An Empirical Analysis On The Impact Of RMB Exchange Rate .
Advances in Economics, Business and Management Research, volume 70International Conference on Economy, Management and Entrepreneurship（ICOEME 2018)An Empirical Analysis on the Impact of RMBExchange Rate Fluctuation on Stock Index FuturesTaking Shanghai and Shenzhen 300 Stock Indexes as an ExampleJiehui LiJinlan HeCollege of FinanceFujian Jiangxia UniversityFuzhou, China 350108Shenzhen Houhe Travel Technology Co., Ltd.Shenzhen, China 518000Abstract—Using the measurement method to study thefluctuation of RMB exchange rate will have a series ofprofound effects on stock index futures. This paper will takethe Shanghai and Shenzhen 300 stock index futures as theexample. In this paper, the author will establish a threevariable VAR model: US dollar, Euro, and Japanese yenexchange rate against RMB to analyze the impact of RMBexchange rate fluctuations on stock index futures. Theempirical test shows that after the "811" exchange rate reform,there is no long-term stable cointegration relationship betweenRMB exchange rate changes and stock index futures.According to the results of impulse response function analysis,there is linkage between China's exchange rate fluctuationsand Shanghai and Shenzhen 300 stock index futures price.From the current point of view, the impact is still very limited.economic context. Is there a dynamic link between the two?Will the interaction between stock index futures and RMBexchange rate fluctuations affect each other? For the Chinesemarket that is developing so rapidly today, this issue isworthy of further discussion. To this end, this paper takesShanghai and Shenzhen 300 stock index futures as anexample, and empirically analyzes whether the fluctuation ofthe RMB exchange rate is related to the change of theShanghai and Shenzhen 300 stock index futures through theVAR model. First, it explores the potential constraints,analyzes the results from the empirical test and proposesrelevant countermeasures.Keywords—VAR model; stock index futures; cointegrationrelationshipI.INTRODUCTIONIn 2015, the stock market fluctuated sharply. This was anightmare for the A-share market. When the market wasshocked, the "811 Exchange Reform" came unexpectedly,which directly led to a sharp drop in the RMB exchange rateby nearly 2% in a short period of time. In the three days ofthe "811 Exchange Reform", the depreciation of the RMBexceeded 3%, and the value of RMB depreciated by 1136basis points. This measure has greatly exceeded marketexpectations. In this process, the RMB exchange ratedepreciation process has great impact on the expectation ofRMB depreciation, which makes the foreign exchangereserves shrink sharply, and the price of the futures markethas changed drastically. The parties in this society pay moreattention to the research in the field of financial futures, suchas stock index futures trading strategy, hedging schemedesign, product development model, and risk controlmanagement experience. However, from the relevantliteratures that have been searched, scholars have studiedmore about the mutual influence between the foreignexchange market and the stock market. The research andaccumulation of the foreign exchange market and the futuresmarket is not deep enough. It is worth exploring. There arestill many problems to be studied. The domestic futuresmarket and the foreign exchange market are in the sameII.LITERATURE REVIEWDomestic research on the correlation of exchange ratechanges on stock index futures started relatively late. Sincethe beginning of this issue, the research perspective ofdomestic scholars has been deepening and changing. At thebeginning, some scholars studied the basic theory that theimpact of exchange rate changes on stock prices. Mostresearchers in China have studied this problem. A smallnumber of researchers have come to the conclusion that theRMB exchange rate has positive impact on stock prices.Most researchers have different conclusions.The article of Ba Shusong (2009) and Yan Min (2009)uses the information transmission model (VAR-MEGARCH)to make the analysis. In the long run, the appreciation ofRMB caused the stock price to rise to a certain extent.Negative correlations reflect the true relationship betweenthe two in this period of time. In recent years, there also havearticles on the correlation between China's exchange ratechanges and various indices. Dong Liegang (2015) studiedthe relationship between the Chinese commodity index andthe exchange rate based on mature foreign markets. In thispaper, it first analyzes the price transmission mechanism ofexchange rate fluctuations from a price perspective, andanalyzes the impact of commodity futures prices onexchange rates from the perspectives of capital account,inflation and current account. Yang Liu (2017) uses VARmodel to analyze the impact of RMB exchange rate changeson China's second-tier market index, and divides the datainto two parts based on time for empirical analysis. It isCopyright 2018, the Authors. Published by Atlantis Press.This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).1
