Noisy Inventory Announcements and Energy Prices. Marketa W. Halova, Alexander Kurov and Oleg Kucher. June 2013

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1 Noisy Invenory Announcemens and Energy Prices Markea W. Halova, Alexander Kurov and Oleg Kucher June 2013 Forhcoming, Journal of Fuures Markes Absrac This sudy examines he effec of oil and gas invenory announcemens on energy prices. Previous esimaes of his effec suffer from bias due o measuremen error in invenory surprises. We uilize inraday fuures daa for hree peroleum commodiies and naural gas o esimae he price response coefficiens using radiional even sudy regressions and he idenificaion-hrough-censoring (ITC) echnique proposed by Rigobon and Sack (2008). The resuls show ha he bias in OLS esimaes of he price impac of invenory surprises is quie large. The ITC coefficien esimaes are abou wice as large as OLS esimaes for peroleum commodiies and abou four imes as large as OLS esimaes for naural gas. These resuls imply ha energy prices are more srongly influenced by unexpeced changes in invenory han shown in previous research. * Corresponding auhor. Deparmen of Finance, College of Business and Economics, Wes Virginia Universiy, P.O. Box 6025, Morganown, WV 26506, Tel: , Fax: , alkurov@mail.wvu.edu. We hank Arabinda Basisha, Harry Turle, Chardin Wese Simen, seminar paricipans a he 2013 Easern Finance Associaion Meeings, and an anonymous referee for helpful commens and suggesions. Special hanks o Jeremy Shockcor for his help wih he invenory forecas daa. Errors or omissions are our responsibiliy. Markea Halova is an assisan professor of economics a he Deparmen of Economics, Washingon Sae Universiy, Pullman, Washingon. Alexander Kurov is an associae professor of finance a he Deparmen of Finance, Wes Virginia Universiy, Morganown, Wes Virginia. Oleg Kucher is an assisan professor of economics a he Deparmen of Economics, Frosburg Sae Universiy, Frosburg, Maryland.

2 1 Noisy Invenory Announcemens and Energy Prices Absrac This sudy examines he effec of oil and gas invenory announcemens on energy prices. Previous esimaes of his effec suffer from bias due o measuremen error in invenory surprises. We uilize inraday fuures daa for hree peroleum commodiies and naural gas o esimae he price response coefficiens using radiional even sudy regressions and he idenificaion-hrough-censoring (ITC) echnique proposed by Rigobon and Sack (2008). The resuls show ha he bias in OLS esimaes of he price impac of invenory surprises is quie large. The ITC coefficien esimaes are abou wice as large as OLS esimaes for peroleum commodiies and abou four imes as large as OLS esimaes for naural gas. These resuls imply ha energy prices are more srongly influenced by unexpeced changes in invenory han shown in previous research.

3 2 1. Inroducion Commodiy prices are volaile. There is much debae in he academic lieraure wheher his volailiy is driven primarily by supply and demand fundamenals or by speculaive rading (e.g., Kilian and Murphy, 2012, Büyükşahin and Harris, 2011, Tang and Xiong, 2012). An imporan srand of his lieraure invesigaes he effec of public news on commodiy prices. 1 Boudoukh, Richardson, Shen, and Whielaw (2007) show ha even when relevan fundamenals can be easily measured, modeling heir impac on prices is no a rivial exercise. 2 When researchers are unable o explain he majoriy of commodiy price variaion by fundamenal facors, he logical conclusion is ha mos of he volailiy is generaed by uninformed speculaive rading. We examine he effec of fundamenals, represened by news abou invenory, on energy fuures prices. Invenory repors for peroleum and naural gas are prepared by he U.S. Energy Informaion Adminisraion (EIA). The oil and gas invenory repors are widely followed by marke paricipans because he release of hese repors ofen riggers large price moves. For example, on March 19, 2009, he price of naural gas fuures for April delivery jumped almos 13% afer EIA daa showed a larger han expeced wihdrawal of naural gas from invenories. Linn and Zhu (2004) find a large increase in volailiy of naural gas fuures reurns afer he release of he Naural Gas Sorage Repor. Gay, Simkins, and Turac (2009) show ha a 1% unexpeced increase in naural gas invenory resuls in an approximaely 1% drop in he naural gas fuures price. Chang, Daouk, and Wang (2009) find a significan response of crude oil fuures reurns o unexpeced invenory changes immediaely afer he announcemen. Using 1 See, for example, Roll (1984), Bauer and Orazem (1994), Karali and Thurman (2009), Charah, Miao, and Ramchander (2012). 2 Boudoukh e al. (2007, p. 411) wrie: Even in he comparaively simple seing of he frozen concenraed orange juice fuures marke, he relaion beween reurns and emperaure is nonlinear, mulidimensional, and sae and pah dependen. In oher markes, idenifying he fundamenals and modeling heir relaion o prices is likely o be even more difficul.

