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Currency futures hedging effectiveness in cme group  by md rubel khondoker
 

Currency futures hedging effectiveness in cme group by md rubel khondoker

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    Currency futures hedging effectiveness in cme group  by md rubel khondoker Currency futures hedging effectiveness in cme group by md rubel khondoker Document Transcript

    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP BY MD RUBEL KHONDOKER1. Introduction :Currency hedging is a mechanism to reduce foreign currency risk exposure .Foreign currencyhedgers use various strategy to eliminate the risk in foreign currency market. For AOptimum currency hedging, hedger can take delta hedge, cross hedge or delta cross hedge.Currency Hedgers use financial derivative to reduce the risk from variations in the spotmarket. Hedgers usually sort a currency futures contract when they take a long position onunderlying assets. Hedgers participate in futures market to reduce their risk for a premiumbut in futures market there is mismatch maturity mismatch in currency so hedgers need toknow the optimal number of futures contract for taking a long or short position in futuresmarket .If hedger can estimate the optimum number of contract for short or long they cansignificantly reduce their risk. The hedge ratio is the ratio of the size of the position taken infutures contracts to the size of the exposure (C.Hull, 1998).Currency risk:Currency futures have become extremely popular after Bretton Wood agreement wasbreakdown. The appearance of futures markets for foreign currency inspires hedger toreduce their currency risk exposure. Since world is becoming smaller and international tradeis going up significantly, currency risk turn out to be a fundamental concern for manyinternational merchandiser and international investor. International investors diversify Page 1
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430there portfolio internationally because domestic market potentially my not give the returnfor the risk they take for so they invest in foreign country for compensate their risk butcurrency risk appear in the middle and their profit can turn into sour . There for in order tohedge this risk hedge seekers look for a approach that can eliminate there exposure. Thereare many financial derivatives in the market to reduce this risk like such as ;currency futures,currency options, currency swap etc. among them currency futures is prefer in case ofcurrency hedging . Adams,j.& Montesi,C,J.(1995) in their study find that currency futures aremore preferable to currency option for corporate managers because of considerable bigtransaction cost. Chang, J. S. K. and Shanker, L.(1986) in their study also concluded thatcurrency futures are better hedging derivatives compare to currency options.Empirical evidence in currency risk exposure:Volkswagen is a German automobile manufacturer company in year 2002 to 2004 it wasfacing problem because of their home currency EUROFX appreciation against foreigncurrency dollar .it had to pay its labour cost and operating cost in EUROFX but it receivedrevenue in dollar for the cars that it sold in the USA. For foreign exchange risk exposurebetween 2004 and 2005 it has increased hedging against foreign exchange risk by currencyderivative and it’s also expand some of its production facilities in USA .This way Volkswagenwas able to shield its revenue from foreign exchange volatility and eliminate the currencymismatch between cost and revenue. (Carbaugh, R,j., 2009)Currency hedging with futures : Page 2
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430futures contract my not match the maturity or currency .when currency does not match butmaturity match in the futures contract it this case by doing a cross hedge hedgers caneliminate their exposure . if there is maturity miss mass futures contract may not provide aperfect hedge so When maturity dates does not match the exposure to be hedged thendelta hedge can be constructed and when both currency and maturity does not match deltacross hedge can be constructed in order to minimize exposure that needed to be hedged.Basis risk:At one stage usually spot and futures price have a big spread specially when the settlementtime is long period but when the maturity or settlement time comes very close the spreadreduced significantly .Basis can be express like below:Basis = (Futures price – Spot price)Another way to express it is:Basis= (spot price –futures price)When basis is positive it called Contango and when basis Is negative it is Backwardation.Another way to state it as premium or discount. Basis point is one hundredth of 1% or0.01%.Futures are closely compared to forward transaction which is usually priced by “COST OFCARRY “ idea .If the market is efficient which means all the information is present in themarket, everyone got the same information about the market and there is no arbitrage thendetermining the basis will be difference between domestic and foreign currencies interestrate and the payout on the underlying asset but the relationship between the term and base Page 3
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430interest rates may affect the basis in the interim. Determination of the basis in currency canbe found by following equation: Ft,T=Ste(r-r*)τ (Clark, 2002) (1)Where:Ft,T =price of a future contract at time t for delivery at time TT=delivery date of currency future contract (years)t =current date (years)τ =T-tSt =Spot price at time tr =risk free rate on domestic currencyr*=risk free rate on foreign currency1.2 Currency futures in chicago mercantile exchange group (CME) Group :Currency futures was first launch in 1972 by Chicago Mercantile Exchange via InternationalMonetary Market (IMM) .when Bretton Wood agreement was been breakdown currencyfutures my be considered as a direct respond .CME Group is the largest market for Foreignexchange futures in the world .its makes transactions of more then $1.9 trillion a day andforeign exchange market impact on all the countries economy .Since its creation it had added many currency contracts among them British pound,EUROFXFX, Japanese yen, Swiss franc, Canadian dollar, Australian dollar, Mexican peso,Russian ruble, Swedish korna, Nowegian korne, Brazilian real are quite frequently used infutures contract . Page 4
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Trade unit for EUROFXFX futures is 125,000 EUROFXs, Swiss franc Futures is 125,000 francs,British pound future is 62,500 pounds, Japanese yen futures is 12,500,000 yean and MexicanPeso Futures is 5,00,000 pesos .contract settlement are usually in the month of march, June,September, December.Chicago mercantile exchange group use U.S. central time ,the time in Chicago, where CMEGroup headquartered situated. CME Group begins trading at 0720 hours and close out at1400 hours and for Electronic Trading 17:00 to 16:00 hours next day all the currency futurescontract that we have used for our analysis was been traded between those hours .There isno counter party risk involved and all the transaction goes through be clearing house. Andthere is low transaction cost.Traders notes:The rapid growth of futures contracts in foreign currencies testifies to their usefulness andpopularity, but some of these markets are still somewhat thin. This can be very dangerous.It is advisable to avoid Friday afternoon after the London markets close because of the lackof liquidity at this time. (Wasendorf, 2001)1.3 Statement for research problem:Currency fluctuation can cause investors or merchandiser income in there basecurrency .so to reduce there exposure they can hedge by taking long or sort position incurrency futures market .for hedging against exchange rate exposure they needs to findoptimal hedge ratio . By hedging through currency future they can significantly reducetheir amount of exposure and increase their gain . Page 5
