Weather Derivatives:An Attractive Additional Asset ClassDAVID VAN LENNEP, TEDDY OETOMO, MAXWELL STEVENSON,

AND ANDRE DE VRIES

D A V I D VAN L E N N E P

is a scriiccurcr lii FiiuncialMarkets Derivatives Market-ing—Weather & InsiiraiicfDerivatives at ABN AMROBank N.V. in Amsterdam.Tlu' NftlierLiiid';.david.van.lennep 'iil.abnatnro.coin

T E D D Y O E T O M O

is 3 doctorjl student in tlicFinance Discipline. Schoolof Business at the Universityof Sydney in Sydney,Australia.

W

MAXWELL STEVENSONis a senior lecturer in theSchool of Business at theUniversity ot Sydney.ni.slevenson@econ.usyd.eduju

ANDR£ DE VRIES

IS a striicturer in FinancialMarkets Derivatives Market-ing—Weather & InsuranceDerivatives at ABN AMKOBank N.V in Amsterdam,andre.de.vries @ nl.abnamro.coni

hile still a relatively youngmarket, the popularity ofweiithcr derivatives ha.s expe-rienced a rapid escalation in

recent years. The weather derivatives marketwas introduced during the mid 199()s in themidst ot the deregulation of the energyindustry in the United States. Market surveysreport an increase of market turnover trom695 trades for USSl .8 billion notional value in1999 to 4,517 trades for USS3.5 bilhon m2003.' The growth of the weather derivativesmarket was enhanced by the presence of anorganized market for weather derivatives atthe Chicago Mercantile Exchange (C^ME),which started to trade temperature weatherderivatives cotitracts of 10 U.S. cities in 1979.This has since expanded to 15 U.S. cities and5 European cities with turnover of 7,239 tradesfor USS0.7 billion notional value ni 20(13.

Extant literature attributes the rapidgrowth in the weather derivatives market tothe deregulation of the energ)' and electricitymarkets.- While the importance of weatherrisk on the revenue generated by electricityand energy companies is apparent, it is alsoimportant to recognize that a large number ofother industries are exposed to weather risks.Indeed, Challis |1999] and Hanley [1999| esti-mate that around USSl trillion of the USS7trillion U.S economy is sensitive to weatherrisks. Globally. Auer |2()O3] points out that"around tour-fifths ot all economic activityworld-wide is directly or indirectly aftected

by the weather" (p. 1). However, despite thebroad range ot industries affected by weatherrisks, a significant portion of weather deriva-tives are traded against temperature-relatedvariables. Moreno [2000] claims that temper-ature-based derivative contracts are the mostwidely traded weather derivatives. Indeed,Cao, Li, and Wei |2OO3| report that "morethan SO percent of all the weather derivativescontracts are traded against temperature vari-ables ... accounting tor more than 91) percentof the total value in any given year" {p. 2).

The high concentration of temperature-based contracts is somewhat perplexing gi\'enthe significant effect imposed by other non-temperature weather variables on the volatilityof companies' revenues within a wide range ofindustries. Exhibit 1 outlines the impact ofweather risks on various industries. This studyexamines the role ot non-standardized con-tracts in serving as a hedge instrument for com-pany revenues. Non-standardized contracts aredefmed as weather derivatives contracts thatare constructed to fit the need of a particularindustry or corporate clients and are traded onthe Over-The-Counter (OTC) market ratherthan an organized exchange such as the CME.Additionally, the potential of weather deriva-tives as an alternative asset class in portfoliomanagement is analyzed. Extending priorstudies, the weather derivatives contracts exam-ined here are not confined to temperature-based contracts.