4 3 cross-secional daa on analys forecass of invenory, boh Gay e al. (2009) and Chang e al. (2009) provide evidence ha prices reac more srongly o forecass of analyss wih beer prior forecas accuracy. Halova (2012) finds ha unexpeced changes in naural gas invenory have a saisically significan effec on prices of crude oil fuures. Similarly, unexpeced changes in crude oil invenory move naural gas fuures prices. Despie using inraday fuures reurns, Gay e al. (2009) repor an R 2 of only abou 23% for an even sudy regression ha allows for ime variaion in he response of naural gas fuures o he Naural Gas Sorage Repor announcemens. Chang e al. (2009) obain an R 2 of abou 35% in he regression of inraday crude oil fuures reurns on he aggregae forecas error for crude oil, gasoline and disillae fuel oil. I is surprising ha much of he price variaion remains unexplained by fundamenal news even in a narrow inraday window around he mos imporan and widely anicipaed informaion release of he week. Limied explanaory power of even sudy regressions is consisen wih subsanial amoun of noise in he measured invenory surprises. Due o he presence of noise in measures of invenory surprises, previous esimaes of he response of energy prices o such news have been biased owards zero. We conribue o his lieraure by adjusing for he measuremen error bias using he idenificaion-hrough-censoring echnique proposed by Rigobon and Sack (2008). We examine own- and cross-commodiy responses of four energy commodiies o invenory news. The resuls show ha he aenuaion bias in even sudy esimaes of price responsiveness o invenory surprises is quie large. For example, based on even sudy esimaes, an unexpeced 1% increase in naural gas invenory seems o resul in a 2.4% decline in naural gas fuures prices. In conras, our esimaes adjused for he measuremen error bias show ha naural gas prices fall by almos 10% on average in response o a 1% invenory shock. In oher

5 4 words, energy fuures prices are much more responsive o invenory announcemens han shown in previous research. Accuraely measuring he effec of invenory news on energy prices is imporan for several reasons. Firs, unbiased esimaes of he response coefficiens allow indirec inferences abou elasiciy of supply and demand curves for energy commodiies. Unexpeced changes in commodiy invenory are driven by supply and demand shocks. Commodiy price responses o such shocks are deermined by he price elasiciy of supply and demand. The less elasic he supply and demand, he larger he price response o a given shock. Our resuls provide evidence ha shor-erm supply and demand for energy commodiies are less elasic han hey appear based on radiional even sudy regression resuls. This finding helps o explain large flucuaions in energy prices in response o moderae demand and supply disurbances. 3 Second, our resuls are useful for modeling price dynamics of energy commodiies. Large jumps in energy prices are common, and are ofen riggered by invenory announcemens (e.g., Chiou-Wei, Linn, and Zhu, 2010). Our esimaes of he price response coefficiens help explain such exreme price changes. This informaion can be incorporaed ino dynamic models of energy commodiy prices. Finally, knowing sensiiviy of energy fuures prices o invenory news should help commercial users of energy and energy raders predic price moves based on heir forecass of invenory changes. For example, accurae esimaes of he responsiveness of energy commodiy prices o invenory news may influence he decision of power generaion faciliies o accumulae invenory or o acquire a capabiliy o swich beween naural gas and oil producs. 3 Baumeiser and Peersman (2012) provide evidence ha he increased volailiy of crude oil prices since he mid- 1980s is explained by declining shor-erm elasiciies of oil demand and oil supply. They show ha variance of oil demand and supply shocks acually declined during his period.

