hedge effectivenes

44 TESTING HEDGE EFFECTIVENESS FALL 2001

Reporting changes in the fair valueof a derivative each quarter with-out offsetting changes in thevalue of the hedged item could

create a matching problem because changes inthe value of the derivative could appear in theincome statement several periods before thechange in the item that the derivative is hedg-ing. Unless the method of accounting allowsfor an adjustment of this potential mismatch,hedging might increase (or decrease) reportedearnings in one period and have the oppositeeffect sometime later, thus distorting earn-ings. The Financial Accounting StandardsBoard has taken steps to prevent this problemfrom happening. Under FASB Statement 133,Accounting for Derivative Instruments and Hedg-ing Activities, as amended by FASB Statement138, Accounting for Certain Derivative Instru-ments and Certain Hedging ActivitiesAnAmendment of FASB Statement No. 133, enti-ties are permitted to match the timing of thegains and losses of hedged items and theirhedging derivatives, but only if, and to theextent that, the derivative qualifies as a highlyeffective hedge.1

PURPOSE OF HEDGE ACCOUNTING

The FASBs special hedge-accountingrules can alleviate at least some of the poten-tial impact that changes in fair value may haveon the derivative users reported earnings whenthe derivative qualifies as a highly effective

hedge. In principle, a hedge is highly effectiveif the changes in the fair value (or cash flow)of the hedged item and the hedging derivativeoffset each other to a significant extent. Underhedge accounting, an entity that has a quali-fying fair value hedge records currently inearnings the gain or loss on the derivative andthe change in the fair value of the hedgeditem attributable to the hedged risk (with thecarrying amount of the hedged item adjustedaccordingly). An entity that has a qualifyingcash flow hedge includes in other compre-hensive income (OCI) the effective portion ofany changes in the hedging derivatives fairvalue, until the entity recognizes the hedgeditem in earnings.

Defining a method for measuring hedgeeffectiveness and determining whether a hedg-ing relationship is highly effective are impor-tant aspects of hedge accounting. Initially, theBoard wanted to require specific effectivenesstests. However, given the difficulty of devel-oping methods that would accommodate thewide variety of risk-management objectivesand strategies that exist in practice, the Boardultimately decided not to prescribe specificeffectiveness tests. Instead, the Board allows anentity to develop its own method for measur-ing and assessing hedge effectiveness, providedthat the method is reasonable and based on anentitys risk-management objective, as well asapplied consistently to all similar hedges.

Consequently, a variety of tests are avail-able for assessing hedge effectiveness. This arti-

Testing Hedge EffectivenessJOHN M. ALTHOFF AND JOHN D. FINNERTY

JOHN M. ALTHOFFis a partner at Pricewater-houseCoopers LLP,Florham Park, NJ. Hemay be contacted [email protected]

JOHN D. FINNERTYis a partner at Pricewater-houseCoopers LLP,New York and profes-sor of finance at Ford-ham University. Hemay be contacted at [email protected]

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cle describes the dollar offset method of assessing hedgeeffectiveness, as well as the two possible statisticalapproaches: 1) the regression method, and 2) the variance-reduction method. The appropriate application of thesemethods in a proposed hedging relationship requires con-siderable judgment. Therefore, before implementinghedges, preparers of financial statements should discusswith their auditors the methods they would like to employ.The manner in which hedge effectiveness is to be testedand the critical values for determining whether a proposedhedge can be considered highly effective are among thematters that should be discussed with the auditors, whomust concur with managements choices. Accordingly, thisarticle does not attempt to promote the acceptability ofany of these tests. Each entity must determine for itselfthe method that is appropriate to use when testing hedgeeffectiveness for a specific hedging relationship.

TESTING HEDGE EFFECTIVENESS

Before a derivative position can qualify for hedgeaccounting, an entity must specify the hedged item, iden-tify the hedging strategy and the derivative, and documentthe basis for its expectation that the hedge will be highlyeffective in offsetting the designated risk exposure. We usethe term hedged item throughout this article to refer toeither 1) an asset or a liability or 2) a prospective cashinflow or outflow. Similarly, we use the term derivative torefer to any derivative instrument or combination ofderivative instruments that is used to hedge changes in fairvalue or cash flow. We use the term hedged position to referto the combination of a hedged item and a derivative. Thehedged item can be 1) a designated portion of an asset ora liability or 2) a designated expected future cash flow (suchas a certain interest payment on a floating-rate note or theprice to be paid for an expected purchase of a commod-ity) that is attributable to a particular risk. The effective-ness test is applied to the designated item and to thespecific risk that is being hedged.