FALL 2I.HI4 TnF JOURNAL o^ AITF.HNATIVE 65

E X H I B I T 1Sectors Affected by Weather Risks

Element Beneficial to Detrimental to

FrostHeatSunRainfallDownpourWindSnowWavesFog

Energy, retailUtilities, beverages, waterLeisure, tourism, retailCrops, hydro plantsRetailWindparksSki resorts, snowmobilesWave power generationAirports

Construction, production, cropsInsurance, crops, energyCinemas, video rentalsFood and beveragesCrops, insurance, dredgingInsurance, airlinesAirports, transportOffshore construction, transportAirlines, transport, insurance

Companies from a wide range ot industries are ableto bedge more acctiratcly against the volatility of their rev-enues, costs, or marL ins by resorting to non-standardizedweather derivatives contracts. For example, the impact otweather risks on agrictiltural companies vanes throughouta calendar year, witb a precipitation-based weatbcr deriv-atives providing a bcdgc against volatility' ot revenues causedby either too much or too little raintall. Additionally, tbeconventional analyses ol- the ability ot weather derivativescontracts to serve as a risk transfer mechanism have largelybeen conducted on single-variable weather derivativescontracts, such as temperature-based contracts. However,companies' revenues arc often exposed to more than oneweather risk tactor. This article proposes the tise of a basketot weather derivatives contracts to more accurate!)' hedgeagainst a number of weather variables tbat directly atTectthe volatility of revenues.

The role of weather derivatives in portfolio man-agement is also examined in this study Extending thework of Cao, Li, and Wei [2()()3| who analyze the role ofthe temperattire-based contract, this article investigatesthe role of both temperature- and non-temperature-basedcontracts on porttolio management. The results docu-mented in this study demonstrate adding weather deriv-atives into conventional portfolio produces signitK-antdiversification benefits and enhances the ctficicnt fron-tier line of the conventional porttolio.-^ These benetitsallow the market maker to diversity the systematic riskinvolved in writing non-standardized contracts by repack-aging these contracts and including these contracts as partot existing portfolios, m order to enhance diversificationand increase rettiriis.

A HEDGING INSTRUMENT FOR THEAUSTRALIAN AGRICULTURAL CHEMICALINDUSTRY

Prior to the introduction of weather derivatives, agri-cultural companies were only able to hedge against pricerisk caused by the volatility ot their revenues by using com-modity tlitures contracts.' The recent introduction otweather derivatives provides these companies with a con-tract to hedge the risks associated with variable produc-tion volume due to the level of annual rainfall. Whiletemperature has been a significant tlictor attecting theenergy' industry, agricultural companies may want to hedgeagainst other weather variables, such as raintall.""

The effects of rainfall on agricultural industries havelong been documented. Orlove, Chiang, and Cane (2(){)()]state that tollowing an abnormally low raintall period,tanners adjust and moderate their planting pattern. Fur-ther, the 2(H)2 report by the Wisconsin Agricultural Sta-tistics Service reports that corn and soybean tanners reducetheir planting volume if they anticipate a lower thannormal raintall. On the other hand, extremely high rain-fall translates to higher probability that the crops will incurdisease.'' Consequently, the demand for crop-protectionchemicals is expected to be atfected by raintall conditionsthat intluence both the volume of planting and the needfo]- pre-harvest treatments.

This section discusses the ftmdamentals underlyingthe construction ot non-standardized weather derivativesbased on rainfall. Such a weather derivatives contractwould allow agricultural crop-protection chemical pro-ducers to hedge against rain fa II-driven sales volatility. Asan example, we take a precipitation-based weather deriv-

66 Wi-AIHLK l>b.ll.iVAMVt,S: A N Al I KACIIVL A D I ' I 1 itlNAL CLASS FALL 2m )4

E X H I B I T 2Adjusted R-Squared and Pearson Correlation

Sales vs. Annual Rainfall Log{Sales) vs.LoS<Ann. Rainfall)

Pearson Corr. Adj R-Sq. Pearson Corr. AdJ R-Sq.

Central West Region 0.32' 8.93 0.33 8.02^

atives contract written tor an agricultural chemical com-pany to hedge their rainfall risk in the Central West regionof New Soutli Wales, Australia. The dataset of dollar valuespent on crop protection chemical per non-irrigatedhectare (Sdlcy) is obtained from ABARE and covers theperiod between jLine3(), 1990, and June 30, 2002.'