6 5 2. Idenificaion-Through-Censoring In a ypical even sudy, asse reurns are regressed on he unexpeced componen of he daa release. In he conex of invenory announcemens wih one invenory surprise and one commodiy reurn, his approach implies he following specificaion: z R, z I E I, (1) where R is he commodiy fuures reurn in he even window around he announcemen, he acual change in invenory, E I I is is he marke s expecaion of he invenory change before he release, and is an i.i.d error erm represening price movemens unrelaed o he daa release. The surprise componen of he invenory announcemen is z. To esimae he response coefficien more precisely, previous sudies use fuures reurns in a narrow inraday window surrounding he daa release. This approach reduces he variance of he error erm by excluding he influence of mos oher price-moving evens ha occur on he same day. To measure he unexpeced change in invenory, previous sudies use he Bloomberg consensus forecass. Gay e al. (2009) provide evidence ha his measure of marke expecaions is an improvemen over forecass obained from hisorical invenory daa using saisical approaches. However, he resuling measure of he unexpeced change in invenory conains measuremen error. This measuremen error comes from wo sources. Firs, he surveybased measure of expeced invenory changes is an imprecise proxy for marke expecaions a he ime of he announcemen. For example, he analys forecass used o compue he Bloomberg consensus forecas may come from an unrepresenaive sample of analyss, and a leas some of hese forecass are likely o be ou of dae a he ime of he release of invenory

7 6 daa. Second, he announced value of he invenory change is a noisy esimae of he rue change in invenory, for example, because he EIA s daa collecion process covers only a sample of sorage operaors. Therefore, he measured invenory surprise z can be represened as follows: z *, (2) z where * z is he rue invenory surprise and is he measuremen error. The marke response o he news is driven by he rue invenory surprise * z. However, he rue surprise is unobservable. Subsiuing he measured invenory surprise for is rue value leads o he following model, which is ypically esimaed wih OLS: R, z. (3) Since he error erm is correlaed wih he regressor, he OLS esimae of is biased. Under reasonable assumpions regarding he disurbance and he measuremen error, he OLS esimae of is: 2 ˆ 1. (4) OLS 2 2 * z Equaion (4) shows ha he OLS esimae is biased downwards (owards zero), undersaing he marke s response o he news. Rigobon and Sack (2008) argue ha he problem of measuremen error in he news variable is essenially an idenificaion problem. One can readily compue hree quaniies: he variance of he asse reurns, he variance of he measured news, and he covariance beween hem. However, hese momens are deermined by four 2 unknown parameers:,,, and * z 2 2. To solve his idenificaion problem, Rigobon and

8 7 Sack (2008) propose a mehodology called idenificaion-hrough-censoring (ITC). Scheduled daa releases occur a pre-specified imes. On non-even days boh he rue surprise * z and he measuremen error are zero. In effec, he measuremen error is censored on non-even days. Reurns on non-even days provide addiional informaion needed for idenificaion. This approach can be represened as follows: R * z D 1 D (5) z *, z where D is he se of announcemen days. Assuming ha 2 does no change on announcemen days, we can esimae his variance using reurns before he announcemen. 4 This model leads o he following se of momen condiions: 2 var R, * var R, 2 2 * var z, z z (6) cov R, 2* z. Solving hese equaions for he main parameer of ineres, we ge: R varr covr, z z var 1. (7) Volailiy varies over he rading day in a predicable manner. Therefore, we use preeven-day reurns in he same inraday inerval as he inerval used o compue even-day reurns. 4 Reurns do no have o be condiionally homoscedasic o saisfy his idenifying assumpion. Reurn volailiy varies over ime. Ye, unless he variance of he srucural shocks is sysemaically differen beween he even and pre-even days, i is appropriae o use he pre-even-day reurns o esimae he variance of he srucural shocks.