Advances in Economics, Business and Management Research, volume 70concluded that the long-term impact of RMB exchange ratefluctuations on the GEM index is significant. Foreignscholars have studied the impact of exchange rate on stockindex futures. Almost everyone uses relevant data fromdeveloped countries. China’s exchange rate system has beenlinked to single dollar until July 21, 2005. The stock marketand the foreign exchange market are isolated. China's stockindex futures are listed late. The research scholars in Chinahave begun to study the relationship between exchange rateand stock index futures.In summary, whether in foreign countries or at home, thetheoretical research on the impact of exchange rate on stockindex futures is gradually deepening. The related systemsand complete theories have not been formed. In general,there is certain correlation between RMB exchange rate andstock index futures. With the accelerated process of China'sfinancial market reform and the gradual opening of capitalaccounts and financial accounts, the correlation betweenRMB exchange rate and stock index futures volatility isgetting closer. At the same time, the fluctuations in the RMBexchange rate and the stock price index, and the furtherdeepening of the marketization process of the foreignexchange market and the stock index market will haveimpact on the stable development of China's financial marketand the real economy.TABLE I.Representation symbolIFUSDCNYEURCNYJPYCNYIII.EMPIRICAL ANALYSIS OF VAR MODEL TESTA. Sample Selection and Raw DataThe sample interval selected in this paper is the monthlydata from August 2012 to December 2017, with a total of 65data.In order to make the data more representative, this paperselects the closing price of the Shanghai and Shenzhen 300stock index futures to represent the price of stock indexfutures. Shanghai and Shenzhen 300 stock price index is theearliest and stable, with a mature and rich indicator system.Shanghai and Shenzhen 300 stock price index has 60% ofmarket depth. After the exchange rate reform in 2005,China’s exchange rate composition is no longer pegged tothe US dollar. Instead, it implements management-controlled,floating, dollar-based "a basket of currencies" exchange ratemechanism. According to China's foreign economicexchanges and the economic strength represented by variouscurrencies, we choose the exchange rate of the US dollar,Euro, and the Japanese yen against RMB to represent thebasic situation of China's RMB exchange rate. The authoruses the direct quotation method to show the exchange rateof 1 US dollar against RMB and 1 Euro against RMB, andthe exchange rate of 100 units of Japanese yen against PMB.The data comes from Sina Finance. The relevant informationof the four variables is shown in “Table I”. “Fig. 1”, “Fig. 2”,“Fig. 3” and “Fig. 4” depict the trend of the Shanghai andShenzhen 300 stock index futures, the exchange rate of RMBagainst the US dollar, and the exchange rate of RMB againstJapanese yen from July 2012 to December 2017.BASIC SITUATION OF THE FOUR VARIABLESVariable nameDefinition methodShanghai and Shenzhen300 stock index futuresThe USdollar/RMBexchange rateThe euro/RMB exchangerateThe Japanese yen/RMBexchange rateThe closing quotation of Shanghai andShenzhen 300 stock index futuresdirect quotation 1 US dollar in exchangefor the amount of RMBdirect quotation 1 Euro in exchange forthe amount of RMBdirect quotation100 Japanese yen inexchange for the amount of RMBFig. 1. Curve of the Shanghai and Shenzhen 300 stock index CNYLNEURCNYDLNEURCNYLNJPYCNYDLNJPYCNYFig. 2. Graph of USD/RMB exchange rate.2
