Ahmed, Abdullahi Farah

Latif, Ismail

Mohd Arshad, Fatimah

Universiti Putra Malaysia Institutional Repository

Social SciencesComparative Analysis of Forecasting Performance:Crude Palm Oil Futures vs Expert OpinionsAbdullahi Farah Ahmed and Fatimah Mohd Arshad, Ismail LatifFacultyof EconomicsandManagementUniversitiPutraMalaysia43400UPM,Serdang,SelangorMalaysiaE-mailofCorrespondingAuthor:fatimah@econ.upm.edu.myKey words: futures markets, forecast accuracy, expert opinion, agricultural commodity, crude palm oil.IntroductionThe main economic functions of thefutures market are to provide pricediscovery and risk management facili-ties. If the market is not efficient, ef-fective transmission of informationfrom one market to another will beimpaired, thus the value ofthe futuresmarket in price discovery and hedging.In the present article we analyze a par-ticular commodity futures market,namely MDEX (Malaysian DerivativesExchange) crude palm oil futures et al.with comparing expert opinions. The .creation of the crude palm oil (CPO)futures market in Malaysia was to ful-fill the need for an efficient pricing andhedging mechanism for Malaysia's'palm oil. Therefore, the extent to whichCPO futures market has served as anefficient center of price discovery andrisk management has been the focus ofconsiderable research. In general, de-bate has centered around the extent ofwhich CPO futures market provideprice leadership to cash market and theability of futures market to predictsubsequent spot price in accurate way.Empirical evidence has shown thatCPO futures prices performed relativebetter compared to other forecastingmodels. However, no comparison ismade in forecast accuracy of CPOfutures with expert opinion. This ap-proach is unique in the sense that itexamines the relative efficiency of exante forecasts rather then ex post. Thepurposes of this study are: firstly, toevaluate the forecast accuracy of theCPO futures market relative to expertprediction. Secondly, to test the rela-tionship between the futures, forwardand cash prices, to examine whetherthese price series have the same prop-erties and relationship in the long run.Materials and MethodsUsing time-series techniques, its possi-ble to estimate the nature of the rela-tionship between two prices. Theseprice series in generally believed to benonstationary and therefore much ofthe variability in the results may havehad to do with the spurious nature ofregressing non stationary series on eachother. In order to overcome this prob-lem, the cointegration technique asproposed by Engle et al. and Granger etal.(1987) could be used. This analysisis carried out in three stages.Firstly, there is a need to examine forthe existence of nonstationary in priceseries. If the two price series are non-stationary and the presence of unit rootin both the price series is confirmed,they are examined then to determinewhether they are cointegrated or not.The stationarity of the price series aretested with Phillips et al. and Perron(PP)(1988), and ADF (1979) tests. Onethe nature of cointegration is estab-lished the second step involves measur-ing the direction of the causality be-tween variable and error correctionmodel can be tested to investigate theforecasting power of CPO futures andforward prices. Engle et al. andGranger et aL( 1987) proposed that if alinear combination of two differencetime series each is integrated of orderone, denoted I (1) is stationary, then thetwo difference time series are cointe-grated, and they expected to movetogether in long-run .. When both thespot price (Spt+i ) and the futures price(Fpt, h ) are each nonstationary andthey require a single difference to makethem stationary, their linear combina-tion is also generally I (I). By integrat-ing the concept of cointegration andcausality in the Granger et al. sense it ispossible to develop a model that allowsfor the testing of the presence of both ashort and long run relationship betweenthe variable Fpt and Spt,. This model isknown as the error correction model(ECM) proposed by (Engle et al. andGranger et al., 1987). The ECM modelinvestigates the potential long and shortrun impact of the variable, Fpt on thevariable Spt or vice verse. Lastly a testis carried out to measure the relati veaccuracy of CPO futures to forwardprices as predictor of cash prices. Avariety of summary statistics have beencomputed from futures forecast error(cit-h) to compare with forward fore-cast error (ejt+h) to evaluate the fore-cast accuracy of the futures market.The statistics used for comparing eco-nomic forecasts are based on meanabsolute error (MAE), mean squareerrors (MSE), mean absolute percent-age errors (MAPE), mean percentageerrors (MPE), and their variants such asroot mean square errors (RMPSE) androot mean percentage errors (RMAPE).Most of these measures involve aver-aging some function of the differencebetween actual values and its forecastvalues. The purpose of an error meas-ure is to provide an informative andclear summary of the distribution,which could be easily comparable. Thebest forecasting model would be theone that produce the lowest statisticaldescriptive.Three types of data have been used forthis study. The first data is the dailyspot price or cash price of CPO tradedin Malaysia. This data can be obtainedfrom the statistical handbook "Palmupdate" of the Malaysian Palm OilBoard (MPOB). The second is dailyforward prices of crude palm oil et al.that can also obtained from variousadditions of PORLA et al. Update. Thethird is historical data on CPO futuresprices and their settlement prices fortrading days in COMMEX, which isavailable to producers, and trader fromtheir COMMEX WebPages. The CPOfutures contracts are traded on the355UPM Research Report 1997-2000, Section 2-Extended AbstractsCOMMEX for every month up to five-month ahead and alternative monthsafter up to twelve months ahead. Sincemore distant futures contracts are usu-ally inactive or untraded, only contractprices up to four months forward hasbeen used in this study. The data arecollected over the lO-ycars period,from January 1989 to December 1999of the daily prices on last trading dayof the each month. The last trading dayof the each month was utilized, underthe assumption that the latest pricewould incorporate the latest informa-tion and thereby provides the mostaccurate and up-to-date forecast.Results and DiscussionThe results of ADF and PP tests ofstationarity for the levels and first dif-ference of the price series of spot andfutures suggest that the null hypothesisof the presence of unit root in priceseries cannot be rejected and therefore,the spot and futures prices are nonsta-tionary in the level model. But theirresults on the first differences of spotand each of four futures price seriesshows that the null hypothesis of unitroot is rejected at 5% levels of signifi-cance for all trading months. Based onthese ADF and PP test results, it can beconcluded that the cash and futuresprices are integrated of the first order I(I). Similar tests of ADF and PP wereapplied on the spot and forward priceseries (one, two and three monthsahead) for detection of unit root inprice series. The tests indicate that allthe prices are integrated of the firstorder; that is, 1(1).The spot and futures price for thespread of one, two, three and fourmonths were first checked for cointe-gration. The results suggest that coin-tegration with rank equal to one(r = 1)exist between the spot and futures pricefor spreads to one and two-months.Hence, the one-and two-month spreadmodels are cointegrated with oneco integration vector. Besides, there is along-run relationship between spot andfutures prices series in one and twomonths ahead while the spot and fu-tures price series more than thatspreads and do not have such relation-ship.The cointegration tests were repeatedon spot and forward prices for the one-month, two-month and three-monthsahead. It was found that cointegrationwith rank equal to one exist between356spot and forward prices for spreads ofone months and two-months, whilethere is no such cointegration evidenceexisting between spot and forwardprices with three months ahead.It can be concluded that the spot andfutures up to two months and spot andforward prices up to two months arecointegrated. These finding suggestthat futures and forward markets ofCPO price discovery are efficient up totwo months. Such relationships do notexist for further futures and forwardprices of CPO, implying that thoseseries are separately generated andcannot be expected that further futuresprice provide information for predict-ing CPO cash prices. The further thedistance the period is into the future,the more fluctuations in both spot,forward and futures markets are ex-pected. As such, the shorter the periodahead futures prices may move alongthe spot price, while the longer theperiod ahead futures prices may notdue to the greater uncertainty.The results of ECM in futures, forwardand cash price series imply a futuresmarket that is working in an efficientmanner and one that is providing aforward pricing function up to one andtwo months spreads. The estimatedresult indicates that a movement in spotprice is causing a correction to thefutures price. It was also found that thefutures responded faster to a change inthe spot price in the one-month than inthe two-months spread futures.In general, the mean square