Exhibit 2 reports the Pearson correlation betweentlie actual and the logged value ot' Siilcs and the values of(Viiiua! niiiijiill. The double-log specification is selected asit can be interpreted as the percentage change in S(iU\< fore\'ery 1% increase or decrease in the annualized rainfall.The results documented in Exhibit 2 demonstrate thattlie two variables are, at best, weakly correlated. Thisfinding is further supported by the small adjusted R-squared value trom the regression o( Sales (log Siilcs) oniiiinu.il niiiifal! (log ofaiiinuil rainfall) as reported in Exhibit2. Tbe adjusted R-sc]uared is a measure of explanatorypower and quantifies the ability of the annual rainfall vari-able to explain vai'iation in Sales. The results show tliatiiiintial laiiiJ'iH explains only around 9% of the variation inSiilcs. The weak correlation and low adjusted R-squareddocumented indicate that crop protection chemical pro-ducers would not be able to efFiciently liedge their volumerisk using swap contracts that are benchniarked against anannual rainfall variable.

The lack of explanatory power attributed to annualrainfall in explaining variations ni Sales is largely due tt)the variable impact of rainfall on the sales of crop-pro-tection chemicals throughout a calendar year. The effectof rainfall varies throughout the different stages of thecropping cycle. In order to account tt)r such variations,it is important to weight the impact of rainfall on Salescorresponding to different stages of crop maturation. Themonthly weightings are the regression coefficients in thefollowing eqtiations:

ln( Sales,) = a H- In /?,, + e, (1)

fall, has been decomposed mto monthly variables. R;denotes the intjiithly accumulated rainfall for month /.The estimation covers the period between 199() and 1997.The years from 1998 to 2()(H inclusive are held out fromthe estimation period and are used to generate forecaststo asses the robustness ot the results over an t)ut-of-sainpleperiod. The independent variables represent the C()nibi-nation of months that best explain the variations in thelog value of Sales. The combination with the mimmumAkaike Information Criterion (AIC) is selected. Thisinformation criterion is given by the following equation:

AIC- (2)

where the independent variable, annual accumulated rain-

where ;( is the number of observations, SSH denotes theerror sum ()f squares, and /) represents the number of para-meters in the model. As tbe number of parameters in theregression model increases, there will be a reduction ofthe error sum of squares. Also, as a consequence ot anincrease in the number of parameters, further error isintroduced into the model via the estimation of the addi-tional parameters. The AIC penalizes the introduction ofextra parameters by balancing the reduction of tbe SSHwith the increased error from the estimation process. Thepi coefficients ot the monthly accumulated rainfall vari-ables from the selected model represent the weights allo-cated for the various months. Exhibit 3 reports the weightsallocated to each ot the selected months for our NewSouth Wales C'entral West example.^

Controlling for variations in tbe impact of rainflillon the different stages of crop maturation significantlyincreases the ability of the derivative contract to act as ahedge against the volatility in the demand for crop-pro-tection chemicals. The results reported in Exhibit 4demonstrate that, during tbe estimation periods, the set-tlement levels of the raintall contracts exhibit a high cor-relation against the variations in sales volume of thecrop-protection cliemicals. The Pearson correlation

FAII 21II14 llURNAL C)l Ai.rr^KNATlVL INVLSIMLNIS 6 7

E X H I B I T 3Weights Allocated to the Selected Months

January0.08

April-0.04

July August October February May0.13 0.06 0.15 0.09 0.17

E X H I B I T 4

In-Sample and Out-of-SampIe Tests

Pearson Correlation Adjusted R-SquaredIn-Sample

Out-of-Sample0.940.97

84.8 K/f553m

increases from 0,33 to 0.94, wliereas the adjusted R-squared increases from 8.02% to 84.81%.

