China Financial Futures Exchange Research Articles

Effects of Futures Trading Constraints on Volatility of Stock Market

Financial regulations and policies affect the financial market efficiency. Due to China's stock market experienced a serious slump from June to August in 2015, China Financial Futures Exchange (CFFEX) subsequently implements constraints on trading futures aiming to limit the speculation in futures market. To explore the impact of trading constraints on index futures on the volatility of stock market and focuses on the change in CSI300 volatility before and after restricting futures from 2015 to 2016. The trading constraints on futures help decrease the stock market’s price fluctuation and effectively calm down the stock market. The results suggest that the proper trading constraints may be necessary while the market is irrational and the findings provide insights for the investors and regulators.

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

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.

Discovering the Trading Pattern of Financial Market Participants: Comparison of Two Co-Clustering Methods

Co-clustering is rapidly becoming a powerful data analysis technique in varied fields, such as gene expression analysis, data and web mining, and market baskets analysis. In this paper, two co-clustering methods based on smooth plaid model (SPM) and parallel factor decomposition with sparse latent factors (SLF-PARAFAC) are respectively applied to synthetic data set and investors’ transaction-level data set from the China Financial Futures Exchange. We present the comparison between two methodologies. Both SLF-PARAFAC and SPM are efficient, robust, and well suited for discovering trading ecosystems in modern financial markets. We recognize temporal pattern differences of various trader types. The results help to develop a thorough understanding of trading behaviors, and to detect patterns and irregularities.

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP) was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT) method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI) 300 futures in the emerging China Financial Futures Exchange (CFFEX) market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.

Research on the Price of Stock Index Futures with ARIMA Model

On April16, 2010 Chinese first stock index futures listed in China Financial Futures Exchange Traded. It means that, Chinese financial markets ushered in the era of short. Stock index futures have become the focus of the vast majority investors. This paper focus on the price fluctuation of stock index futures since it appears on the market. Through the collection of the CSI 300 index futures closing prices, with data processing and analysis to establishment of ARIMA model. According to the model, the price of stock index futures is forecasted, and then simulating trading based on the predicted results, Simulation results show the accuracy of ARIMA model prediction. The results provide a reference for investors to provide a reference for investment activities.

(The Estimation of Chinese Term Structure of Interest Rate and Treasury Bonds Pricing)

Chinese Abstract: 基于上交所国债数据，沿袭Bliss（1997）的研究方法，并加以改进和扩展，本文考察了五种静态利率期限结构模型在样本内拟合和样本外预测上的表现，与Bliss（1997）的结果不同，我们发现三次平滑样条方法更适合用来估计中国上交所国债的利率期限结构。在此基础上，通过对各模型的拟合误差和预测误差的分析，我们发现，利率期限结构并不是决定国债定价的唯一系统性因素，上交所国债的定价还需考虑国债的其他特征因素，投资者在交易国债时会将息票金额的大小和债券价格的溢价程度考虑到定价中来,这一点与Bliss（1997）的结论相同。作为本文结论的一个应用以及对Bliss（1997）方法的扩展，我们发现息票金额低的投资组合和溢价因子高的投资组合分别高于息票金额高的投资组合和溢价因子低的投资组合，本文的结论对国债投资有一定的指导意义。English Abstract: The term structure of interest rate is a concept central to economic and financial theory and the pricing of interest rate contingent claims. An estimate of the term structure of interest rate is the necessary starting point for applying them to pricing and hedging interest rate derivatives. Since September 06, 2013 when treasury bond futures was launched by China Financial Futures Exchange, it had become more important to learn about the relation between the estimate of the term structure and the treasury bond pricing which is crucial to the pricing of treasury bond futures. To study their relationship, using Treasury bonds data from Shanghai Stock Exchange and methods proposed by Bliss (1997), this paper first compares five distinct models for estimating the term structure in China. Combining in-sample performance and out-of-sample performance, we find that the smoothed cubic spline method does best overall, which is different from Bliss (1997) who studied the USA treasury data.However, is the term structure of interest rate the only factor that determines the pricing of treasury bonds? To exploit this question, we examine the behavior of fitted-price errors of individual bonds from period to the next and across different models. Specifically, we have done three following studies:In the first place, we test the transition matrix for fitted-price errors classified as positive, zero, or negative. If the errors are indeed noise, the transition probabilities should reflect the frequency of positive, zero, and negative errors in the overall population. In this test, we find that the fitted-price errors for specific bonds are correlated through time.In the second place, we test the coincidence matrices of positive, zero, and negative errors across estimation models, that is, we examine the probability that a positive (zero, or negative) fitted-price error produced by one model will also have a positive (zero, or negative) fitted-price error under the other model. In this test, we find that the fitted-price errors are related across models.In the third place, we regress the fitted-price errors against variables that may be relevant to the bond pricing process. In this test, we identify coupon rates and Price Premium might be relevant to the pricing of Treasury bonds.All the three tests have the same results with Bliss (1997), meaning that the term structure of interest rate is not the only pricing element in the price of treasury bond market in China.Finally, as an application of our finding and an extension to the Bliss (1997), we exploit its implication for bond portfolio investment. We find that small-coupon portfolio and high-price-premium portfolio have better return than large-coupon portfolio and low-price-premium portfolio respectively.

The Development and Challenges of China’s Futures Markets

The futures market in China started in the early 1990s as the economic reform deepened. After two decades of development, there are now four futures exchanges: Shanghai Futures Exchange, Dalian Commodity Exchange, Zhengzhou Commodity Exchange, and China Financial Futures Exchange. Product innovations and regulatory changes largely contribute to the rapid expansion of the market. Several futures contracts are now among the most active contracts in the world market. With development of its futures market and efforts to open up the market to investors overseas, China’s pricing power in the global commodity market has improved over time.

A Tale of Two Index Futures: The Intraday Price Discovery and Volatility Transmission Processes between the China Financial Futures Exchange and the Singapore Exchange

This is the first study to examine the intraday price discovery and volatility transmission processes between the Singapore Exchange and the China Financial Futures Exchange. Using one- and five-minute high-frequency data from May to November 2011, we found that China’s CSI 300 index futures dominated Singapore’s A50 index futures in terms of the price discovery process. However, A50 futures contracts also made a substantial contribution (26%-37%) to the price discovery process. The CSI 300 futures market also dominates its Singapore counterpart in the intraday volatility transmission process. These results have important implications for both traders and policymakers.

Price–volume cross-correlation analysis of CSI300 index futures

We investigate the cross-correlation between price returns and trading volumes for the China Securities Index 300 (CSI300) index futures, which are the only stock index futures traded on the China Financial Futures Exchange (CFFEX). The basic statistics suggest that distributions of these two time series are not normal but exhibit fat tails. Based on the detrended cross-correlation analysis (DCCA), we obtain that returns and trading volumes are long-range cross-correlated. The existence of multifractality in the cross-correlation between returns and trading volumes has been proven with the multifractal detrended cross-correlation analysis (MFDCCA) algorithm. The multifractal analysis also confirms that returns and trading volumes have different degrees of multifractality. We further perform a cross-correlation statistic to verify whether the cross-correlation significantly exists between returns and trading volumes for CSI300 index futures. In addition, results of the test for lead-lag effect demonstrate that contemporaneous cross-correlation of return and trading volume series is stronger than cross-correlations of leaded or lagged series.