error levelof bias is larger in futures compare toforward errors in both one and two-month spreads. The MSE of futures inone-month (Fl ) and two-month (F2)horizons are 556.32 and 1704.16, re-spectively, while the MSE of forwardsare 331.59 (Fwl) and 1046.78 (fw2),respectively. This indicates that theerror bias of futures is nearly 70 percent over the error of forward price.ConclusionsA number of studies found that CPOfutures prices perform well relative tomodel forecasts. Given the efficiencyand implications of these studies, arelevant issue is whether alternativecomparisons yield similar results. Onealternative is to compare the accuracyof CPO futures prices and forwardprices. This approach is appealing be-cause ex ante futures prices are com-pared to ex ante forecasts, rather thanex post forecasts. This study comparesthe forecasting accuracy CPO futuresprice to PORLA's et al. expert predic-tions (forward prices). The sampleperiod is the end-of-month tradingdaily price of the 10-year period begin-ning in January 1989 and ending withDecember 1999. This provides a suffi-cient number of observations to con-duct valid statistical tests (Williams etal., 1993). A test developed by Ashleyet al., Granger et al., and Schmatensee(1980) is used to assess the signifi-cance of differences in forecastingaccuracy and cointegration analysisdeveloped by Johansen et al.(1988,1991) to test the long-run rela-tionship between prices series. Whilethere is long-run relationship betweenfutures and cash prices, which impliesthat the futures market is forecast effi-cient until one-and two-month spreadsfor using cointegration analysis, theAGS's test results indicate conflictoutcome than former. The forecastaccuracy of forward pricing outper-forms futures pricing. This suggeststhat a forecast taken from the futuresmarket is not likely to provide the userthe most accurate information. Hence,the results did not support the effi-ciency of CPO futures pricing. Thus,the CPO producers and traders mightbe better off to use the forward price topredict subsequent cash prices since ithas more accurate information thanCPO futures markets.Benefits from the studyThe study provides an understanding ofthe relationship between physical, for-ward and futures market of CPO. Italso provides some empirical evidenceon the relative forecasting efficiency offutures vs expert opinion. The findingsof the study are useful for policy analy-sis and further research on this subjectmatter.Literature cited in the textAshley,R.,Granger,C.W.J. andSchmalensee,R. 1980.Advertising andaggregate consumption: An analysis ofcausality. Econometrica,48(5): 1149-1197.Engle,R., andGranger,C.1987.CointegrationandErrorCorrectionRepresentation,EstimationandTesting.Econometrica,55: 251-276.Johansen,S.1988.Statistical Analysis ofCointegration Vectors. JoumalofSocial SciencesEconomic Dynamics and Control, 12:231-254.Johansen, S.I991. Estimation andHypothesis Testing of CointegrationVectors in Ganssian VectorAutoregression Models Econometrica,6:551-1580.Malaysia. PORLA, 2000, Annual Palm OilStatistics, homepage http://porla.gov.my.Malaysia, PORLA. PORALA Update.Kuala Lumpur: PORLA, Various Issues.Phillips, P. and Perron, P.1988.TestingforaUnit Root in Time series Regression.Biometrika, 75:335-346.Williams, J.1993. The Forecasting Ability ofFutures Markets Compared toEconometric Models. Working Paper,Food Research Institute, StanfordUniversity.Project Publications inRefereed JournalsFatimah Mohd. Arshad and ZainulabidinMohamed. 1991. The efficiency of theCrude Palm Oil (CPO) futures markets inestablishing forward prices. Malaysian J.of Agricultural Economics. 8: 25-39.Mad Nasir Sharnsudin, ZainulabidinMohamed and Fatimah MohdArshad.1989. Selected Factors AffectingCrude Palm Oil Prices. Malaysian J. ofAgricultural Economics. 5:20-29.Project Publications inConference ProceedingsAbdullah Farah Ahmed, Fatimah MohdArshad and Ismail Latif. 200 1.Comparative Analysis of ForecastingPerformance: Crude Palm Oil Futures vsExpert Opinion. paper presented at theSeminar FEP 2001, organized by theFaculty of Economics and Management,Universiti Putra Malaysia, 2-5 Oktober2001, Kuantan, Pahang.Graduate ResearchTaufiq Hassan. 200 1. Finance [Ph. OJUniversiti Putra Malaysia.Abdullah Farah Ahmed. 2001. AgriculturalEconomics [M.ScJ Universiti PutraMalaysia.Chan Tung Sing. 2001. Economics [M.Sc]Universiti Putra Malaysia.357

Comparative analysis of forecasting performance: crude palm oil futures vs expert opinions

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Comparative analysis of forecasting performance: crude palm oil futures vs expert opinions

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