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 44301.4 Objectives: Our objectives are to emphasize hedging effectiveness in currency futures contracts. Estimate the “Optimal hedge ratio” for hedging in march, June, September, December settlement . Determining the relationship between changes in spot price and futures price.1.5 Scope for this study:Currency futures helps to reduce exposure from the currency movement such that;income and profit can become sour if cash inflow is low because of an appreciation ordepreciation of currency .Spot and future exchange rate differ significantly beforematurity and infrequent maturity dates made it difficult for futures contact tocorrespond perfect maturity of the cash flow that needs to be hedge. So this study isconstructive for the participant of futures market who wants to hedge against their cashflow in certain period of time. By using the hedge ratio they can eliminate theirexposure against uncertain movement of currency exchange rate.2. Literature Review:Many researcher invented many new technique to come out with better estimation ofoptimal hedge ratio for currency futures and many models like Ordinary Least squire,Autoregressive integrated moving average, Autoregressive moving average, Generalized Page 6
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430autoregressive conditional Heteroskedasticity, Vector Auto regression ,Exponential generalautoregressive conditional Heteroskedasticity etcThe review focuses on studies specifically conducted on currency futures but for estimationof optimal hedge ratios other types of futures contracts ,farms value using currencyderivatives instrument , hedging in different market, hedging for different investors alsomentioned in order to understand the development of the research .2.1 Farm Value Using Currency Derivatives Instruments:Elliott,W,B.,Huffman,S,P.,Makar,S,D,(2003)in a study they investigate the implications offoreign exchange derivatives use for the association between firm value changes andexchange rate changes and they found a lagged firm value/exchange rate relationship andforeign exchange derivatives plays an important role in understanding the lagged marketresponse to changes in exchange rate . They found that the lagged firm value effects ofexchange rate changes are particular to companies with low foreign exchange derivativesuse relative to their foreign sales and the level of foreign exchange exposure decreasesmonotonically across all foreign exchange derivatives group. Terry,E(2007) hedging foreigncurrency exposure when a future foreign currency does not exist but exist a futurescontract on the value of the local currency in terms of foreign currency exist . in comparisonof inverse hedging strategies they have examined five inverse hedging strategies using bothdaily and weekly return . the inverse conintegrated hedge for daily return performed betterthen all other strategy ,the inverse lognormal hedge performed little bit less and the inverseCI-GARCH hedge performed most terrible average hedging strategy. On the other hand in Page 7
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430comparison with direct hedging strategies using daily returns, the most effective directcurrency hedge performed better from the sample of “CME” contract than thecorresponding inverse currency futures hedge from the sample of “ICE “contract. Nguyen,H& Faff,R.(2003) in a study with a sample of 469 non financial Australian companies with asample period of 1999 to 2000 and two levels of analysis (Logit and Tobit) found thatleverage and firm size are the two most important factors to use financial derivatives largefirms with more debt in its capital structure is likely to use foreign currency derivatives andlarge firms with high levered ,high liquid and pays higher dividends use interest ratederivatives. there result are reliable with existing hedging theories.Nguyena,H.,Faff,R.,Marshall,A(2007) examine the impact of the introduction of the EUROFXon foreign exchange exposures for French firms .they examine the post EUROFX exchangerate exposure for those corporate use foreign currency derivatives to hedge .Their findingsignal that introduction of the EUROFX related with reduction in number of firms significantexchange rate exposure and absolute size of exposure and French firms use foreigncurrency derivative less intensively. Geczy,C., Minton,B,A., Schrand,C,M.(1997)in a paper“why firms use currency Derivatives” they have examine the use of currency derivatives fora sample of firms that have ex ante exposure to foreign exchange rate risk and themagnitude of exchange rate risk exposure benefits that can be realized from reducing riskand cost associated with risk reduction . in there sample 41% firms have used currencyfutures ,currency option ,currency swap . All the firm that have greater growthopportunities and tighter financial constraints are more likely to use currency derivatives.Allayannis,G,S & Ofek,Eli(1997)In study analyses whether firms use currency derivatives forhedging or for speculative reason and the impact of currency derivatives on firm exchangerate exposure and all the factors for hedge and factors that cause their decision on how Page 8
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430much they should hedge .by taking a sample of S&P nonfinancial firms for 1993 ,and usingweighted least squares and probit model they found strong negative relationship betweenforeign currency derivative for hedging and speculate in the foreign exchange markets.Allyayannis, G,S. & Weston,J.(1998)Examines the use of foreign currency derivatives(FCDs)and its potential impact on farm value in large U.S non financial firms using sampleperiod of 1990 to 1995.Using Tobin’s Q as an proxy of a firms market valuation they foundrelation ship between firm value and the use of foreign currency derivatives which meanshedging increase firm value overall . Bodnar,G,M.,Hayt,G,S.,Marston,R,C.(1998) in a study,explained that Exchange rate risk management is combination of financial and operationalhedges as part of an integrated risk management strategy aimed at reducing exposure toforeign exchange risk. and financial hedges via the use of derivative instruments mainlytarget short-term ,observable exposures.2.2 Hedging effectiveness in different Market :Floros C and Vougas D, V,(2006)in there study investigate the hedging effectiveness of Greekstock index future contracts on FTSE /ASE-20 and FTSE/ASE-40 and they have consisted themethods of OLS,ECM,VECM and Bivariate GARCH(1,1)to obtain hedge ratio .the outcome ofOLS model for FTSE /ASE-20 provides large risk reduction and ECM produces the mosteffective hedges and both contracts the OLS hedge ratio shows greater variance reductionand BGARCH (1,1) hedge ratio provides greater variance reduction then other models andgenerates better results in terms of hedging effectiveness .finally for hedging effectivenessby considering the hedging performance for the post-sample periods, and using forecastingstatistics they found that Error Correction model outperforms the OLS model ,there forthe Error correction model(ECM)is superior to the OLS model . Page 9