To properly assess hedge effectiveness, an entitymust perform both prospective and retrospective testing.The prospective testing must be done before an entityenters into the hedge, as well as on an ongoing basis, tojustify the continuing expectation that the hedging rela-tionship will be highly effective in future periods. Anentity must regularly perform retrospective testing todetermine how effective the hedging relationship actu-ally has been in achieving offsetting fair values or cashflows. Throughout the life of the hedge, retrospective test-

ing should be performed at least quarterly or each timea financial statement is issued or earnings are reported.Although the retrospective test must be based on theactual results of the hedge, an entity may also draw onhistorical data if the defined length of the test periodexceeds the length of the period during which the hedgehas been in place. Before implementing the hedge, anentity must specify the method of retrospective testing andthe length of the testing period, as well as perform theinitial prospective testing. FAS 133, however, does per-mit an entity to use different methods for prospective andretrospective testing, provided that it documents the cho-sen methods before implementing the hedge.2

FAS 133 requires that an entity measure the inef-fective part of a hedge each quarter and, depending on thenature of the hedge, possibly recognize in current earn-ings the gain or loss associated with the ineffective portionof the hedge. Thus, even if a hedge is determined to behighly effective, there may be an impact on current earn-ings to the extent that the offset of the hedged risk is notexact. In the case of a fair value hedge, an entity shoulddetermine the amount of hedge ineffectiveness for account-ing recognition by directly comparing the differencebetween change in the fair value of the hedged itemattributable to the hedged risk and the change in fair valueof the derivative. For a cash flow hedge, the hedge inef-fectiveness that an entity recognizes in current earnings islimited to the excess of the cumulative change in the fairvalue of the hedging derivative over the cumulative changein the present value of the hedged items expected futurecash flows. In addition, if the entity has elected to excludea component of the derivatives change in fair value fromthe assessment of effectiveness (e.g., the change in thetime value of the option contract when the effectivenessof the option hedge is based on changes in the optionsintrinsic value), the change in the fair value of the excludedcomponent must be recognized directly in earnings.

Appendix A, Section 2 of FAS 133 provides imple-mentation guidance on hedge-effectiveness testing.Although FAS 133 requires that entities use statistical orother numerical techniques when testing a hedges effec-tiveness (unless a specific exception applies), it does notendorse a specific testing method. An entity must selectthe method, choose the measurement period, and spec-ify an appropriate test statistic, along with the criticalvalue that will distinguish a highly effective hedge froma hedge that is not highly effective.

FAS 133 also does not specify a bright-line test thatcan distinguish highly effective hedges from less effective

FALL 2001 FAS 133 AND THE NEW DERIVATIVES ACCOUNTING LANDSCAPE 45

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(or ineffective) hedges; therefore, an entity must exercisejudgment when assessing whether a specific hedging rela-tionship is expected to be highly effective. The Board did,however, state that the high-effectiveness requirement isessentially the same as the high-correlation requirementspecified in FASB Statement 80, Accounting for FuturesContracts. In practice, the term high correlation hasbeen interpreted as meaning that either 1) the cumula-tive changes in the hedging derivative should offsetbetween 80% and 125% of the cumulative changes in thefair value or cash flows of the hedged item3 or 2) theregression relationship between the changes in the hedgeditem and the derivative should have an adjusted R2 of atleast 80%.4

FAS 133 allows entities flexibility in choosing thelength of the testing interval (e.g., daily, weekly, monthly,etc.). It also allows entities flexibility in selecting the num-ber of data points when they are prospectively testingwhether a hedging relationship is expected to be highlyeffective. Some people have recommended that the test-ing interval match the time horizon of the hedge.5 How-ever, matching the hedges time horizon sometimes maylimit the number of independent observations that are avail-able for statistical testing or may result in the use of datathat have questionable value. For example, obtaining 30independent data points to test a one-year hedge based onhistorical data would require 30 years of data, which maynot be available or may extend so far back that it includesdata unrepresentative of the current environment. Thus,considerable judgment must be applied in selecting the test-ing interval and the number of data points.

