scholarly journals Shock Transfer in Futures and Spot Markets: An Agent-Based Simulation Modelling Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xuan Zhou ◽  
Menggang Li
Keyword(s):  
Simulation Modelling ◽  
Futures Market ◽  
Stock Index ◽  
Market Model ◽  
Stock Index Futures ◽  
Risky Assets ◽  
Index Futures ◽  
Market Interaction ◽  
Futures Contract ◽  
Arbitrage Trading

There have been heated debates about the role of stock index futures in the financial market, especially during the crash periods. In this paper, a multiagent spot-futures market model is developed to analyze the micromechanism of shock transfer across spot and futures markets. We assume that there are two stocks and one stock index futures contract in the spot-futures market. Agents are heterogeneous, including fundamentalists, chartists, noise traders, and arbitragers. The spot market and the futures market are linked by arbitragers. The simulation results show that our spot-futures market model can reproduce various important stylized facts, including the price co-movement between stock index prices and index futures prices and the fat-tailed distribution of the returns of risky assets and the basis. Further analysis shows that when we introduce an exogenous fundamental shock to one of the stocks, the backwardation phenomenon appears in the futures market and the shock is widespread across the whole market by means of index futures. Moreover, the backwardation gradually disappears when the number of arbitragers increases. Besides, when there are few arbitragers or when there are sufficient arbitragers, shocks cannot be transferred to other stocks via the futures market, while an intermediate level of arbitrage will amplify the shock transfer and hurt market stability. These findings underscore that arbitragers play an important role in spot-futures market interaction and shock transfer, and adequate arbitrage trading during crises may help eliminate the positive basis and halt the further spread of the crises.

scholarly journals Study on Informational Transaction and Its Effect on China's Stock Index Futures Market

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Hongli Che ◽  
Xiong Xiong ◽  
Jiatong Han ◽  
Wei Zhang ◽  
Yongjie Zhang
Keyword(s):  
Futures Market ◽  
Informed Trading ◽  
Stock Index ◽  
Stock Index Futures ◽  
Risky Assets ◽  
Index Futures ◽  
The Future ◽  
Probability Of Informed Trading

Information is one of the important factors that influence the behavior of investors and then have an effect on the price of the risky assets in the market. Firstly, the new procedure developed by Easley et al. (2011) is used to estimate the Volume-Synchronized Probability of Informed Trading (VPIN) of the Chinese stock index futures market. Then VPIN for special scenarios is depicted. As a result, we find that the future contracts generally have a larger number of information transactions. We also find that, for particular scenarios, the probability of informed trading in the market has obvious exceptions. The larger proportion of informed trader is, the higher the volatility of the price is.

Price Clustering in the Nikkei 225 Stock Index Futures Contract on the SIMEX: An Intraday Empirical Analysis

2000 ◽  
Vol 03 (04) ◽  
pp. 519-533 ◽  
Author(s):  
Horace Chueh
Keyword(s):  
Financial Markets ◽  
Empirical Analysis ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Futures Contract ◽  
High Volatility ◽  
Price Clustering ◽  
Bid Ask Spreads

Price clustering in financial markets has been identified by previous studies. However, few studies have examined the phenomenon in the futures market. This paper presents price clustering for the Nikkei 225 stock index futures contract on the SIMEX. An extremely low percentage of odd-tick trades appears at the opening for the first trading session, while moderately low percentage occurs at the opening and the closing for the second trading session. GARCH estimation results document that the degree of price clustering increases in the periods with high volatility, bid-ask spreads, and transaction frequency. Price clustering tends to occur on the last trading day which the futures contract is to be presented. Generally, the results support the negotiation hypothesis of price clustering proposed by Harris (1991).

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 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.

scholarly journals An Agent-Based Computational Model for China’s Stock Market and Stock Index Futures Market

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hai-Chuan Xu ◽  
Wei Zhang ◽  
Xiong Xiong ◽  
Wei-Xing Zhou
Keyword(s):  
Market Mechanism ◽  
Stock Index ◽  
Market Model ◽  
Stock Index Futures ◽  
Volatility Clustering ◽  
Agent Based ◽  
Index Futures ◽  
Bid Ask Spread ◽  
Trading Mechanism ◽  
Index Future

This study presents an agent-based computational cross market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index future to simulate this structure. This model allows heterogeneous investors to make investment decisions with restrictions including wealth, market trading mechanism, and risk management. Investors’ demands and order submissions are endogenously determined. Our model successfully reproduces several key features of the Chinese financial markets including spot-futures basis distribution, bid-ask spread distribution, volatility clustering, and long memory in absolute returns. Our model can be applied in cross market risk control, market mechanism design, and arbitrage strategies analysis.

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