9 8 For example, when he Peroleum Saus Repor is released on he regular schedule (Wednesday a 10:30 a.m.), he even-window reurns are compued in he inerval from 10:25 a.m. o 10:40 a.m. on he day of he announcemen. Non-announcemen reurns are compued in he inerval from 10:25 a.m. o 10:40 a.m. on he day before. The Peroleum Saus Repor announcemens include invenory esimaes for hree peroleum commodiies: crude oil, gasoline, and disillae. Chang e al. (2009) show ha gasoline and disillae invenory surprises move crude oil fuures prices. Therefore, in addiion o he invenory surprise for crude oil, hey also use he aggregae peroleum invenory surprise. However, invenory surprises for crude oil, gasoline and disillae are likely o have a larger effec on prices of crude oil, gasoline, and heaing oil, respecively. The ITC esimaion can easily accommodae muliple markes and muliple daa surprises. In he case of hree daa surprises and hree markes, he model becomes: * * * R 1, 1,1z1, 2,1z2, 3,1z3, 1,, * * * R 2, 2,1z1, 2,2z2, 3,2z3, 2,, * * * R 3, 3,1z1, 3,1z2, 3,3z3, 3,, * z 1, z1, 1,, (8) * z 2, z2, 2,, * z 3, z3, 3,. Esimaing his model involves 27 unknown parameers (nine response coefficiens, hree variances of he srucural shocks of he noise erms i, i,, hree variances of he rue surprises * z i,, hree variances, hree covariances of he srucural shocks, hree covariances of he

10 9 surprises, and hree covariances of he noise erms). Fuures reurns and he observed daa surprises provide 27 momen equaions. This number includes six momen equaions provided by he variance-covariance marix of non-announcemen reurns and 21 momen equaions provided by he variance-covariance marix of announcemen window reurns and invenory surprises. The model parameers can be esimaed using he generalized mehod of momens (GMM). 5 I is also ineresing o examine he effec of he Peroleum Saus Repor announcemens on naural gas fuures prices. Therefore, we esimae a model ha includes reurns in all four energy fuures markes and he hree separae invenory surprises conained in he Peroleum Saus Repor. The model is similar o ha shown in equaion se (8) above, bu includes one addiional equaion for he fourh fuures reurn. Adding he fourh reurn equaion involves esimaing seven addiional parameers. The expanded variance-covariance marix of reurns and invenory surprises provides eleven addiional momen equaions. 6 Therefore, he ITC esimaor is over-idenified, as in Rigobon and Sack (2008). To compare he explanaory power of OLS and ITC esimaions, we compue he following pseudo-r 2 saisic for our ITC models: 1 (9) This saisic represens he fracion of reurn variance explained by he model. Because variances of he srucural shocks are esimaed using non-even reurns, his formula for pseudo-r 2 assumes ha invenory surprises explain all of he increase in reurn variance around he announcemen. 5 The GMM esimaion heory is based on asympoic principles. To make sure ha ITC esimaes are reliable in a finie sample, we esimaed an ITC model wih one daa surprise and wo markes using arificial daa. The ITC esimaes were close o he assumed populaion parameer values, showing ha he ITC esimaes are reliable. A deailed descripion of his exercise is available upon reques. 6 The momen equaions for his model are shown in he Appendix.