Before implementing a hedge, an entity that wishesto have its derivative qualify for hedge accounting must, withone important exception, document that the derivative isexpected to be highly effective. The exception applies whenthe critical terms of the hedging derivative and the criticalterms of the entire hedged asset or liability (or the hedgedforecasted transaction) are the same, which exempts anentity from the requirement to perform a detailed analysisof hedge effectiveness. In that case, the entity can justifiablyexpect a complete offset of the risk that is being hedged.6

However, it is not always possible to achieve the perfecthedge; therefore, most entities will have to learn how tomeasure and assess hedge effectiveness.

AN EXAMPLE

To illustrate the process of determining whether ahedging relationship is expected to be highly effective, we

are applying three methods of testing hedge effectiveness(described in the remainder of this article) to a two-yearhedge, based on monthly data.7 To keep things simple, weare basing our example on the assumption that the hedgedposition is a forecasted purchase of a commodity and thatthe hedging derivative is a short futures position in arelated asset. Thus one should expect that an increase inthe value of the hedged item will be associated with anapproximately equal decrease in the value of the hedgingderivative (and vice versa) when the hedge is highly effec-tive. The combined change in the values of the two items,therefore, should be close to zero for a highly effectivehedge.

Assume that on June 1, 2001, an electric utility isconsidering hedging its future purchase of natural gas. Thedelivery date is June 1, 2003 (two years forward), and thenatural gas purchase contract specifies that the deliveredprice is the Houston Ship Channel Index price for thedelivery month. The gas purchaser intends to implementa two-year cash flow hedge using the New York Mer-cantile Exchanges Henry Hub Natural Gas Futures Con-tract. The spot price and the futures price are bothdenominated in dollars per million British thermal units($/MMBTU). Although the underlying commodity andpricing dates match in our example, the location or basisdifference between the hedged item (i.e., Houston ShipChannel) and the derivative (i.e., Henry Hub) preventsus from assuming that there will be no ineffectiveness.Therefore, testing must be performed to support theassertion that the hedge is expected to be highly effective,notwithstanding the basis difference.

To apply each of the three hedge-effectiveness tests,the entity must make two important choices regarding thefrequency of data observation and the time span of theobservation. FAS 133 gives entities flexibility in makingboth choices. With respect to frequency, the assumedfact pattern in our example leaves open several reasonablealternatives, most common among them being choices infrequency intervals: daily, weekly, monthly, quarterly, orannual. Similarly, there are many possible alternativesfrom which the entity may choose when determining thetime span of observation. Determining the time span ofobservation involves a familiar trade-off: the shorter thetime span, the fewer the observations and, possibly, thegreater the influence of atypical observations; the longerthe time span, the more dated the observations and, pos-sibly, the less relevance they will have. In our example, theentity 1) collected monthly spot prices for natural gas thatwere based on the Houston Ship Channel Index and the


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monthly expiration prices for the Henry Hub FuturesContract for the period spanning June 1999 through June2001 and 2) calculated the 24 monthly changes in eachprice series.

DOLLAR-OFFSET METHOD

The dollar-offset method compares the change inthe value of the derivative with the change in the valueof the hedged item. A hedge qualifies for hedge account-ing if the cumulative change in the fair value (or cash flow)of the hedging derivative is expected to offset the cumu-lative change in the fair value (or cash flow) of the hedgeditem enough to justify a conclusion that the hedge willbe highly effective. As previously stated, practice hasinterpreted the term high effectiveness as meaning that ahedge has achieved an offset within the range of 80% to125%. The logic underlying a range of 80% to 125% isdemonstrated by the following equations: 80% = 4/5and 125% = 5/4. The 80%-to-125% range (which wasfirst articulated by a member of the Securities andExchange Commissions Office of the Chief Accountantin a speech delivered at the SECs 1995 Annual Account-ing Conference) has become a guideline for assessing thehedge effectiveness of futures contracts that are accountedfor under FAS 80 and it has carried over to effectivenesstesting that is applied to hedges that are accounted forunder FAS 133. When applied retrospectively to an assess-ment of actual effectiveness, the dollar-offset method canbe applied either period-by-period or cumulatively begin-ning as of the hedges inception.