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Pok,W,C.,poshakwale,S,S.,Ford,J,L(2009)examined the hedging performance of dynamic andconstant models in the emerging Malaysian market during the financial crises and foundthat the General GARCH model outperforms other models like TGARCH and provides thebest hedging performance during the normal period, financial crisis period ,and in theperiod after imposition of capital controls.2.3 Hedging for different Expectation of investors:Wang,C.,& Low,S,S.(2003) in their studies they have compare optimal hedging strategies fortwo different types of investors .one is international investor and other is domesticinvestors . they have investigate with MSCI Taiwan index future contracts from January1997 to June 2000 , and daily closing price of MSCI Taiwan index future contracts and theyfound that MSCI Taiwan index futures market is about fifty percent more volatile then thespot market ,the average daily changes in the price of New Taiwan dollar is -0.022% so USdollar was appreciating against Taiwan dollar on the sample period . they have usedGARCH(1,1)error correction model to estimate optimal hedge ratios for both theinternational and domestic investors and reason behind it was that GARCH(1,1) adequacyof characterizing the dynamics of the second moment of financial asset prices . and theyhave compared four different hedging techniques such as Naïve ,OLS ,OLS-CI(spot andfuture prices cointegration ) ,and GARCH error correction model . their result shows thatdomestic and foreign both investors benefit from future contracts and internationalinvestor benefit more then domestic investors and optimal hedge ratio in equity, futuresand currency markets tends to be large then the domestic inventors. Page 10
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 44302.4 Research for hedging effectiveness:Herbst,A,F.,Kare,D,D.,Marshall,J,F.,(1997) in a study they have employed futures contractsfor British pound ,Canadian dollar, German mark, Japanese yen and Swiss franc and all thiscontracts were traded on Chicago Mercantile Exchange and the data range was form 2 nd ofJanuary 1985 to 17th June 1985 and they have compared OHR and JSB and conclude thatJSB s minimum risk hedge ratios calculate as the slope coefficient in ordinary least squaresregression and the intercept term does not considered and do not take in to account for adeclining basis of a future contract and JSE Portfolio hedging technique do not take intoaccount of a direct hedge relationship of futures price to spot price restricted by cost ofcarry and convergence of future price to spot price at maturity . they also mentioned thatOLS residuals form JSE estimation of minimum variance hedge ratio are serially correlatedand for that Box –Jenkins “Auto regressive integrated moving average (ARIMA) model couldbe use for estimating the minimum risk hedge. And for the suggestion for hedgers they saidOHR hedge ratio is better for sort term and for long term JSE hedge ratio performs superior.Tingting Y., Zongye C (2006) in their study they have compared with four different hedgingtechniques; the OLS regression model, the autoregressive model (VAR), the vector errorcorrection model(VECM) and Multivariate GARCH with error correction model arecompared in expressions to minimize variance by using spot and future exchange rates ofBritish Pound from 18 July 1994 to 1st march 2006 and they find that VAR and VECMperfume the same and perfume little higher then the OLS regression model and theMultivariate GACH model with error correction model that capture the time varying nature Page 11
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430of hedge ratio do not make difference vary much . Marmer, H, S(1986) in his article‘portfolio model hedging with Canadian dollar futures: A framework for analysis “ heanalysis the hedging effectiveness of Canadian dollar future from the sample period of July1981 to September 1984 and found that time invariant Minimum Variance Hedge Ratiohas a limitation of expediency. Akin(2003) investigate the volatility of financial futuresreturn with Australian dollar ,British Pound, Canadian dollar, German mark, Japanese yen,Swiss franc and the sample of future data form Chicago Mercantile Exchange for a periodof 4th January 1982 to 31 December 2000 using GARCH model find evidence that time tomaturity play a big role in currency future .Liouia,A.,& Poncet,P.(2003)Currency forwardand currency future contracts are not substitutable when interest risk exists.Brailsford,T.,Corrigan,K.,Heaney,R(2001) “A comparison of measures of hedgingeffectiveness: a case study using the Australian all Ordinaries share price index futurescontract” the time period selected was 17th July 1990 to 9th June 1990 from AOI spot index.the analyze the hedging effectiveness on reduction in portfolio standard division all themeasure they employed that falls under Markowitz Mean Variance structure. LIEN,D.,YANG,L(2006) Investigates the effects of the spot-futures spread on the return and risk structure incurrency market of Australian dollar, British pound, Canadian dollar, Deutsche mark,Japanese yen and Swiss. They found evidence of positive and negative return on spot andfuture. And they found that in sample asymmetric effect model provides the best hedgingstrategy for all currency except Canadian dollar and out of sample the asymmetric effectmodel provides the best strategy for all currency and symmetric effect model providesbetter strategy in Canadian dollar and Japanese Yen. Markowitz, H.(1952)”PortfolioSelection “ mean variance framework was mentioned for hedging with basis risk ,which isdifference between future price and spot price .after that Working,H.(1953) , (Johnson, L., Page 12
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 44301960) drive minimum variance framework and (Ederington, Louis H., 1979) suggest thatminimum variance hedge ration can be defined as the ratio of the covariance between spotand future price to the variance of the future price and he mention that minimum variancehedge ratio is the slope coefficient of Ordinary Least Squire regression .Kenneth, F.K .,&Sultan ,J.(1993) have propose and estimate Bivariate error correction model(ECM) in ΔStand ΔFt with a GARCH error structure . The error correction term imposes the long runrelationship between St and Ft, and GARCH error structure allow the second moments ofthe distribution to change through time and the time varying hedge ratio can be calculatedform the estimated covariance matrix from the model .for the risk minimizing futures hedgeratio. They have employed British pound, Canadian dollar, German mark, Japanese yen andSwiss franc for their analysis. They have argued that there is a potential problem inconventional model first of all; if spot rates and futures rates are conintegrated thenconventional model will over difference data and ambiguous long run relationship betweenspot and future rates .secondly spot and future markets is constant which is not right inreality and difficult to produce risk minimizing hedge ratios. Engle,R,F.(1982) Suggested thatthis unobservable second moment could be model by specifying a functional form for theconditional variance and modelling the first and second moments jointly, giving what iscalled in the literature the Autoregressive Conditional Heteroskedasticity (ARCH )model .healso suggestion that the conditional variances depend on elements in the information set inan autoregressive manner has become the most common perhaps . The linear ARCH modelwas generalized by (Bollerslev, 1986) in a manner analogous to the extension from AR toARMA models in traditional times series by allowing past conditional variances to appear inthe current conditional variance equation .the resulting model is called Generalized ARCHor GARCH .ARCH and GARCH materialize valuable for estimating time-varying optimal hedge Page 13