11 10 3. Daa and Sample Selecion 3.1. Energy Invenory Repors Our daa for he U.S. invenory of crude oil and oher peroleum producs are obained from he Weekly Peroleum Saus Repor compiled by he EIA. The daa include weekly ending commercial socks of crude oil, gasoline, and disillae fuel oil. The daa included in he Peroleum Saus Repor are colleced by he EIA on weekly surveys from a sample of operaors a several key poins along he peroleum producion and supply chain. 7 The key daa in he Peroleum Saus Repor are released a 10:30 a.m. (Easern Time) every Wednesday for he week ending he previous Friday. For some weeks which include holidays, releases are delayed by one day. The invenory daa for naural gas represen weekly esimaes of naural gas in underground sorage in he Lower 48 Saes. These esimaes are repored in he EIA s Weekly Naural Gas Sorage Repor. 8 The daa in his repor are obained from a survey of a sample of naural gas sorage operaors. The repor is released a 10:30 a.m. (Easern Time) every Thursday, excep for cerain weeks ha include Federal holidays. Hisorical daes and imes of release for boh invenory repors are obained from Bloomberg. Figure 1 shows hisorical values of invenory for crude oil, disillae, gasoline, and naural gas over our sample period. 9 The naural gas invenory exhibis srong seasonaliy. On average, naural gas invenory increases from April o November and falls during he heaing season in winer and early spring. [Inser Figure 1 abou here] 7 The survey and esimaion mehodology used in he Weekly Peroleum Saus Repor are described a hp:// 8 The mehodology used in he Naural Gas Sorage Repor is described a hp://ir.eia.gov/ngs/mehodology.hml. 9 These daa are available on he EIA s websie a hp://

12 Sample Selecion Our sample period exends from July 16, 2003 hrough June 27, This period conains 468 releases of he Peroleum Saus Repor and 467 releases of he Naural Gas Sorage Repor. During his period, here were 30 occasions when he wo repors were released simulaneously, a 10:30 a.m. on Thursdays. We exclude such simulaneous announcemens from he sample. A few observaions are removed due o missing fuures reurns daa. The final sample conains 435 observaions for each of he wo invenory repors Invenory Surprises To compue he unexpeced changes in invenory, or invenory surprises, we need a proxy for he marke expecaions a he ime of he invenory announcemen. Following he previous lieraure, we use he Bloomberg consensus forecass o measure expeced changes in invenory. The consensus forecas is compued as he median of individual analys forecass. We compue invenory surprises as he difference beween he acual and expeced change in invenory, divided by he invenory level. Summary saisics for invenory surprises are shown in Panel A of Table I. The sandard deviaion of invenory surprises for naural gas is lower han hose for crude oil and oher peroleum producs. This indicaes ha naural gas invenory changes are more predicable han peroleum invenory changes. This higher predicabiliy is likely o be relaed o he seasonal paern in he naural gas invenory. [Inser Table I abou here] 10 Our sample period begins in July 2003 because he firs Weekly Peroleum Saus Repor announcemen dae for which Bloomberg forecas daa is available for all hree peroleum commodiies is July 16, 2003.

13 Energy Fuures Reurns To examine he response of energy prices o invenory news, we use inraday fuures prices for WTI crude oil, gasoline, heaing oil, and naural gas. 11 These fuures conracs are raded on he New York Mercanile Exchange (NYMEX). Energy fuures markes are very liquid, wih he combined average daily rading volume in he four fuures conracs ha we examine exceeding 1.2 million conracs in he firs six monhs of Fuures markes have been shown o dominae price discovery in energy commodiies (e.g., Schwarz and Szakmary, 1994). We compue coninuously compounded reurns in an inraday even window surrounding he invenory announcemen surprises using prices of he nearby fuures conrac. The nearby conrac becomes relaively illiquid in is las few days of rading. Therefore, in he las hree days of rading of he nearby conrac we subsiue prices of he nex closes conrac. The even window is from five minues before o en minues afer he announcemen ime. 12 The 15- minue even window allows for a comparison of our resuls wih resuls of exising sudies looking a he marke response o energy invenory announcemens. For example, Gay, Simkins, and Turac (2009) also use 15-minue inervals conaining he announcemen. Summary saisics for fuures reurns are shown in Panel B of Table I. The able also provides non-announcemen day reurns, which are used in he ITC esimaion. We use equally mached numbers of even and non-even days. The non-announcemen day reurns are compued in he same 15-minue inraday inervals as he announcemen day reurns. For he Peroleum Saus Repor, he non-announcemen day reurns are compued using fuures prices from he day before he announcemen. Because he Peroleum Saus Repor is normally released exacly one 11 The fuures marke daa are obained from Genesis Financial Technologies. 12 The resuls wih longer even windows are similar.