Advances in Economics, Business and Management Research, volume 70In “Fig. 1”, “Fig. 2”, “Fig. 3”, we clearly see that fromJuly 2012 to May 2015, Shanghai and Shenzhen 300 stockindexes first peaked, and then fell, and tend to grow slowlyin 2016. The exchange rate of the US dollar against RMBhas been slow downward trend since 2012, and has beencontinuously adjusted in the upward trend since July 2015.The exchange rate of Euro against RMB first fell to a lowpoint and did not begin to rise until July 2015. The Japaneseyen against the RMB has been constantly adjusting from theoverall downward trend since 2012.B. Sequence Test1) The sequence's stationarity test (unit root test): Thepremise of analyzing the time sequence is to ensure thestability of the sequence, and the non-stationary timesequence participates in the regression modeling analysis,which leads to the pseudo-regression problem. Therefore,before performing the analysis, it is necessary to perform aunit root test on the original variable sequence to judge thesequence's stationarity. If the sequence is stable, themodeling can continue. If the sequence is a non-stationarysequence, differential processing is required. For smoothmodeling, the ADF unit root check of the original sequenceis required. The original hypothesis is that there is one unitroot in the sequence, and the alternative hypothesis is thatthere is no unit root. The results are shown in “Table II”below:Fig. 3. Curve of the exchange rate of Euro/RMB.Fig. 4. Curve of the exchange rate of Japanese yen.TABLE II.SequenceIFUSDCNYEURCNYJPYCNYADF value-1.727399-1.168528-1.168528-3.112481ADF UNIT ROOT TEST RESULTS FOR ALL SEQUENCESThreshold at 1%level-3.538362-3.536587-3.536587-3.536587As can be seen from "Table II", after performing the unitroot test on the original sequence, only the P value of theJPYCNY variable is less than 5%, and the P value of thedependent variable IF and the other two independentvariables USDCNY and EURCNY are 0.6830, greater than5.%. The original hypothesis that the sequence has a unit rootcannot be rejected, and the sequence is considered to be nonstationary. Therefore, the first-order difference test isTABLE III.First-orderdifference sequenceDIFDUSDCNYDEURCNYDJPYCNYADF value-6.041509-5.452358-7.528294-8.714180Threshold at5% level-2.908420-2.907660-2.907660-2.907660Threshold at 10%level-2.591799-2.591396-2.591396-2.591396P ratios0.41280.68300.68300.0306performed on the original sequence, and the unit root test isperformed. The results are shown in "Table III" below:ADF UNIT ROOT TEST RESULTS AFTER FIRST-ORDER DIFFERENCEThreshold at ld at ld at 10%level-2.591799-2.591799-2.591799-2.591799a.P ratios0.00000.00000.00000.0000Note: D represents the first-order difference of the sequence.3
Advances in Economics, Business and Management Research, volume 70It can be seen from "Table III" that P values are equal tozero under the threshold of the first-order difference that isless than 1% of the confidence level. The unit roots havebeen eliminated. It is in line with the conditions of smoothmodeling.influence of heteroscedasticity. And the sequence is stableand satisfies the modeling conditions. In this paper,Johansen cointegration test is used to test the VAR (1)model Johansen cointegration test of the Shanghai andShenzhen 300 stock index futures prices, the US dollaragainst RMB, the euro against RMB, and Japanese yenagainst RMB. The test results are shown in "Table IV":2) Cointegration test: After the first-order difference,the data is verified to have no unit root, which eliminates theTABLE IV.TRACE STATISTICS TEST RESULTSUnrestricted Cointegration Rank Test (Trace)Hypothesized No.of CE(s)EigenvalueNone *At most 1 *At most 2At most 3Trace Statistic0.3086860.1893370.0676130.031576Critical Value 0715.494713.841466a.As can be seen from "Table IV", the trace statisticcorresponding to each hypothesis is smaller than thecorresponding critical value. Each corresponding P value isgreater than the confidence level by 5%. The originalhypothesis cannot be rejected, and there is no association inthe sequence. The whole relationship satisfies the VARmodeling conditions.C. Establishment, Estimation and Verification of VARModel1) Establishment of VAR model: The general form of theVAR model is as follows: Y1t p Y1t Y1t 1 Y2 t p Y2 t Y2 t 1 Y3t A Y3t 1 . A Y3t p1p Y Ykt Ykt 1 kt p2) Choice of the number of lag periods: There are threemethods in the market that are often used to calculate thenumber of lag periods, such as the likelihood ratio (LR) test,the Akaike Information Criterion (AIC), and the SchwarzInformation Criterion (SC). This paper selects two methodsto determine the number of lag periods of the model,including AIC and SC minimization criteria. As shown in"Table V" below:TABLE 05 Y AYttt 1,2,3.nt -1 AY2Prob.**0.11880.42500.63240.1519t -2 Trace test indicates no cointegration at the 0.05 levelAY3t 3 . AYpt p BX t,t(1)General Training Module: Yt represents endogenousvariable of the K in the same period. X t represents externalvariable of d . At and B represents the correspondingcoefficient matrix to be estimated. P stands for the lag periodof endogenous variables. n represents the number of datasamples. t is the motivations of K . The k-dimensionalVAR (P) model is expressed as a vector as follows: X 1t X 2t B X3t X dt 1t 2t 3t kt （2） t 1, 2,3.n SELECTION OF LAG PERIOD OF VAR 32*9.77834110.2002310.68641a.* indicates the lag order selected by the standardb.c.d.After testing, the minimum AIC and SC values of the lagperiod are 9.234061 and 9.932176, respectively. Therefore,LogL: log likelihood function valueAIC: Akaike Information GuidelinesSC: Schwarz Information Guidelinesthe lag period is selected as the lag phase of the VAR model.Therefore, this paper chooses the VAR model with a lag4