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430ratio and number of Scholar given opinion that ,this ratios take in to considerations ofvariability over time. Among all the scholar Baillie,R, T.& Myers, R, J.(1991) in there studyconcluded that GARCH Model is more satisfactory.But until now there is no convenience evidence that such time-varying hedge rations arestatistically desperate from a constant hedge ratio .a time-varying covariance matrix of cashand futures prices is not adequate to establish that the optimal hedge ratio is time varying.Moschinia,G. & Myers,R,J.(2003) in a studies with a sample of corn cash and futures prices asample period of 1996 to 1997 they have drive their new multivariate GARCHparameterization to see optimal future ratio is constant over time and it is flexible form timevarying volatility even in constant hedge ratio and found that optimal hedge ratios does notvary only systematically with seasonality and time to maturity effects and optimal hedgeratio for weekly storage hedging of corn in the Midwest are time varying and can not beexplained by seasonality and time to maturity .Myers,R.J(1991) suggest that empirical ARCHmodels performance is not better than OLS model and there is no significant hedgingperformance between them .Moosa,I.A.(2003)in his studies with a sample period of 1987 to2000with a sample of spotexchange rates of British Pound and Canadian Dollar in opposition to United States Dollarand with a sample of monthly data for cash and futures prices and he analyzed with a firstdifference model, a simple error correction model and a general error correction model .after analyzing model he did not find significant difference for hedging effectiveness withboth sample and he concludes that “Although the theoretical arguments for why modelspecification does matter are elegant, what really matters for the success or failure of a Page 14
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430hedge is the correlation between the prices of the unhedged position and the hedginginstrument”In other words, low correlation make poor hedging position and high correlation make agood hedging performance3. Research Methodology :We have applied Minimum variance delta hedge because there is basis risk forasymmetrical or infrequent maturity and its not likely to maturity of futures contract willmatch up and it will mismatch with its cash flow that needs hedged.. And when it occursbasis risk appear and make it imperfect hedging rather the perfect hedging.So if a hedger want to hedge against its portfolio risk the value of the port folio will be like:St1C-N(ft1,t2-ft0,t2)Q (Apte, 2006) (2)Where :st1 = spot price at time 1Ft1,t2 = futures price of 1 foreign currency at time t1 for settlement at time’t2’Ft0,t2 = futures price of 1 foreign currency at time ‘t0’ for settlement at time’t2’N= number of futures contractC=Cash amount to be hedgeQ= Size the contractIf we divide equation (2) by C ,we can find hedge ratio like: β=and the equation (2) can be written like: -β( - ) (3) Page 15
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430and variance of equation(3) will be like below: Var ( ) - 2 βCov ( , )+ Var ( ) (4)so the hedge ratio for sorting future contract which is beta coefficient defined as : β= (5)so once we have estimate beta or hedge ratio hedgers can find optimal contract number by: N= β (6)Regression model :Our Autoregressive model or AR(1)which can be express like below: ΔSt1=α+βΔFt1,t2+ ut (Apte,P,G ,2006) (7)Where ΔSt1=change in spot exchange rate at time 1,Alpha α= intercept or constant ,ΔFt1,t2= change in future exchange rate at time t1 of future contract maturing at t2β= slope coefficient for minimum variance hedge ratio, and thefirst order Autoregressive scheme in here , ut= ρut-1+ εt , -1<ρ<1 (8) there for E(ut)= ρE(ut-1)+E(εt)=0 var(ut)= ρ2 var(ut-1)+var(εt) (N.Gujarati, 2003)the u’s and ε’s are uncorrelated and εt it the normal error term in regression model . Page 16
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Historical data has been collected for the time series regression although theory says thismodel should be estimated as forecast but the data need to forecast above equation is notavailable. So our dependent Variable is Change in Spot Price which is denoted as ΔS andour Independent Variable Change in Futures Price which is denoted as ΔF.Null hypothesis isthere is no relationship between ΔSpot price and ΔFutures Price. And the Alternativehypothesis is there is relationship between ΔSpot price and ΔFutures Price.In order to examine whether there is serial correlation between the error terms, we haveapplied Durbin- Watson test because many a times regressions of time series data have theproblem of positive autocorrelation, the hypotheses in Durbin-Watson test is in below : Null hypothesis : ρ=0 Alternative hypothesis: ρ>0Durbin-Watson statistic is in below: (9) (N.Gujarati, 2003)Decision Criteria  If t-probability value is more then 5% we accept the null hypothesis.  If t-probability value is less than 5% we reject the null hypothesis.  R2 coefficient of determination . Page 17
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430  Probability (F-Statistic)or test statistic which is rusticated and unrestricted regression if it is more then 5% we accept the null hypothesis if less we reject the null hypothesis.  Durbin-Watson statistics or ‘D’test decision rules are below: Null hypothesis Decision if There is No positive autocorrelation reject 0<d<dL There is No positive autocorrelation no decision dL<d<dU There is No negative correlation reject 4-dL<d<4 There is No negative correlation no decision 4-dU<d<4-dL There is no autocorrelation positive or negative do not reject dU<d<4-dUWhere du is devaluated upper value and dL is devaluated lower value(N.Gujarati, 2003) .Wewill follow Durbin-Watson d statistic table for level of significance points of d L and dU at 5%level.We have used this model because there are some drawback with simple regression model .Measurement of Hedging effectiveness :Ederington(1979)suggested that hedging effectiveness is equal to R 2 of the OLS regression inother words R2 of the regression line explaining the data if high then hedging is effective,so the higher the R2 the higher the minimum variance hedge . so we can measure hedgingeffectiveness by R2 in our regression model .Change in spot price to the change in futuremeasured by the correlation coefficient .in our analysis R 2 which is squire of the correlationcoefficient is been applied which is denoted as : Page 18
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430R2 = =1-if R2 80% or 0.80 then it would mean the variation in dependent variables which is ΔShas been explained be the independent variable which is ΔF .when R2 is low for instanceless then 50% or .50 then for hedgers it would be not wise to use that currency futurescontract to hedge . if it is less then 80% or .80 the hedging effectiveness is inefficient .3.1 Data Description :We have collected data from ‘DATASTREAM TOMASON REUTERS’ and USD is the basecurrency. We have collected USD/EUROFX, USD/SWISS-FRANC, USD/GBP,USD/MEXICANPESO,USD/YEN. Five days a week basis daily Futures contracts settlement price and spotexchange rate of those futures. We have been taken direct quote which means units of USDfor one unit of foreign currency (EUROFX, SWISS FRANC, GBP, MEXICAN PESO and YEN). ForYEN however units of USD for 100 Japanese yen is been taken into consideration .Becausecompare to other currency one unit of foreign currency Japanese yen is too low againstUSD. For March settlement from 16th March, 2001 to16th March, 2009 .for June settlement form 15th June, 2001 to 15th March, 2009 .forSeptemberSettlement from 14th September, 2001 to 14th September 2009 .for December settlementfrom14th December, 2001 to 14th December, 2009.The Number of observation table below: Page 19
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Number of observation in our sample:Quotation March settlement June settlement August settlement December settlementUSD/EUROFX 2086 2086 2086 2085USD/SWISS FRANC 2086 2086 2086 2086USD/GBP 2082 2086 2086 2086USD/MEXICANPESO 2086 2086 2086 2086USD/YEN 2086 2086 2086 2086We have used daily data because of currency fluctuation in spot and futures and futuresmarketParticipant re equilibriums their position daily basis also in currency futures market there ismarginal cost involved daily basis.For technical reason due to problems of stationary or nonstationarity in mean and varianceof price level in data series effect futures price unpredictability that’s why we haveestimated hedge ratio(β) based on natural logarithm changes in the spot market rather thanon the actual rate. Stationarity and related problem such as cointegrastion can be overcomebe using this method . Cavanaugh,K,L(1987) mention that raw price and natural logarithmof future is big issue for convenience. Logarithm of first difference of futures prices or pricechange or returns in a sample will have a better distribution then the first difference of theraw series and its more convenient to base hypothesis testing on the first difference ofnatural logarithm of prices .Moreover the futures prices are quoted in terms of units of USD for per foreign currency orper USD unit to foreign currency units will not significantly affect the analysis. Page 20