The prospective test can be based on either 1) actualhistorical prices for a representative period or 2) simulateddata when the estimated parameters of the simulation arebased on actual market data. The formula for assessing higheffectiveness under the dollar-offset method can beexpressed as follows:

(1)

where n

i=1Xi is the cumulative sum of the periodic changes

in the values of the derivative and ni=1

Yi is the cumula-tive sum of the periodic changes in the values of thehedged item. When performing prospective testing, anentity must determine the number of past periods that itwill use in its assessment of whether a hedge holds thepromise of being highly effective. If an entity uses data

0 8 1 251 1

. / . = =

X Yii

n

ii

n

from only one prior period, the hedge will either pass orfail on the basis of that single test. If data for more thanone prior period are used, there will be more than onepossible computation of effectiveness, and the entity willhave to decide how to interpret the results of the multi-ple computations. The most stringent requirement is thatthe hedge satisfy the 80/125 test in every period; how-ever, other less stringent interpretations may be accept-able. For retrospective purposes, the test can be based ona comparison between the fair value (or cash flow) changesthat have occurred during the current period or betweenthe cumulative fair value (or cash flow) changes since theinception of the hedge. In either case, the hedge will passthe test only if the ratio is within the critical range.

We are illustrating the dollar-offset method by usingthe two-year change in prices that occurred betweenJune 1999 and June 2001 because the hedge is a two-yearhedge. In the example, the closing futures price increasesfrom $2.226 per MMBTU to $3.738 per MMBTU forthe contract with an expiration date that is closest to June1 each year (the sum of the monthly changes is $1.512);the spot price as of June 1 increases from $2.21 perMMBTU (1999 spot price) to $3.89 per MMBTU in2001 (the sum of the monthly changes is $1.68). If thehedge ratio is one (short one futures contract for each10,000 MMBTU that is to be delivered), the value of thetest ratio is

-((-1.512)/(1.68)) = 0.90

This falls within the critical range. As a check on therobustness of this test result, the value of the test ratio forthe June 1997-June 1999 period is

-((0.120)/(-0.100)) = 1.20

This also falls within the critical range. Based onthese test results, the monthly data for 1997 to 2001 indi-cate that the hedge is expected to be highly effective.

Because entities must evaluate the ongoing effec-tiveness of their hedges and account for them quarterly,it is instructive to examine the values of the test ratio overshorter time intervals. This may be particularly helpful,for example, if an entity is considering changing its hedgestrategy so that its hedges will take place over a shorterperiod, or perhaps if an entity is considering switching toa discrete-period, dollar-offset method for its retrospec-tive testing. Exhibit 1 reports the ratios for the four annualperiods ending on June 1 of 1998, 1999, 2000, and 2001.


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Three of the four annual periods have test ratios that falloutside the critical range when the hedge ratio is 1.0,although two of these ratios are only slightly outside thecritical range. In both of the cases that fail, the change inthe spot price is relatively small.

Later in this article, we apply the regression methodto the example and find that the best hedge ratio is to beshort 0.963 futures contract per 10,000 MMBTU that areto be delivered. With this hedge ratio, the change in thefutures price in Exhibit 1 is 0.963 times the short futuresprice change that corresponds to a hedge ratio (HR) of1.0; and the 80/125 test ratio is 0.963 times the test ratiofor HR = 1.0. Three of the annual hedge ratios are withinthe critical range for this lower hedge ratio, which illus-trates the importance of choosing the hedge ratio carefully.

Even with a hedge ratio of 0.963, one of the annualtest ratios is outside the critical range. This result isattributable to a general shortcoming of the dollar-offsetmethod. When the changes in the values of either thehedged item or the derivative are close to zero, smalldollar amounts of ineffectiveness can produce extremeratios. Under the dollar-offset method, this problem ismore likely to occur when there are short testing inter-vals. As a result, the dollar-offset method can be unreli-able, causing hedges that are, by other reasonable standards,highly effective, to fail the hedge-effectiveness test. There-fore, we caution entities not to rely exclusively on the dol-lar-offset method because of its sensitivity to small changesin value.