14 13 day before he Naural Gas Sorage Repor, he non-announcemen day reurns for he Naural Gas Sorage Repor are compued using fuures prices from wo days before he announcemen. The able shows ha volailiy of peroleum fuures reurns approximaely doubles when he Peroleum Saus Repor is released. The increase in volailiy of he naural gas fuures around releases of he Naural Gas Sorage Repor is even larger. Volailiy of naural gas fuures reurns also increases somewha afer he release of he Peroleum Saus Repor. In conras, volailiy of peroleum fuures seems o be unchanged afer he release of he Naural Gas Sorage Repor. Figure 2 shows cumulaive average reurns (CARs) for crude oil and naural gas fuures around he invenory announcemens. The CARs are presened separaely for posiive and negaive invenory surprises. Fuures prices end o increase when he invenory is lower han expeced and decline when he invenory is larger han expeced. The negaive relaion beween excess supply shocks and fuures reurn movemens is consisen wih basic economic heory (prices fall when supply increases). The naural gas fuures reurns afer he release of he Naural Gas Sorage Repor end o be larger in absolue value han reurns in he crude oil fuures marke following he release of he Peroleum Saus Repor. There also seems o be some asymmery beween he effecs of posiive and negaive invenory surprises. Specifically, posiive invenory surprises end o be followed by bigger price moves, paricularly in he naural gas fuures marke. Overall, he figure shows ha he price impac of he news is immediae and appears o be permanen. [Inser Figure 2 abou here]

15 14 4. Empirical Resuls 4.1. Full Sample Resuls Esimaion resuls for he effec of peroleum invenory surprises on energy prices are presened in Table II. 13 According o he OLS esimaes, a 1% unexpeced increase in crude oil invenory leads o an approximaely 0.5% drop in he crude oil fuures price. The corresponding ITC esimae is more han wice as large. As expeced, he response coefficiens differ across he hree invenory surprises. For example, he ITC esimae of he crude oil response coefficien for crude oil invenory surprises is abou 1.06, whereas he crude oil response coefficien for gasoline invenory surprises is only abou The corresponding esimaes for gasoline fuures are abou 0.52 and 1.25, respecively. All hree peroleum invenory surprises have similar (and relaively small) impacs on he naural gas fuures prices. The average raio of ITC o OLS esimaes ranges from abou 1.7 for naural gas o abou 1.9 for crude oil, showing ha radiional even sudy regressions underesimae he energy marke responses o news abou peroleum invenory by approximaely a facor of wo. The esimaed proporion of he variance of he measured invenory surprise due o noise ( 2 2 z ) ranges from abou 49% for gasoline o abou 59% for crude oil, showing ha he measured invenory surprises are quie noisy. For comparison, he corresponding saisic for several major macroeconomic announcemens repored in Rigobon and Sack (2008) exceeded 90%. [Inser Table II abou here] To examine he effecs of he naural gas invenory announcemens on he four energy commodiy markes, we esimae an ITC model wih four markes and one invenory surprise. The model has 16 unknown parameers, and he variance-covariance marix of fuures reurns 13 Invenory surprises and fuures reurns are demeaned prior o esimaion.