In order to ensure the robustness ot the results, thecorrelation between the settlement values of the raintallcontracts and the sales volume during the out-of-sampleperiod (1998. 1999, 2(100, and 2001) is examined. Theresults tor the out-ot-sample test are tound to be consis-tent. The results reported in Exhibit 4 docunient a Pearsoncorrelation of 0.97 and an adjusted R-squared ot .S5.38%)''

A HEDGING INSTRUMENT EOR THEAUSTRALIAN WINE INDUSTRY

In the last five years, the Australian wine industryhas experienced very rapid growth. Wine production hasincreased by 52% between 1998 and 2003, with export vol-umes increasing by 170% to AUDS2.4 billion in value.'"The rapid increase in Australian wine production indi-cates a growing demand for financi;il instruments that allowwinegrape producers to hedge against weather risks.Jackson and Spurling [1992] identity weather risks as oneof the most significant factors that affect the quality andthe quantity of grape production. The significance otweather risks is flirther pronounced by the aniu>uncementin the 1998 Australian Wine and Grape Industry report thatthey account for 42%i of the loss in Australian Chardonnaygrape production. Responding to the need for risk man-agement, many studies have proposed the introduction oifinancial derivatives that allow participants in the wineindustry to hedge some ot the risks involved.

Taylor [20()0j proposes the use ot wine tonvard con-tracts to hedge against price volatility. Winkler, Kliewer,and Lider [1974] propose the use of temperature deriva-tives that are benchmarked against the Dcj^wc Dny huiex.

C'onsistent with the Hcdliin^ Dci^ycc Day (HDI)) and theQ'('//ni,' Dfiiivc Day (C'DD). the Degree Day Index cap-tures a one-sided deviation from a threshold level. Whileextensive literature has, in general, agreed on the method-ology, there are disparities on what the appropriate levelot the threshold should be. The level of the thresholddetermines the level ot acceptable temperature. Gladstone|I965| proposes the use of 19°C as the upper limit whileBoehm | ]97O| finds that the use of 8°C provides a betterbase temperature between the period of budburst andflowering. Aney [1974| proposes an alternative indexwhich utilizes Thornthwaite's potential evapotranspira-tion index as an arbiter ot climatic suitability for grapegrowing. This method was later challenged by Jacksonand Spurling J1992]. Other indices include the MeanAverage Range {Dry and Smart [1984|) and the SunshineIndex (Becker [1985]).

Interestingly, the existing wine indices that measurethe weather desirability are all benchmarked against a singleweather variable. This is highly perplexing given that thewine industry is exposed to a number of risks tixsm weather.Ciladstone |1992] argues that temperature, sunshine,hiimidit\', and raintall are the dominant weather risks thatconfront the wine industry. Happ [ 1999| and Smart [2001]suggest that temperature above 3()°C and below 2()°Ccould have an adverse effect on winegrape production.McCarthy and Coombe [1985] and Bravdo and Hepner11987] point tnit the danger of water stress on the qualityof winegrapes. Jackson and Spurling [ 1992] state that exces-sive raintall increases the likelihood of disease.

Moreover, C'oombe [1987] warns against the gen-eralization of the relationship between climate and winequalit)'. Hedberg [2000] asserts that, while grapevines cansurvive at very low temperatures, the new spring growth

68 WFATIIER DERIVATIVES: AN AnRAcnvF ADDITKINAI ASSF:T C LASS FALL 2004

E X H I B I T 5Weighted Coefficients

ATMin ATMax ATVol ExRain ShrRainJanuaryFebruary

MarchAprilMayJuneJuly

AugustSeptember

OctoberNovemberDecember

-

---

0.21-

0.180.24

-0.37

0.42

0.26

0.32

0.360.31

0.33

0.680.16

0.16

0.24

0.12

0.64

E X H I B I T 6Contribution Ratios of the Underlying Weather Variables

AnnATMin AnnATMax AnnATVoI AnnExRain AnnShrRain

BarossaMeanMedianStd Dev

28.98%29.03%8.95%

27.74%26.77%9.46%

36.27%33.95%11.66%

5.55%0.00%

11.11%

1.46%0.00%3.24%

is very sensitive to low temperatures. Hotackcr, Alleweldt,and Khcider |I976] assert that the growth of berriesexhibits greater dependence on precipitation than on thetemperature during the shoot, interflort-scence develop-ment, and flowering stages.