REGRESSION METHOD

Regression analysis is a statistical technique used toanalyze the relationship between one variable (the depen-dent variable) and one or more other variables (indepen-dent variables). This technique is a formal means ofexpressing the dependent variables tendency to vary withthe independent variables in a systematic fashion. In the

context of testing hedge effectiveness, the regressionmethod can be used to determine whether changes in thehedged item and the derivative are highly correlated andthus support an entitys assertion that the hedge willachieve a high degree of offset in the fair values or cashflows.

The degree of the explanatory power of, or cor-relation between, the dependent and independent vari-ables in a regression model is measured by the coefficientof determination or R2. The R2 indicates the propor-tion of variability in the dependent variable that can beexplained by the variation in the independent variables.

For instance, an R2 of .95 indicates that 95% of the move-ment in the dependent variable is explained by the vari-ation in the independent variables. R2 values are alwayspositive (because R2 is a squared number) and can neverexceed a value of 1 (i.e., it is not possible to explain morethan 100% of the movement in the dependent variable).

The regression method uses linear regression totest hedge effectiveness. The formula for expressing thelinear relationship between the changes in the fair value(or cash flows) of the hedged item and the derivative isas follows:

Y = a + bX + e (2)

where Y is the dependent variable, X is the independentvariable, a is the estimated intercept term, b is the slopecoefficient, and e is the error term. The slope is also animportant consideration in an evaluation of whether ahedging relationship is highly effective. It is determinedby computing the change in the Y value over the changein the X value and, therefore, can be interpreted as the change in the hedged item that is associated with thechange in the derivative (assuming that the model usesthe derivative as X and the hedged item as Y). Because theslope coefficient is the slope of the straight line that theregression model determines best fits the data, it rep-resents the variance-minimizing hedge ratio, i.e., theoptimal number of hedging contracts per unit of thehedged item.

To apply the regression method, an entity has tospecify the critical values for each test statistic. For R2, themodel should reflect an adjusted R2 of at least 0.8. Sev-eral studies that have used the regression method to testhedge effectiveness have documented that well-designedhedges generally have a coefficient of 0.8 or greater.Therefore, only models in which more than 80% of thevariability of the dependent variable can be explained by


SpotShort

Futures PricePrice Price Change 80/125 Test Ratio

12 Months Change Change Difference HR = 1.0 HR = 0.9636/976/98 -$0.260 $0.329 $0.069 1.27 1.226/986/99 0.160 -0.209 0.049 1.30 1.256/996/00 2.120 -2.180 0.060 1.03 0.996/006/01 -0.440 0.668 0.228 1.52 1.46

E X H I B I T 1Annual Spot and Futures Price Changes and the80/125 Test Ratio

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the variability of the independent variable should be con-sidered highly effective. The slope coefficient must be neg-ative because the derivative is expected to offset changesin the hedged item. Also, if the actual hedge is based ona one-to-one hedge ratio, the slope coefficient should beclose to 1 and certainly fall within the dollar-offsetmethods critical-value range of 80/125. However, whenthe actual hedge ratio that is utilized equals the slopecoefficient, the slope coefficient may have any value as longas it is negative.

In addition to evaluating R2 and the slope coeffi-cient, an entity should consider the statistical validity ofthe overall regression model. Statistical packages calculatean F-statistic as part of the standard output. The F-statis-tic measures the statistical significance of the relationshipbetween the dependent and independent variables and,therefore, can be used to evaluate whether the regressionmodel results in a valid description of the relationshipbetween X and Y. The better the model fits the data, thegreater the F-statistic. Because the significance of the F-statistic varies with the number of observations in themodel, oftentimes a poor F-statistic result can be attributed

to insufficient data. A reconsideration of the time span andfrequency of data observations may help an entity rectifythis situation. Care, however, should be taken to ensurethat the new observation period is representative of thehedging relationship.8