Advances in Economics, Business and Management Research, volume 70phase to verify the impact of the exchange rate factor onShanghai and Shenzhen 300 stock index futures price index.TABLE dj. R-squaredSum sq. residsS.E. equationF-statisticLog likelihoodAkaike AICSchwarz SCMean dependentS.D. dependent3) Estimation of VAR model: We use the statisticalsoftware Eviews7.0 to estimate the VAR model by the lagphase. The output results are shown in "Table VI" below:VAR MODEL ESTIMATION RESULTSIF0.843740(0.07947)[ 10.6174]122.5464(144.022)[ (0.02739)[ (0.08923)[ (0.12178)[ 876)[-0.29413]727.3922(1167.92)[ .01885)[-3.00832]0.003364(0.01190)[ 0.28260]0.643301(0.22210)[ .06140)[ 14.2054]0.066218(0.03878)[ 1.70775]0.410506(0.72357)[ 0.08380)[-0.84384]0.847091(0.05292)[ 16.0068]0.415254(0.98753)[ .03278-0.251024-0.0823625.9895310.658622a.Note: ( ) is the standard deviation of the coefficient, and [ ] is the t-statistic of the coefficient.In order to clearly analyze the results of the VAR modelestimation output, we present the estimation results asvectors in the following form:IF 0.84374 USDCNY 2.02 E 05 EURCNY 398 E 05 JPYCNY 0.000105 18.40885 727.3922 IF t - 1 0.003364 USDCNY t 1 0.643301 * 0.045667 0.8722600.066218 EURCNY t 1 0.410506 0.208892 0.070717 0.847091 JPYCNY t 1 0.415254 122.5464 116.39670.974548 0.056700 0.881008 0.888563 2 0.969849 0.971763 R R2 0.923341 0.928208 0.902567 0.908754 The estimation results of the model in "Table 6" show theoverall goodness of the model. In terms of , the USDCNYequation has the best goodness of fit, reaching 97.1763%.Followed by the EURCNY equation, the goodness of fit is（3）92.8208%. The goodness of fit of the IF equation is thelowest, and the goodness of fit is 88.8563%.The mathematical equation expression for the VARmodel is as follows:IF t 0.84374 * IF t 1 122.5464 USDCNY t 1 116.3967 * EURCNY t 1 18.40885 * JPYCNY t 1 727.3922 （4）4) Stability test of VAR model: The eigenvalues of theVAR model in this paper are shown in “Fig. 5” below:5
Advances in Economics, Business and Management Research, volume 70Inverse Roots of AR Characteristic PolynomialAs can be seen in "Fig. 5", the reciprocal modes of theVAR model eigenvalues are all distributed in the unit circle,indicating that the established VAR model is stable. And themodel can further perform Granger causality test andimpulse response function 51.01.5Fig. 5. The modulus distribution of the reciprocal of the AR eigenvalue.5) Granger causality test: The test results are shown in “Table VII”, “TableVIII” and “Table IX”.TABLE VII.GRANGER CAUSALITY TEST RESULTS OF RMB EXCHANGE RATE AGAINST THE US DOLLAR AND SHANGHAI AND SHENZHEN 300 STOCK INDEXFUTURESDegree offreedom626160595857565554535251504948TABLE VIII.Lag lengthF 830.74116P GER CAUSALITY TEST RESULTS OF RMB EXCHANGE RATE AGAINST EURO AND SHANGHAI AND SHENZHEN 300 STOCK INDEX FUTURESDegree offreedom6261TABLE IX.Granger causalityDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIFDUSDCNY DIF123456789101112131415Laglength12Granger causalityDEURCNY DIFDEURCNY DIFF value1.851574.00877P ratios0.17880.0236ConclusionacceptrejectGRANGER CAUSALITY TEST RESULTS OF RMB EXCHANGE RATE AGAINST JAPANESE YEN AND SHANGHAI AND SHENZHEN 300 STOCK INDEXFUTURESDegree 3456789101112131415Granger causalityDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFDJPYCNY DIFF value0.042150.246680.444280.320890.440510.3
concluded that the long-term impact of RMB exchange rate fluctuations on the GEM index is significant. Foreign scholars have studied the impact of exchange rate on stock index futures. Almost everyone uses relevant data from developed countries. China’s exchange rate system has been linked to single dollar until July 21, 2005.
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Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.
Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. 3 Crawford M., Marsh D. The driving force : food in human evolution and the future.