C^onscquently. the production risks would be betterrepresented by a biiskct ot weather derivative contractsthat cover nuiltiple weather variables. Additionally, theproposed contracts place different weight on the impactot weather risks on winegrape production throughoutdifferent months in the calendar year. The sample uti-lized in this sttidy represents grape yield and weather datah"om the Barossa region ot South Australia. The grapeyield data differentiates between the yield contributingto either white or red wine, and the sample covers theperit)d between 1981 and 20(^2. The grape yield sampleis obtained through the Australian Bureau of Statistics(ABS). The weatber data is obtained from the AustrahanBureau ot Meteorology (BOM) and covers the periodbetween 19H() and 2(102.

In order to decide on the composition ot'tbe basketot'contracts that efficiently serve as a hedging mecbanism

tor winegrape production, a number ot factors need tobe identified. First, the weather risks that confront wine-grape production need to be identified based on conjec-tures and hypotheses set out by prior literature. Higherthan expected temperature {A'l'Miix). lower than expectedtemperature (ATMlii), excessive temperature volatility{A'il bl}, excessive rainfall {HxRaiii). and shortage ot rain-tall (ShrRain) are utilized as the weather variables thatserve as the benchmark tor the winegrape hedging con-tracts. The variables are constructed in a manner similarto the Heating Degree Day (hIDD) and Cooling DegreeDay (CDD) contracts traded on the CME."

Second, in order to identify the months that signif-icantly effect winegrape prodtiction. the grape yield vari-able is regressed against eacb of tbe weather variablesseparately. All possible monthly combinations are exam-ined tor each weather variable witb combinations thatexhibit the lowest AlC selected as the base model. ThecoetTicients of the selected models are weighted so thatthe sum of tbe coettlcients is equal to one. The stan-dardized weighted values of the coetticieiits represent theweight imp(.)sed on each month and represent the quan-

FAU 2IHI4 THb JOURNAL Oi Al I t.KNAIIVi, iNVtSIMhNfS 6 9

E X H I B I T 7In-Sample and Out-of-Sample Tests

In-SampleOut-of-Sample

E X H I B I T 8

Weather Portfolio

Location

MelbourneMelbourneParisOsakaFukushimaSydneyTownsville AeroSacramento AirportKassel, Nuerburg, HannoverFrankfurtSchipholMemphis. Nashville, Chattanooga,TorontoMemphis. Nashville, Chattanooga.DenmarkUSSpanish Hydro IndexAustralian Stream FlowTokyoWPI SpainEssenEssenDe BiltWPI AustraliaKairi Australia RainChicago

Adjusted R-Squared0.36

-

Position

Short PutShort CallShort PutShort CollarShort CallLong SwapShort CallShort CallLong SwapShort SwapShort Call

Knoxville Long SwapShort Put

Knoxville Long SwapLong SwapLong SwapShort PutShort PutShort CollarShort PutShort PutShort PutShort CallLong SwapShort CallShort Put

Pearson Correlation-0.61-0.90

Index

Temperature & SunshineCDD{18C)HDD (18 C)True Avg TempSnow DepthHDD(I8C)PrecipitationCold DaysFreezing DaysAverage TempFreezing DaysHDD (65 F)HDD(18C)CDD (65 F)Wind SpeedWind SpeedRes. LevelStream FlowTrue Avg TempWind IndexHDD(18C)2 Coldest DaysPrecipitation > 19mmWPIPrecipitationHDD (65 F)

tified impact of the weather variable on the winegrape pro-duction for that month. They are utilized to compute acontribution ratio tor the annualized value of the weathervariables. The monthly weightings of the weather vari-ables are reported in Exhibit 5. while the contributionratios of the annualized weather \'ariables are reported inExhibit 6.

