The Effects of US Macroeconomic News Announcements on Borsa Istanbul Stock Index Futures Market: An Analysis Based on High Frequency Data

2017 ◽  
pp. 31
Author(s):  
İbrahim Gök ◽  
Sefa Topuz
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Macroeconomic News ◽  
Index Futures ◽  
News Announcements

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.

scholarly journals Study on Stock Index Futures’ Mean Reversion Effect and Arbitrage in China Based on High-Frequency Data

iBusiness ◽  
2012 ◽  
Vol 04 (01) ◽  
pp. 78-83 ◽  
Author(s):  
Wei Zhuo ◽  
Xiujuan Zhao ◽  
Zhou Zhou ◽  
Shouyang Wang
Keyword(s):  
High Frequency ◽  
Mean Reversion ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Index Futures

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.

Measuring the Toxicity of Order Flow using High Frequency Data: The Case of KOSPI200 Index Futures

2014 ◽  
Vol 22 (1) ◽  
pp. 117-139
Author(s):  
Ki Yool Ohk ◽  
Ming Wu
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Informed Trading ◽  
Market Volatility ◽  
High Frequency Data ◽  
Order Flow ◽  
Frequency Data ◽  
Conditional Probability Distribution ◽  
Index Futures ◽  
Probability Of Informed Trading

This study presents a new informed trading probability measure VPIN (Volume-Synchronized Probability of Informed Trading) to estimate toxic order flow of KOSPI200 index futures in a high frequency world. This measure does not require to estimate non-observable parameters as PIN. Also, it is estimated based on volume time, so it can estimate toxicity of order flow in a high frequency world. We show a relation between KOSPI200 index futures VPIN and futures market volatility using correlation and conditional probability distribution. A main empirical result is that persistently high VPIN signifies a high risk of subsequent large futures market volatility. It means that VPIN is a useful measure to estimate a toxicity induced volatility.

scholarly journals VPIN and the China’s Circuit-Breaker

2017 ◽  
Vol 9 (12) ◽  
pp. 126 ◽  
Author(s):  
Yameng Zheng
Keyword(s):  
High Frequency ◽  
Circuit Breaker ◽  
Futures Market ◽  
Informed Trading ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Trading Market ◽  
Probability Of Informed Trading

The market microstructure theory and high-frequency trading analyze as quantitative investment’s frontier and hot issue is popular in China in recent years, but China’s stock index futures introduced later, so there are not much academic attention. This paper measures the probability of informed trading in China’s stock index futures market by VPIN method. The empirical results show that the VPIN can not only monitor the probability of the informed trading market of IF 300, IH 50 and IC 500, but also play an early warning role before the “circuit-breaker”. Tracking VPIN values allows the liquidity providers to control their position risk, and regulators can monitor the liquidity quality of the market, limit transactions in advance or tighten market controls.

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.

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