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430All the currency we have taken to analysis play a vital role in currency and other financialfutures market as well as in global economy. That’s why we have chosen all this currency.4. Empirical Result and Analysis:we have used ‘EViews 6 ‘ statistical software to calculate our regression all the result belowis the out put of EViews 6.From our regression model we have found that USD/EUROFX,USD/SWISS FRANC andUSD/GBP All this futures contracts in four different settlement date are significant exceptDecember settlement for USD/EURO is not significant when we measure with R2 for goodhedging effectiveness and also we also found that there is relationship between spot andfuture price changes .One the other hand USD/Mexican Peso and USD/YEN futurescontracts non of the four different settlement dates are insignificant when we measure thehedging effectiveness with R2. So we can not measure hedging effectiveness because R2 islow for We have the out put form EView 6 and we have interpreter the result below.Appendix No-1 :March Settlements:March Settlement(USD/EUROFX): Dependent Variable: _SPOT_EUROFX Method: Least Squares Date: 04/29/10 Time: 16:41 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 6 iterations Page 21
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Coefficient Std. Error t-Statistic Prob. C 0.000706 0.003404 0.207372 0.8357 _FUTURES_EUROFX 0.951744 0.007177 132.6172 0.0000 AR(1) -0.408963 0.020278 -20.16787 0.0000 R-squared 0.880260 Mean dependent var 0.017567 Adjusted R-squared 0.880145 S.D. dependent var 0.632154 S.E. of regression 0.218852 Akaike info criterion -0.199403 Sum squared resid 99.72005 Schwarz criterion -0.191284 Log likelihood 210.8773 Hannan-Quinn criter. -0.196428 F-statistic 7652.842 Durbin-Watson stat 2.252983 Prob(F-statistic) 0.000000 Inverted AR Roots -.41The Intercept (α)is 0.000706 and the slope coefficient (β) is 0.951744.T-probability is 0.0000,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level , Durbin-Watson statistics implies there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.880260which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position by using a sort position in future contractthe hedge ratio is 0.951744.which implies that 0.951744 units of the future asset to sell1unit of the spot asset held.March Settlement(USD/SWISS FRANC) Dependent Variable: _SPOT_SWISS Method: Least Squares Date: 04/29/10 Time: 16:43 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 4 iterations Coefficient Std. Error t-Statistic Prob. C 0.001283 0.004117 0.311670 0.7553 _FUTURE_SWISS 0.920558 0.007953 115.7521 0.0000 Page 22
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 AR(1) -0.419374 0.019997 -20.97210 0.0000 R-squared 0.850855 Mean dependent var 0.017433 Adjusted R-squared 0.850712 S.D. dependent var 0.690180 S.E. of regression 0.266671 Akaike info criterion 0.195832 Sum squared resid 148.0576 Schwarz criterion 0.203951 Log likelihood -201.1552 Hannan-Quinn criter. 0.198807 F-statistic 5938.785 Durbin-Watson stat 2.119732 Prob(F-statistic) 0.000000 Inverted AR Roots -.42The Intercept (α) is 0.001283 and the slope coefficient (β) is 0.920558. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.850855which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.920558.which implies that 0.920558 units of the future asset to sell 1unitof the spot asset held.March Settlement(USD/GBP) Dependent Variable: _SPOTPOUND Method: Least Squares Date: 04/29/10 Time: 16:45 Sample (adjusted): 9 2087 Included observations: 2079 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C -0.000389 0.004197 -0.092556 0.9263 _FUTURES_POUND 0.896710 0.008657 103.5836 0.0000 AR(1) -0.295938 0.021332 -13.87291 0.0000 Page 23
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 R-squared 0.823281 Mean dependent var -0.000910 Adjusted R-squared 0.823111 S.D. dependent var 0.589726 S.E. of regression 0.248028 Akaike info criterion 0.050891 Sum squared resid 127.7111 Schwarz criterion 0.059029 Log likelihood -49.90144 Hannan-Quinn criter. 0.053873 F-statistic 4835.745 Durbin-Watson stat 2.154171 Prob(F-statistic) 0.000000 Inverted AR Roots -.30The Intercept (α) is -0.000389 and the slope coefficient (β) is 0.896710. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level ,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.818409 which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.896710.which implies that 0.896710units of the future asset to sell 1unit ofthe spot asset held.March Settlement(USD/MEXICAN –PESO ) Dependent Variable: _SPOT_PESO Method: Least Squares Date: 04/29/10 Time: 16:48 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C -0.007364 0.009429 -0.780915 0.4349 _FUTURE_PESO 0.612525 0.015878 38.57706 0.0000 AR(1) -0.300972 0.021192 -14.20226 0.0000 R-squared 0.410741 Mean dependent var -0.019036 Adjusted R-squared 0.410175 S.D. dependent var 0.728979 S.E. of regression 0.559856 Akaike info criterion 1.679165 Sum squared resid 652.5806 Schwarz criterion 1.687284 Page 24
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Log likelihood -1747.530 Hannan-Quinn criter. 1.682140 F-statistic 725.6259 Durbin-Watson stat 2.031475 Prob(F-statistic) 0.000000 Inverted AR Roots -.30The Intercept (α) is -0.007364 and the slope coefficient (β) is 0.612525.T-probabilityis,0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level ,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.410741 which is insignificant and in previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So the slope coefficient hedge ratio β in not very effective because it is far form unity andR2 is very low which indicates our model outcome is insignificantMarch Settlement(USD/YEN) Dependent Variable: _SPOT_YEN Method: Least Squares Date: 04/29/10 Time: 16:46 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.003241 0.005070 0.639132 0.5228 _FUTURE_YEN 0.873119 0.009910 88.10775 0.0000 AR(1) -0.337895 0.020797 -16.24696 0.0000 R-squared 0.776680 Mean dependent var 0.010719 Adjusted R-squared 0.776465 S.D. dependent var 0.655062 S.E. of regression 0.309710 Akaike info criterion 0.495075 Sum squared resid 199.7056 Schwarz criterion 0.503194 Log likelihood -513.1157 Hannan-Quinn criter. 0.498050 F-statistic 3620.467 Durbin-Watson stat 2.120026 Prob(F-statistic) 0.000000 Inverted AR Roots -.34 Page 25
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430The Intercept (α) 0.003241is and the slope coefficient (β) 0.873119 isT-probability is 0.0000and Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level ,Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R 2 is 0.776680which is close to significant level .So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.873119.which implies that 0.873119 units of the future asset to sell 1unitof the spot asset held ,which Is the slope estimate in our regression. but it is just aboutefficient because of R2 ,which is bit lessAppendix No-2 :June Settlements:June Settlement (USD/EUROFX) Dependent Variable: _SPOT_EUROFX Method: Least Squares Date: 04/29/10 Time: 16:49 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 6 iterations Coefficient Std. Error t-Statistic Prob. C 0.000640 0.003428 0.186731 0.8519 _FUTURES_EUROFX 0.962152 0.007126 135.0245 0.0000 AR(1) -0.398660 0.020390 -19.55219 0.0000 R-squared 0.884800 Mean dependent var 0.022609 Adjusted R-squared 0.884689 S.D. dependent var 0.644055 S.E. of regression 0.218704 Akaike info criterion -0.200754 Sum squared resid 99.58538 Schwarz criterion -0.192635 Log likelihood 212.2860 Hannan-Quinn criter. -0.197779 F-statistic 7995.469 Durbin-Watson stat 2.241569 Prob(F-statistic) 0.000000 Inverted AR Roots -.40 Page 26