Exhibit 7 reports a Pearson correlation of—0.61 forthe ill-sample period (19S1 to T)99) between the settle-ment of the basket of weather derivatives and the grapeyield. The negative correlation is expected as the weatherderivatives are correlated with the low levels of grapeyield. Variation in the settlement of the weather deriva-tives, however, explains only 36.49% of the variation in

70 WrATiit.k r.)LkiVAiiVES: A N ATTRAi.:Tivi-. Ai)iJi!H)NAL ASSET CLASS F A L L 2 I M I 4

E X H I B I T 9

Performance of Weather Derivatives as an Additional Asset Class

1234

Assets

FquitiesBondsReal EstateWeather

ExpectedReturn

(annualized)

10%5%7%

18%

Standard Deviationof Return

(annualized)

16%6%

12%26%

Equities

100%21%55%0%

Correlation

Bonds

21%100%25%

0%

of Return

RealEstate

55%25%

100%0%

Weather

0%0%0%

100%

E X H I B I T 1 0

Performance of the Optimal Portfolios

Portfolio Initial Investment Spectrum Including Weather

EquitiesBonds

Real EstateWeather

Expected ReturnStandard Deviation

Sharpe Ratio

32%57%11%0%

6.82%7.52%

0.52

19%52%9%

20%8.75%7.54%0.78

the i r.ipe yield. Again, this modest adjusted R-squared isto be expected as weather derivatives are used to hedgeagainst the low grape yield and therefore capture only aone-sided variation in the grape yield. In order to ensurethe robustness of the results over different time periods,settlement values are estimated tor the out-ot sampleperiod (2000 to 2(K)2). Exhibit 7 reports the in-saniple andout-ot-sample Pearson correlations, as well as the in-sample adjusted R-squared. The results for the out-of-sample analysis are consistent with those of the in-samp!e.'-

THE ROLE OF WEATHER DERIVATIVESIN PORTFOLIO MANAGEMENT

The negative correlation between various sectorsadversely affected by weather exposure, in addition to lowcorrelation between weather variables, suggests that theincorporation ot weather derivatives into conventionalportfolios sliould provide significant diversification ben-efits. This study examines the impact of including a

F,M.i 2004

number of different weather derivatives contracts into aconventional portfolio. Exhibit 8 oudines the 26 weatherderivatives contracts which represent the constituent of thepotential weather portfolio.

Exhibit 9 reports the expected returns of weatherderivatives contracts and the conventional asset classes.The expected returns and standard deviations of the threeasset classes equity, bonds, and real estate are pardy basedon historical observations of indices for these asset classes.For equity, the MSCI World Equity hidex was used: forbonds, the Salomon Brothers Investment C.irade BondIndex; and tor real estate, the EPRA Index from the Euro-pean Public Real Estate Association (all monthly obser-vations). The assumed returns of the asset classes are basedpartly on five-year historical observations and partly oncurrent market expectations. Weather derivatives con-tracts arc assumed to exhibit minimal correlation withother asset classes.

Exhibit 10 compares die performance of the optimalportfolio in the absence and the presence of weather cleriv-

THF.JOUKNAL OF ALibkNAiivi' INVESTMENTS 7 1

E X H I B I T 1 1Impact of the Weather as an Additional Asset Class on Efficient Frontier and Capital Market Line

1E%

17%

ts%

13%

11% -

4% j - - - •

Efficient Frontier

Efficient Frontier incl. Weather

Capital Markel Line

. . _ Capital Market Line incl. Weather

1C% 12% 14% 1S% 1S% 20%

Standard Devi^ion erf Return [annual)

atives. The optimal portfolio is computed in accordancewith modern portfolio theory (see Markowitz [1952]).The results reported in Exhibit 10 demonstrate that, byincluding weather derivatives, the expected return of theoptimal portfolio is increased from 6.82% to 8.75%. Addi-tionally, the Sharpe ratio is increased from 0.52 to 0.78.'-^This ratio is a performance measure for portfolios and isessentially a reward-to-variability ratio.''^ From Exhibit10, we observe tiiat including weather derivatives increasesthe standard deviation of the optimal portfolio only mar-ginally, from 7.52%i to 7.54%. Exhibit 11 depicts theimpact of weather as an additional asset on efficient fron-tier and capital market line of the optimal portfolios.