Exhibit 2 displays the regression results that arebased on an analysis of the 24 months spanning June1999 to June 2001. The results indicate that for thisperiod approximately 99.0% of the variability in the spotprice of natural gas at the Houston Ship Channel Indexprice can be explained based on the variability of theNYMEX futures contract price for natural gas at theHenry Hub price. The variance-minimizing hedge is ashort position of 0.963 futures contracts for each 10,000MMBTU or a short position of 9,630 MMBTU at theHenry Hub price for each 10,000 MMBTU that is tobe purchased at the Houston Ship Channel price. TheF-statistic for the regression model is 1,187, which is sta-tistically significant at the 99% confidence level. Basedon these results, the analysis supports a conclusion thatthe hedge in our example is expected to be highlyeffective.


-$4.00

-$3.00

-$2.00

-$1.00

$0.00

$1.00

$2.00

$3.00

$4.00

-$5.00 -$4.00 -$3.00 -$2.00 -$1.00 $0.00 $1.00 $2.00 $3.00 $4.00 $5.00

Change per MMBTU in the Value of a Short Position in the NYMEX Henry Hub Natural Gas Futures Contract

Cha

nge

per

MM

BT

Uin

the

Hou

ston

Ship

Cha

nnel

Inde

xPr

ice

Y = $0.0086 - 0.963X

Adjusted R2= 0.990

E X H I B I T 2Regression MethodRelationship between Changes in the Spot Price and the Futures Price: Monthly Data,June 1999June 2001

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VARIANCE-REDUCTION METHOD

The variance of an item is a statistical measure of thedispersion of the items possible values. In using the vari-ance-reduction method to assess hedge effectiveness, anentity measures the extent to which the hedge offsets thevariance of the hedged item. Specifically, this test com-pares the statistical variance of the fair value (or cash flow)of the combined position with the statistical variance ofthe fair value (or cash flow) of the hedged item alone. Ifthe variance of the combined position is significantly lessthan the variance of the hedged item alone, it is likely thatthe hedge will be effective. Of course, critical to an assess-ment of hedge effectiveness is the value that an entity usesfor determining how large the variance reduction mustbe if it is to serve as sufficient evidence that the hedge ishighly effective. An entity should specify this value whenasserting that a hedge is highly effective. Several previousstudies of the effectiveness of different futures hedgeshave documented that properly designed hedges canreduce the variance by 80% or more.9

If a hedge is highly effective, the variance of thecombination Hi = Xi + Yi is small relative to the varianceof the hedged item alone. Therefore, a suitable measureof hedge effectiveness is the proportion of the variance ofthe hedged item that the derivative offsets, as follows:

= variance reduction (3)

where s 2Y is the sample variance for the hedged item ands 2H is the sample variance of the hedged position. We usedhistorical prices for the hedged item and the derivativeinstrument to calculate these two variances.

The variance-reduction method tests a hedges effec-tiveness in reducing the dispersion of changes in the fairvalue (or cash flow) of the hedged item. A perfect hedgewould reduce this dispersion to zero and produce = 1.0.In a highly effective hedge, the ratio of the variance of thehedged position to the variance of the unhedged item isclose to zero and, based on the previously mentionedresearch, produces an epsilon greater than 0.8. Thus, theresults of the variance-reduction method are evaluated ona basis that is consistent with the regression method. Thatis appropriate given that R2 is identical to the variance-reduction statistic when the slope coefficient is the hedgeratio. Both techniques measure the portion of variance thatis eliminated through the hedge.

= 12

2

s

sH

Y

In the natural gas hedging example, the variance ofthe monthly changes in the Houston Ship Channel spotprice for the period spanning June 1999 to June 2001 is($2) 1.48. The variance of the monthly changes in thehedged position is ($2) 0.04. Therefore, the measure ofhedge effectiveness is

= variance reduction

The hedge offsets 97.3% of the variance of thehedged item.10 Based on a critical value of 80% variancereduction, the monthly data for 1999 through 2001 indi-cate that the hedge is expected to be highly effective.

CONCLUSION

To apply FAS 133, an entity must perform hedge-effectiveness tests that are themselves effective in distin-guishing between hedges that work properly and those thatdo not. Testing the degree to which an entitys risk expo-sure is offset proves key. Highly effective hedges offset riskto a very high degree; less effective hedges do not.