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430The Intercept (α) is 0.000640and the slope coefficient (β) is 0.962152.T-probability is,0.0000,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 0.884800 which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position by using a sort position in future contractthe hedge ratio is 0.962152.which implies that 0.962152units of the future asset to sell1unit of the spot asset held.June Settlement(USD/SWISS) Dependent Variable: _SPOT_SWISS Method: Least Squares Date: 04/29/10 Time: 16:51 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 4 iterations Coefficient Std. Error t-Statistic Prob. C 0.001434 0.004057 0.353393 0.7238 _FUTURE_SWISS 0.932893 0.007723 120.7896 0.0000 AR(1) -0.410216 0.020082 -20.42700 0.0000 R-squared 0.861965 Mean dependent var 0.023373 Adjusted R-squared 0.861832 S.D. dependent var 0.702051 S.E. of regression 0.260959 Akaike info criterion 0.152528 Sum squared resid 141.7830 Schwarz criterion 0.160647 Log likelihood -156.0106 Hannan-Quinn criter. 0.155503 F-statistic 6500.562 Durbin-Watson stat 2.128661 Prob(F-statistic) 0.000000 Inverted AR Roots -.41 Page 27
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430The Intercept (α) is 0.001434and the slope coefficient (β) is 0.932893.T-probabilityis,0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level ,Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R 2 0.861965which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position by using a sort position in future contractthe hedge ratio is 0.932893.which implies that 0.932893units of the future asset to sell1unit of the spot asset held.June Settlement(USD/GBP) Dependent Variable: _SPOTPOUND Method: Least Squares Date: 04/29/10 Time: 16:50 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 4 iterations Coefficient Std. Error t-Statistic Prob. C 0.000569 0.004378 0.129977 0.8966 _FUTURES_POUND 0.899715 0.008708 103.3246 0.0000 AR(1) -0.267868 0.021465 -12.47951 0.0000 R-squared 0.824373 Mean dependent var 0.007117 Adjusted R-squared 0.824204 S.D. dependent var 0.604459 S.E. of regression 0.253438 Akaike info criterion 0.094041 Sum squared resid 133.7283 Schwarz criterion 0.102159 Log likelihood -95.03740 Hannan-Quinn criter. 0.097015 F-statistic 4886.324 Durbin-Watson stat 2.119985 Prob(F-statistic) 0.000000 Inverted AR Roots -.27The Intercept (α) is 0.000569 and the slope coefficient (β) is 0.899715.T-probabilityis,0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level , Page 28
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R2 0.824373 which is also significant because previousstudy suggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position by using a sort position in future contractthe hedge ratio is 0.899715.which implies that 0.899715 units of the future asset to sell1unit of the spot asset held.June Settlement(USD/MEXICAN-PESO) Dependent Variable: _SPOT_PESO Method: Least Squares Date: 04/29/10 Time: 16:50 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C -0.006908 0.009620 -0.718114 0.4728 _FUTURE_PESO 0.617963 0.015545 39.75266 0.0000 AR(1) -0.301190 0.021237 -14.18252 0.0000 R-squared 0.427173 Mean dependent var -0.018726 Adjusted R-squared 0.426623 S.D. dependent var 0.754430 S.E. of regression 0.571267 Akaike info criterion 1.719518 Sum squared resid 679.4524 Schwarz criterion 1.727637 Log likelihood -1789.597 Hannan-Quinn criter. 1.722493 F-statistic 776.3038 Durbin-Watson stat 2.073213 Prob(F-statistic) 0.000000 Inverted AR Roots -.30The Intercept (α) is -0.006908 and the slope coefficient (β) is 0.617963. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level, Page 29
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R 2 is 0.427173 which is not significant.So the slope coefficient hedge ratio β in not very effective and R 2 is very low which pointtowards insignificancy of our model.June Settlement(USD/YEN): Dependent Variable: _SPOT_YEN Method: Least Squares Date: 04/29/10 Time: 16:52 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.003458 0.006097 0.567235 0.5706 _FUTURE_YEN 0.819059 0.011938 68.60764 0.0000 AR(1) -0.377284 0.020352 -18.53782 0.0000 R-squared 0.666950 Mean dependent var 0.011078 Adjusted R-squared 0.666630 S.D. dependent var 0.663946 S.E. of regression 0.383350 Akaike info criterion 0.921702 Sum squared resid 305.9653 Schwarz criterion 0.929821 Log likelihood -957.8747 Hannan-Quinn criter. 0.924677 F-statistic 2084.661 Durbin-Watson stat 2.149022 Prob(F-statistic) 0.000000 Inverted AR Roots -.38The Intercept (α) is 0.003458 and the slope coefficient (β) is 0.819059 . T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.666950 .so our model is not very significant although itis more then 60%.So the slope coefficient hedge ratio β in not very effective and R2 is low which pointtowards insignificancy in our model. Page 30
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430Appendix No-3 :September Settlements:September Settlement(USD/EUROFX) Dependent Variable: _SPOT_EUROFX Method: Least Squares Date: 04/29/10 Time: 16:54 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 6 iterations Coefficient Std. Error t-Statistic Prob. C 0.000853 0.003289 0.259382 0.7954 _FUTURES_EUROFX 0.966677 0.006920 139.6919 0.0000 AR(1) -0.401376 0.020409 -19.66685 0.0000 R-squared 0.892258 Mean dependent var 0.021997 Adjusted R-squared 0.892154 S.D. dependent var 0.640285 S.E. of regression 0.210269 Akaike info criterion -0.279422 Sum squared resid 92.05145 Schwarz criterion -0.271303 Log likelihood 294.2972 Hannan-Quinn criter. -0.276447 F-statistic 8620.933 Durbin-Watson stat 2.259544 Prob(F-statistic) 0.000000 Inverted AR Roots -.40The Intercept (α) is 0.000853 and the slope coefficient (β) is 0.966677. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R 2 is 0.892258 which is highly significant .So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.966677which implies that 0.966677units of the future asset to sell 1unit ofthe spot asset held.September Settlement(USD/SWISS): Page 31
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Dependent Variable: _SPOT_SWISS Method: Least Squares Date: 04/29/10 Time: 16:57 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.001729 0.003946 0.438024 0.6614 _FUTURE_SWISS 0.932900 0.007483 124.6627 0.0000 AR(1) -0.396373 0.020039 -19.77989 0.0000 R-squared 0.870832 Mean dependent var 0.021081 Adjusted R-squared 0.870708 S.D. dependent var 0.699245 S.E. of regression 0.251429 Akaike info criterion 0.078124 Sum squared resid 131.6167 Schwarz criterion 0.086243 Log likelihood -78.44452 Hannan-Quinn criter. 0.081099 F-statistic 7018.287 Durbin-Watson stat 2.146087 Prob(F-statistic) 0.000000 Inverted AR Roots -.40The Intercept (α) is 0.001729 and the slope coefficient (β) is 0.932900. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level ,Durbin-Watson statistics there is no autocorrelation and the test of over all model whichis coefficient of determination ; R 2 is 0.870832is significant its more then 80%.So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.932900.which implies that 0.932900units of the future asset to sell 1unit ofthe spot asset heldSeptember Settlement(USD/GBP): Dependent Variable: _SPOTPOUND Method: Least Squares Page 32
