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ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 108-111 Analysis System for Correlative Fluctuation in Futures Market Xingyun Dai 1 , Weihui Dai 1 , Hua Ye 1 , and Genghui Dai 2 1 School of Management, Fudan University, Shanghai 200433, P.R.China Email: {082025041, whdai, 0525005}@fudan.edu.cn 2 School of Life Science, Zhongshan University, Guangzhou 510275, P.R.China Email: tony2009@vip.163.com Abstract—This paper studied the correlative fluctuation between two different futures markets, and developed an analysis system on .net platform to realize the prediction of price fluctuation. In this system, we explored the computing technology with mathematical programming language R, and integrated it with .net platform, thereof making the calculation precise and convenient. Test by this system has shown the accurate prediction of price fluctuation in the guided market.. Index Terms—futures market, correlative fluctuation, analysis system I. INTRODUCTION As a type of financial instrument, futures provide the buyers a way to purchase the underlying commodity for a specific price across time. There are two kinds of futures, commodity futures and financial futures. With the development of global market and information network, international futures markets have become integrated. Price fluctuation of one futures market may cause related price fluctuation in another exchange. Because each futures market has its own business time, study on the correlation among asynchronous futures markets is helpful for fluctuation prediction in the following market. Nowadays, a lot of studies have been concentrated in the price fluctuation pattern of a certain futures, the correlation between futures and spot price and the price of a certain futures in different markets. Western scholars are inclined to find the reasons for futures price fluctuation from the view of macroeconomics. Williams and Orice M [1] analyzed the relationship between energy market and energy future, Christos Floros and Dimitrios V.Vougas [2], Razak M A and Bacha O I [3] studied the interrelations between the stock index futures and stock markets. Moreover, W Daal et al [4] focused on the characteristic of futures itself which could be the reason for price fluctuation. Some scholars have been also interested in forecasting futures price fluctuation. Michael D. Mackenzie and Heather Mitchell [5], Hyndman and Cody B [6] studies the methods of futures price fluctuation forecasting by using This research was supported by National High-tech R & D Program (863 Program) of China (No.2008AA04Z127) and Shanghai Leading Academic Discipline Project (No.B210). Corresponding author: Weihui Dai. GARCH and Gauss model respectively. Meanwhile, Chinese scholars are more likely to use time series analysis methods to study the relationship among the futures markets, or between futures and spot price. Wang J [7] used time series and causal analysis methods to study the guiding role of futures price in different futures markets. Liu [8] and Deng et al [9] analyzed the price fluctuation of different financial futures. Besides, Ye [10] and Zhang [11] focused on the correlation of the metals futures price fluctuation between LME and SHFE, which is also our concern. Many of these researches have shown that there is high correlation between international and domestic futures price fluctuation. However, quantitative research and information system for analyzing and forecasting the futures price fluctuation have not been found in China. Our paper is aimed to analyze the price fluctuations of the same kind of futures in international and domestic futures markets, find out the correlation model and guiding role between them and finally develop an analysis system to realize the futures price fluctuation analysis and prediction in the guided market. II. CORRELATION MODEL A. Object As we want to analyze the correlation between the futures price in two different futures markets, the price data is essential. Only the price data in the same contract period will be chosen since a certain kind of futures with different contract periods have different price in the same futures bourse. Furthermore, mismatched data will be excluded. For example, the bourses in America are closed on Christmas just like the Spring Festival in China. Finally, all data must be converted to the same currency unit. B. Prediction Model A sequence of methods is taken to handle the price data and finally get the price prediction of the following futures market. After choosing futures price data as time series from different markets, stationary, co-integration and granger causality test are used to check the data validity. Stationary test is to confirm whether the data fulfills the conditions for co-integration test, which can tell there is real correlation among variables, or just spurious © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 108
egression. Granger causality test is useful to determine whether one time series can be used to forecast another. Here we can use Granger causality test to find the guiding market in the markets. Then use cross spectrum analysis to find the exactly relationship between futures price fluctuation, get the lag coefficient and make price prediction for the following market. Basic idea of Spectrum analysis and cross spectrum analysis is that time series are considered as the superposition of sine and cosine waves. Fluctuation pattern of time series can be revealed by comparing variances and circles of the waves. Given time series x and y, the autocovariance coefficients at lag k, cospectrum and quadrature spectrum (Q) of the cross spectrum are represented as follows. can be used to forecast the fluctuation of SHFE the next day. C. Arbitrage Compared with stock market, futures market is a better place for arbitrage. Firstly, also the biggest advantage, futures market supports two-way trade. Secondly, there is low transaction cost in futures market. Thirdly, using of leverage is the fascination of futures investment. Lastly, “T+0” transaction model is used in futures transaction. High speed trading is convenient because a futures contract can be bought and sold in one day. It is very possible for the speculators to realize arbitrage under these favorable conditions, along with the correlation among markets. Since we find the correlation between two futures markets, futures price fluctuation of the guided market can be predicted by using the price data of the guiding market. Hence, with price fluctuation data of the guiding market a unit of leading time before and the characteristic of futures market, arbitrage in the guide market is feasible. III ANANLYSIS SYSTEM A. Framework and Process Analysis system is used to analysis the correlation between any two price fluctuation time series and forecast the price trend of the guided market, based on the correlation model above. x¯ is mean of x, ȳ is mean of y, and . Amplitude (A), phase (H), coagulation spectrum (O), lag (L) of the cross spectrum are as follows. Figure 1. Flow chart of analysis system T j is cycle length of the cross spectrum. In the paper of the analysis of correlation relationship among international futures markets, Ye H. [11] applied the above methods and chose copper futures data as the study object. The results showed that cycle for the copper futures price fluctuation of SHFE and LME with 9.82 days has the highest correlation. The leading time of this cycle is -1.0164, i.e. the price fluctuation of LME comes 1.1064 days before SHFE. The result that the copper futures price fluctuation of LME plays a guiding role in the two markets is consistent with the outcome of Granger causality test. Hcopper price fluctuation of LME Fig. 1 shows the processes of the system. Firstly, check the price data from the markets the user concerns and then exclude the mismatched data. Secondly, use ADF and co-integrated tests to determine whether there is cointegration relationship between the price series. If there is, use spectrum analysis to find the leading time of the guiding market and Granger causality test to verify the result. Finally, provide investment suggestion to the user, according to the deviation analysis after verifying. B. Modules This system is composed of seven modules: data importing, sample selection, database management, knowledge base management, model base management, 109
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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ACKNOWLEDGMENT This work is funded
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Liping Chen .......................
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