CONCLUSIONS

The results documented in this article suggest thatnon-standardized contracts serve as a more efticient risktransfer mechanism than do standardized contracts.Relaxing the assumption that weather risks are constantthroughout the calendar year significantly increases theabiiit)' of weather derivatives to serve as a hedging instru-ment against the volatility in demand for crop-protection

chemicals. Also, by recognizing that companies are oftenexposed to more than one weather risk simultaneously,we examine an example from the wine industry that high-lights the use ot multi-variable weather derivatives con-tracts to hedge against variable production that is affectedby weather. Additionally, this article demonstrates thatincluding weather derivatives in a conventional portfolioenhances the performance of the portfolio while main-taining a low standard deviation.

While it appears that non-standardized weadier deriv-atives contracts allow for better risk transfer as coniparedto standardized contracts, critics express concern over thelack of liquidity in markets that trade the majority of thesenon-standardized contracts. Evidently, this lack of liquiditycontributes to the dif}lcult\' in finding counterparties tothese contracts. However, our results suggest a solution tothis problem. That solution would involve institutionalinvestors writing non-standardized contracts for their cor-porate clients. By recognizing that incorporating weatherderivatives will enhance the performance of conventionalportfolios, institutions could repackage these non-stan-ciardized contracts and ofTer them as an additional assetclass to be included in a conventional portfolio.

72 [>hkivAiivt,s: AN AI IKACTIVE ADPIIIONAL Assti CLASS FALL 2nii4

ENDNOTES

rhe aiitliors tliaTik Craig McBurnie tor the liclptul com-ments and suggestions. Teddy Oetomo thanks Capital MarketsC R C Limited for providing him with a Ph.D scholarship.

'2(1(13 Weather Risk Management Survey. Weather RiskManagement Association (WRMA) and I'ricrewaterhoiiseCoopers, May 2003.

'These studies mclude Challis | iy99 | . Hanley 11999],Aiaton. Djchichc, and Scillbcrgcr (2O(l2|. and Cao and Wei12003].

'All ot our conclusions are hased on historical data.Readers should recognize that the results might change tortuture periods and might not necessarily hold for other ret;:K)iisthat are not examined in this article,

""For studies on coinniodit\- futures, see Ho [IMS4] andLuo 11WS|.

"'Li and Sailor [I9"J5] suggest tliat temperature is the mostsignificant weather variable explaining U.S electricity and gasdemand.

''Ohio State University Extension Bulletin 74 1 and IowaCommercial Pesticide Applicator Manual.

ABARE is the leading government data provider for theAustralian agricultural And resource sector.

^These weightings are applicable only for the period underobservation and the region examinee! in this study.

''The signitlcantly higher l^earson correlation relative tothe adjusted R-squared indicates that while the two variablesare highly correlated, the ability ot the constructeci weatherderivatives contract to explain the dependent variable is con-strained by some degree by noise.

'"Australian Wine and Grape Industry [2003], AustralianBureati of Statistics.

' 'The historical average value tor each variable is selectedas the threshold level.

'-(liven the small number of observations tor the out-of-sampie period, the adjusted K-squared are not computed.

' 'All results are computed using the historical data on theweather variables outlined m Exhibit 7. It is important torreaders to recognize that the results might change when applyingdatasets trom ditlerent observation periods and ditTerent weathervariables.

'""See Sharpe |]966, I975| for further intbrmationregarding the Sharpe ratio.

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THE JOURNAL or ALTLKNATIVE INVESTMENTS 7 3

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