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Date: 04/29/10 Time: 16:56 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.000587 0.003811 0.154039 0.8776 _FUTURES_POUND 0.916809 0.007831 117.0692 0.0000 AR(1) -0.343561 0.021039 -16.32949 0.0000 R-squared 0.853040 Mean dependent var 0.005885 Adjusted R-squared 0.852899 S.D. dependent var 0.609547 S.E. of regression 0.233784 Akaike info criterion -0.067399 Sum squared resid 113.7918 Schwarz criterion -0.059281 Log likelihood 73.26372 Hannan-Quinn criter. -0.064424 F-statistic 6042.561 Durbin-Watson stat 2.127850 Prob(F-statistic) 0.000000 Inverted AR Roots -.34The Intercept (α) is 0.000587 and the slope coefficient (β) is 0.916809. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5%level,Durbin-Watson statistics there is no autocorrelation and the test of over all modelwhich is coefficient of determination ; R 2 is 0.853040 Which is significant at 80% level .So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.916809 .which implies that 0.916809 units of the future asset to sell 1unitof the spot asset held .September Settlement(USD/MEXICAN PESO): Dependent Variable: _SPOT_PESO Method: Least Squares Date: 04/29/10 Time: 16:55 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Page 33
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Coefficient Std. Error t-Statistic Prob. C -0.005475 0.008889 -0.615883 0.5380 _FUTURE_PESO 0.693300 0.014785 46.89230 0.0000 AR(1) -0.313061 0.020970 -14.92922 0.0000 R-squared 0.503494 Mean dependent var -0.017060 Adjusted R-squared 0.503017 S.D. dependent var 0.755745 S.E. of regression 0.532778 Akaike info criterion 1.580012 Sum squared resid 590.9798 Schwarz criterion 1.588131 Log likelihood -1644.163 Hannan-Quinn criter. 1.582987 F-statistic 1055.650 Durbin-Watson stat 2.028881 Prob(F-statistic) 0.000000 Inverted AR Roots -.31The Intercept (α) is -0.005475 and the slope coefficient (β) is 0.693300. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.503494 and its not significant for effective hedging .September Settlement(USD/YEN): Dependent Variable: _SPOT_YEN Method: Least Squares Date: 04/29/10 Time: 16:57 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.002894 0.005018 0.576863 0.5641 _FUTURE_YEN 0.881168 0.009734 90.52467 0.0000 AR(1) -0.349288 0.020634 -16.92803 0.0000 R-squared 0.785546 Mean dependent var 0.012407 Adjusted R-squared 0.785340 S.D. dependent var 0.667079 S.E. of regression 0.309067 Akaike info criterion 0.490919 Sum squared resid 198.8774 Schwarz criterion 0.499038 Log likelihood -508.7832 Hannan-Quinn criter. 0.493894 F-statistic 3813.195 Durbin-Watson stat 2.116231 Prob(F-statistic) 0.000000 Inverted AR Roots -.35 Page 34
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430The Intercept (α) is 0.002894 and the slope coefficient (β) is 0.881168. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ;R2 is 0.785546. Which is very close to be significant at 80%level but its not significant for effective hedging .Appendix No-4 :December Settlements:December Settlement(USD/EUROFX): Dependent Variable: _SPOT_EUROFX Method: Least Squares Date: 04/29/10 Time: 16:59 Sample (adjusted): 3 2087 Included observations: 2082 after adjustments Convergence achieved after 8 iterations Coefficient Std. Error t-Statistic Prob. C 0.001196 0.005745 0.208236 0.8351 _FUTURES_EUROFX 0.843212 0.012067 69.87884 0.0000 AR(1) -0.421089 0.019974 -21.08237 0.0000 R-squared 0.655401 Mean dependent var 0.019750 Adjusted R-squared 0.655070 S.D. dependent var 0.633621 S.E. of regression 0.372130 Akaike info criterion 0.862295 Sum squared resid 287.9022 Schwarz criterion 0.870424 Log likelihood -894.6495 Hannan-Quinn criter. 0.865274 F-statistic 1977.051 Durbin-Watson stat 2.247048 Prob(F-statistic) 0.000000 Inverted AR Roots -.42The Intercept (α) is 0.001196 and the slope coefficient (β) is 0.843212. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination R2 is 0.655401. which implies insignificance and of hedge ratiois not effective. Page 35
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430December Settlement (USD/SWISS): Dependent Variable: _SPOT_SWISS Method: Least Squares Date: 04/29/10 Time: 17:01 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.001694 0.003861 0.438840 0.6608 _FUTURE_SWISS 0.927912 0.007334 126.5233 0.0000 AR(1) -0.413676 0.020056 -20.62558 0.0000 R-squared 0.872995 Mean dependent var 0.022045 Adjusted R-squared 0.872873 S.D. dependent var 0.698425 S.E. of regression 0.249022 Akaike info criterion 0.058891 Sum squared resid 129.1094 Schwarz criterion 0.067009 Log likelihood -58.39347 Hannan-Quinn criter. 0.061865 F-statistic 7155.526 Durbin-Watson stat 2.146225 Prob(F-statistic) 0.000000 Inverted AR Roots -.41The Intercept (α) is 0.927912and the slope coefficient (β) is 0.927912 . T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% leveland the test of over all model which is coefficient of determination ; R 2 is 0.872995 .which isalso significant because previous study suggest that R2 should be between 80% to 99% forhedging effectiveness.So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.927912.which implies that 0.927912 of the future asset to sell 1unit of thespot asset held. Page 36
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430December Settlement(USD/GBP): Dependent Variable: _SPOTPOUND Method: Least Squares Date: 04/29/10 Time: 17:01 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.000553 0.003754 0.147249 0.8829 _FUTURES_POUND 0.924250 0.007641 120.9631 0.0000 AR(1) -0.343212 0.021017 -16.33005 0.0000 R-squared 0.860562 Mean dependent var 0.005372 Adjusted R-squared 0.860428 S.D. dependent var 0.616230 S.E. of regression 0.230219 Akaike info criterion -0.098131 Sum squared resid 110.3480 Schwarz criterion -0.090012 Log likelihood 105.3011 Hannan-Quinn criter. -0.095156 F-statistic 6424.682 Durbin-Watson stat 2.158405 Prob(F-statistic) 0.000000 Inverted AR Roots -.34The Intercept (α) is 0.000553 and the slope coefficient (β) is 0.924250. T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% levelDurbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.860562 which is also significant because previous studysuggest that R2 should be between 80% to 99% for hedging effectiveness.So if a investor wishes to hedge a long position using a sort position in future contract thehedge ratio is 0.924250 .which implies that 0.924250 units of the future asset to sell 1unitof the spot asset held.DECEMBER SETTLEMENT(USD/MEXICAN PESO): Page 37
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Dependent Variable: _SPOT_PESO Method: Least Squares Date: 04/29/10 Time: 17:00 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C -0.004643 0.009139 -0.508060 0.6115 _FUTURE_PESO 0.700378 0.015305 45.76281 0.0000 AR(1) -0.301254 0.021154 -14.24123 0.0000 R-squared 0.495637 Mean dependent var -0.015953 Adjusted R-squared 0.495152 S.D. dependent var 0.763990 S.E. of regression 0.542835 Akaike info criterion 1.617416 Sum squared resid 613.5032 Schwarz criterion 1.625535 Log likelihood -1683.156 Hannan-Quinn criter. 1.620391 F-statistic 1022.989 Durbin-Watson stat 2.027546 Prob(F-statistic) 0.000000 Inverted AR Roots -.30The Intercept (α) is -0.004643 and the slope coefficient (β) is 0.700378 . T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R2 is 0.495637 which is not significant for effective hedging.DECEMBER SETTLEMENT(USD/YEN): Dependent Variable: _SPOT_YEN Method: Least Squares Date: 04/29/10 Time: 17:02 Sample (adjusted): 3 2087 Included observations: 2085 after adjustments Convergence achieved after 5 iterations Coefficient Std. Error t-Statistic Prob. C 0.002849 0.004999 0.569973 0.5688 _FUTURE_YEN 0.891447 0.009596 92.90117 0.0000 AR(1) -0.337976 0.020802 -16.24764 0.0000 R-squared 0.794859 Mean dependent var 0.017479 Page 38