This article has provided an overview of FAS 133srequirements for assessing hedge effectiveness anddescribed three tests of hedge effectiveness. The dollar-offset method is relatively simple to apply and usually reli-able. However, it should be used cautiously when thechange in the underlying price of the hedged item andthe derivative during any test period is expected to be closeto zero. In such circumstances, the dollar-offset methodcould result in the rejection of a proposed hedging rela-tionship that is otherwise highly effective when pricechanges are more significant. The regression and variance-reduction methods are more difficult to apply but are veryreliable. Some entities may find that the interpretation ofthe results is more challenging. Both methods, however,are less susceptible to the risk that overall highly effectivehedge strategies will be rejected during isolated periodsinvolving small price changes.

=

( )( ) =1

0 04

1 480 973

2

2

$ .

$ ..


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ENDNOTES

1See FASB Statement of Financial Accounting StandardsNo. 133, Accounting for Derivative Instruments and Hedging Activ-ities, Financial Accounting Standards Board (Norwalk, CT,June 1998), and Statement of Financial Accounting StandardsNo. 138, Accounting for Certain Derivative Instruments and Cer-tain Hedging ActivitiesAn Amendment of FASB Statement No.133, Financial Accounting Standards Board (Norwalk, CT, June2000).

2Consider, for example, the guidance in Issue E7, Hedg-ing-General: Methodologies to Assess Effectiveness of Fair Value andCash Flow Hedges, which was issued by the Derivatives Imple-mentation Group (DIG) of the Financial Accounting StandardsBoard (Norwalk, CT). Issue E7 notes that paragraph 62 of FAS133 permits entities to use the regression method for prospec-tive testing and the dollar-offset method for retrospectivetesting.

3See Stephen M. Swads speech Accounting and Disclosuresfor Derivatives, which was presented at the 1995 Twenty-Sec-ond Annual National Conference on Current SEC Develop-ments (Office of the Chief Accountant, U.S. Securities andExchange Commission, Washington, DC, January 11, 1995).

4See Robert C. Lipes speech Current Accounting Projects,which was presented at the 1996 Twenty-Fourth AnnualNational Conference on Current SEC Developments (Officeof the Chief Accountant, U.S. Securities and Exchange Com-mission, Washington, DC, December 10, 1996).

5See Louis H. Ederington, The Hedging Performanceof the New Futures Markets, Journal of Finance, 34 (March1979), pp. 157-170; and Stephen Figlewski, Hedging Per-formance and Basis Risk in Stock Index Futures, Journal ofFinance, 39 (July 1984), pp. 657-669.

6There is also a shortcut method that is available forinterest rate swaps and recognized interest-bearing assets orliabilities. See FAS 133, paragraphs 68-70, 114, and 132, as wellas DIG Issues E4, E10, E12, E14, E15, and E16.

7An alternative approach is to use simulated data. How-ever, this approach could bias the test results, unless an entitywere able to verify that the simulated data are truly represen-tative of actual data for both the hedged item and the hedgingderivative.

8When there is a single explanatory variable, as in equa-tion 2, the F-test is equivalent to a t-test of whether the slopecoefficient is significantly different from zero.

9For example, see the references in endnote 5.10The variance-reduction statistic of 0.973 does not equal

R2 of 0.99 as determined by applying the regression method,because it is not assumed that the hedge ratio is equal to theslope coefficient.

Editors Note: The authors would like to thank Nora Dougherty,John James, and Deidre Schiela for helpful comments and JimmyGerhart for research assistance.

John Althoff is a partner in the National Office of Pricewater-houseCoopers LLP. He works in the firm's Risk & Quality Group,primarily consulting with engagement teams and clients on technicalaccounting issues related to derivative instruments and hedging activities.John Finnerty is a professor of finance at Fordham University and untilrecently was a partner in the Financial Advisory Services Group of Price-waterhouseCoopers LLP. While at PricewaterhouseCoopers, he consultedwith clients regarding a variety of issues related to financial instrumentvaluations. John currently works for Analysis Group/Economics.

To order reprints of this article please contact Ajani Malik [email protected] or 212-224.3205.


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