth and volatility of each contract with panel data. Secondly, it takes high-frequency tick-by-tick data to explore the causal relationship between the ordering situation and the probability of informed trading and analyzes the impact of the event on the probability of informed trading. Finally, the dynamic factor analysis method is used to quantify the pricing efficiency based on the probability of informed trading and the characteristics of limit order book, and the influence of the event on the pricing efficiency of stock index futures market is discussed. The results show that the reduction of maximum order volume has different effects on dominant contracts and nondominant contracts of stock index futures. After the event, the overall trading volume of the market increased, where the trading volume of dominant contracts decreased and that of nondominant contracts increased. For dominant contracts, the depth, slope, and liquidity decrease, the spread increases, and the probability of informed trading decreases so that the pricing efficiency becomes worse, while the results of nondominant contracts are the opposite. For Chinese stock index futures market, the pricing efficiency is greatly reduced and the resource allocation capacity is weakened under the influence of the event. Therefore, the adjustment of maximum order volume is not conducive to the healthy development of the stock index futures market. It is suggested that the reduction of the maximum order volume is only implemented for nondominant contracts.

scholarly journals High-Frequency Trading and Its Impact on Exogenous Liquidity Risk of China’s Stock Index Futures Market before and after Trading Restrictions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
GuangWei Shi ◽  
Yun Chen
Keyword(s):  
High Frequency ◽  
Liquidity Risk ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Trading ◽  
Stock Index Futures ◽  
Trade Restriction ◽  
Index Futures ◽  
Restriction Policy ◽  
The Impact

Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.

MARKET IMPACT AND ORDER BOOK CHARACTERISTICS IN THE KOREAN FUTURES MARKET

2011 ◽  
Vol 22 (11) ◽  
pp. 1269-1279
Author(s):  
JUNGHOON LEE ◽  
JANGHYUK YOUN ◽  
WOOJIN CHANG
Keyword(s):  
Futures Market ◽  
Stock Index ◽  
Market Response ◽  
Stock Index Futures ◽  
Order Book ◽  
Market Impact ◽  
Limit Order ◽  
Index Futures ◽  
Market Order ◽  
The Impact

We have examined the order book characteristics and market impact on the Korean stock index futures market (KOSPI 200 index futures). The distribution of order volumes generally follows power-law distribution. The estimated exponents are 1.9 for market order, 2.5 for limit order, and 2.1 for cancel order. This result is different from the case of stocks where the exponent of market order is larger than that of limit order. The order likelihood is distinctively high in every 50's of order volume, which implies the behavioral characteristics of human preference on round-up numbers. The distributions of bid–ask spread and the best quotes volume provide the evidence of the liquidity of KOSPI 200 index futures market. We have obtained the concave relationship between market impact and transaction volume as well. Finally, the market response behavior is observed regarding various transaction sizes. The size of market response is estimated to be proportional to the size of transaction. Also, the larger the transaction size is, the longer it takes to recover the stability from the impact triggered by transaction.

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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