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Adjusted R-squared 0.794662 S.D. dependent var 0.673689 S.E. of regression 0.305277 Akaike info criterion 0.466242 Sum squared resid 194.0296 Schwarz criterion 0.474360 Log likelihood -483.0569 Hannan-Quinn criter. 0.469216 F-statistic 4033.568 Durbin-Watson stat 2.125043 Prob(F-statistic) 0.000000 Inverted AR Roots -.34The Intercept (α) is 0.002849 and the slope coefficient (β) is 0.891447 .T-probability is0.0000 ,Probability (F-Statistic)is 0.0000000 which means both are significant at 5% level,Durbin-Watson statistics there is no autocorrelation and the test of over all model which iscoefficient of determination ; R 2 value 0.794859which is almost good fit and effectivehedging .5. CONCLUSION :In this study hedging effectiveness obtained form regression model and March, June,September, December settlement for USD/EUROFX, USD/SWISS FRANC, USD/GBP we havefound satisfactory result for hedging effectiveness which we measured by R 2 andUSD/MEXICAN PESO and USD/YEN currencies all the settlements hedging effectiveness isnot satisfactory . we have applied minimum variance delta hedge for all the settlementswhich is for mismatch in maturity in futures contracts but hedgers should keep in mind thatfor currency mismatch they should concern about minimum variance cross hedge and whennone of them match with the contract they should concern with minimum variance deltacross hedge. Its very important for hedger to up-to-date there knowledge about all theinformation about currency they are hedging, what’s the interest rate going to prevail in thetime when the amount is payable and receivable ,balance of payment, supply and demand Page 39
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430for foreign currency etc. if a hedger or investor manage to follow above mentioned issuethere is greater chance that he or she will be able to perfectly eliminate the foreign currencyrisk exposure and add value to their firm and themselves .5.1 Further suggestion for research :Further study can be conducted for minimum variance cross hedge and minimum variancedelta cross hedge and also by using different models like Vector auto regression model,Vector error correction model ,Autoregressive moving average model ,Generalizedautoregressive conditional Heteroskedasticity model etc.In our regression model we have used historical data for analysis which can be biasedbecause historical data or past can not always predict futures and any model can becomemeaningless or valueless recent credit crunch is the best example to illustrate this issue. Page 40
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Bibliography1. Adams,j.& Montesi,C,J. (1995). "Major Issues Related to Hedging Accounting ,Financial Accounting". "Financial Accounting Standard Board",Newark,Connecticut .2. Akin, R. M. (2003)." Maturity Effects In Futures Market:Evidence from Eleven Financial Future Markets". Unpublished Article .3. Allayannis,G,S and Ofek,Eli. (1997)." Exchange Rate Exposure, Hedging, And the Use Of Foreign Currency Derivatives".4. Allyayannis, G,S. & Weston,J. (1998)." The Use of Foreign Currency Derivatives and Firm Market Value."5. Apte, P. G. (2006)". International Financial Management,Fourth Edition. McGraw- Hill."6. Baillie,R, T.& Myers, R, J. (1991). "Bivariate Garch Estimation of the Optimal Commodity Futures Hedge"." Journal Of Applied Econometrics" , 6 (2), 109-124.7. Bodie,Z.,kane,A.,Marcus,A,J. (2008)." investments",Seventh Edition,International Edition .8. Bodnar,G,M.,Hayt,G,S.,Marston,R,C. (1998). "1998 Wharton Survey of Financial Risk Management by US Non-Financial Firms"." Financial Management" , 27 (4).9. Bollerslev, T. (1986)." Generalized Autoregressive Conditional heteroskedasticity"." Journal Of Econometrics" , 31 (3), 307-327.10. Brailsford,T ., Corrigan,K.,and Heaney,R. (2001)." A Comparison Of Measures Of Hedging Effectiveness:A Case Study Using The Australian All Ordinaries Share Price Page 41
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 4430 Index Futures Contract"." Journal Of Multinational Financial Management" , 11 (4-5), 465-481.11. C.Hull, J. (1998). "Introduction to Futures and Option Market" ,third edition . prentice –hall,inc.12. Carbaugh, R,j. (2009). "International Economies",Twelfth Edition.13. Cavanaugh,K,L. (1987). "Price Dynamics in Foreign Currency" ." Journal of International Money and Finance ", 6 (3), 295-314.14. Chang, J. S. K. and Shanker, L. (1986)." Hedging Effectiveness of Currency Options and Currency Futures"." Journal of Futurers Market" , 6 (2), 289-305.15. Clark, E. (2002). "International Finacne",Second edition. thomason.16. Ederington, Louis H. (1979). "The hedging Performance of the New Futures Market"." The Journal Of Finance" , 34 (1), 157-170.17. Elliott,W,B.,Huffman,S,P.,Makar,S,D. (2003)." Foreign-denominated debt and foreign currency derivatives: complements or substitutes in hedging foreign currency risk?". "Journal of Multinational Financial Management" , 13 (2), 123-139.18. Engle,R,F. (1982)." Autoregressive Conditional Heteroscedasticity With Estimaties Of The Variance Of United Kingdom Inflation"." Econometrica" , 50 (4), 987-1007.19. Floros C and Vougas D, V. (2006). Christos Floros and Dimitrios V.Vougas “Hedging Effectiveness in Greek stock index future market ,1999-2001"." International Reearch Journal of Finance and Economics" (5).20. Geczy,C., Minton,B,A., and Schrand,C,. (1997). "Why firms Use Currency Derivatives. The Journal of Finance" , 52 (4), 1323-1354.21. Herbst,A,F.,Kare,D,D.,Marshall,J,F. (1997). "A Time Varying Convergence Adjusted Hedge Ratio Model. Advances In Futures And Option Research ", Unpublished Article. Page 42
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 443022. Johnson, L. (1960). "The Theory OF Hedging And Speculation IN Commodity Futures. The Review Of Economic Studies" , 27 (3), 139-151.23. Kenneth, F.K .,& Sultan ,J. (1993)." Time-Varying Distributions And Dynamic Hedging With Foreign Currency Future. Jounral Of Financial And Quantitave Analysis" , 28 (4), 535-551.24. Lien,D.,Yang,L. (2009)." Effects Of Omitting Information Variables On Optimal Hedge Ratio Estimation:A Note."25. Liouia,A., & Poncet,P. (2003)." Dynamic Asset Pricing With Non-Redundant Forwards". "Journal of Economic Dynamics and Control" , 27 (7), 1163-1180.26. Markowitz, H. (1952)." Portfolio Selection"." The Journal Of Finance" , 7 (1), 77-91.27. Marmer, H, S. (1986). "Portfolio Model Hedging With Canadian Dollar Futures: A Framework For Analysis"." Jounral Of Futures Market" , 6 (1), 83-92.28. Moosa,I.A. (2003). "The sensitivity of the Optimal Hedge Ratio to Model Specification". Financie Letters , 1, 15-20.29. Moschinia,G. & Myers,R,J. (2002)." Testing For Constant Hedge Ratios In Commodity Markets: A Multivariate GARCH Approach"." Journal Of Empirical Finance" , 9 (5), 589-603.30. Myers,R.J. (1991)." Estimatin time-varying hedge ratios on futures market"." Journal of Futures Market ", 11 (1), 39-53.31. N.Gujarati, D. (2003)." Basic Econometrics", Fourth edition. McGraw-Hill.32. Nguyen,H & Faff,R. (2003). "Can the use of foreign currency derivatives explain variations in foreign exchange exposure?: Evidence from Australian companies. Journal of Multinational" "Financial Management" , 13 (3), 193-215. Page 43
    • CURRENCY FUTURES HEDGING EFFECTIVENESS IN CME GROUP FIN 443033. Nguyena,H.,Faff,R., Marshall,A. (2007). "Exchange Rate Exposure, Foreign Currency Derivatives And The Introduction Of The Euro: French Evidence"." International Review Of Economcs & Finance ", 16 (4), 563-577.34. Pok ,W, C. , poshakwale,S,S., Ford,J,L. (2009). "Stock Index futures Hedging In The Emerging Malaysian Market"." Global Finance Journal" , 20 (3), 273-288.35. Terry, E. (2007)." Inverse Currency Futures Hedging". Unpublished Article .36. Tingting Y., Zongye C. (2006)." The Hedging Effectiveness Of Currency Futures". Unpublished Article .37. Wang,C.,& Low,S,S. (2003)." Hedging With Foreign Currency Denominated Stock Index Futures: Evidence From The MSCI Taiwan Index Futures Market. Journal of Multinational" "Financial Management" , 13 (1), 1-17.38. Wasendorf, R. R. (2001)." All about futures". Mc Graw-Hill.39. Working, H. (1953). "Futures Trading And Hedging"." The American Economic Review" , 43 (3), 314-343.40. http://www.cmegroup.com/trading/fx/41. http://www.jstor.org/42. http://www.sciencedirect.com/ Page 44