16 15 and invenory surprises provides 25 momen equaions. The full sample ITC and OLS esimaion resuls are presened in Panel A of Table III. The OLS esimae of he response coefficien for naural gas fuures is abou 2.4, implying ha an unexpeced 1% increase in naural gas in sorage resuls in a 2.4% drop in naural gas fuures prices. This esimae is somewha larger in absolue value han he corresponding esimae for an earlier sample period in Gay e al. (2009). The ITC esimae of he response coefficien for naural gas is almos four imes as large as he OLS esimae. The esimaed proporion of he variance of he measured invenory surprise due o noise for naural gas invenory surprises is abou 73%. [Inser Table III abou here] Boh he OLS and ITC esimaes of he effec of naural gas invenory surprises on peroleum commodiy prices are saisically significan. For he cross-commodiy effecs of he naural gas invenory surprises, he raio of ITC o OLS esimaes ranges from abou 2.5 for gasoline o abou 3.3 for crude oil. The difference beween ITC and OLS coefficien esimaes repored in Tables II and III is economically significan. I implies ha supply and demand curves for energy commodiies are much less elasic han suggesed by he previous even sudy resuls. Based on he pseudo-r 2 values of he ITC esimaions, invenory surprises explain a much larger fracion of reurn variance han i appears from he explanaory power of he OLS regressions. Rigobon and Sack (2008) poin ou ha macroeconomic surprises included in he model ofen coincide wih he release of oher imporan daa. Volailiy induced by informaion releases no accouned for in he model may lead o an upward bias in he ITC esimaes. Similar consideraions apply in our case. For example, he Naural Gas Sorage Repor includes no only he oal amoun of naural gas in sorage (for which Bloomberg survey forecass are available),

17 16 bu also he sorage amouns for he Eas, Wes, and Producing regions. Even if he overall amoun of gas in sorage is equal o he marke s expecaions, here may be unexpeced changes in regional gas invenories ha may move energy fuures prices. Therefore, ITC esimaes may have some upward bias Injecion and Wihdrawal Seasons for Naural Gas Naural gas sorage involves wo calendar periods: he injecion season (April hrough Ocober) and he wihdrawal season (November hrough March). During injecion, invenory surprises are deermined o a large exen by supply shocks relaed o he echnology of gas sorage. During wihdrawal, unexpeced changes in naural gas invenory are driven primarily by demand shocks due o weaher. Gay e al. (2009) argue ha, since he demand curve for naural gas is less elasic han he supply curve, prices should respond more srongly o sorage surprises during he injecion season han during he wihdrawal season. They find empirical suppor for his hypohesis. Gay e al. (2009) also provide evidence ha invenory changes are less predicable during he wihdrawal season. An alernaive explanaion for he seasonal variaion in he price response o invenory news is ha, due o a larger proporion of noise in he variance of he measured invenory news, he OLS aenuaion bias is larger during he wihdrawal season. To examine his issue, we esimae he four-commodiy model for he Naural Gas Sorage Repor announcemens separaely for injecion and wihdrawal seasons. The resuls are provided in Panels B and C of Table III. Consisen wih Gay e al. (2009), he OLS esimae of he own-commodiy response o naural gas sorage surprises is abou 55% larger in absolue value during he injecion season han during wihdrawal. The ITC esimae of he own-commodiy response coefficien is sill larger in absolue value during he injecion season han during wihdrawal, bu only by abou

18 17 33%. The OLS esimaes of cross-commodiy effecs are no saisically significan during he wihdrawal season. The corresponding ITC esimaes for crude oil and heaing oil are saisically significan. Consisen wih our expecaions, here is evidence ha noise accouns for a larger proporion of he variance of he measured invenory surprises during he wihdrawal season The Effec of Analys Forecas Dispersion on Response Coefficiens Chiou-Wei e al. (2010) examine he effec of marke uncerainy on he response of he naural gas fuures prices o he sorage surprises. They use he sandard deviaion of he individual analyss forecass of he change in invenory as a proxy for marke uncerainy. They find ha he marke response o invenory surprises esimaed wih OLS is significanly weaker a imes of greaer dispersion in analys forecass. In a sudy of he response of sock reurns o earnings surprises, Imhoff and Lobo (1992) also find ha firms wih high dispersion of analys forecass exhibi lile or no price response o earnings surprises. They provide evidence ha analys forecas dispersion is more likely o proxy for noise in earnings surprises han for uncerainy abou he firm s earnings prospecs. Abarbanell, Lanen and Verrecchia (1995) show heoreically ha he price response coefficien is negaively relaed o forecas dispersion for wo reasons. Firs, measuremen error in earnings surprises increases in he dispersion of analys forecass and inroduces a downward bias in he earnings response coefficiens. Second, even when he earnings surprise is measured wihou error, he marke response increases in forecas precision, which is negaively relaed o forecas dispersion. In he conex of earnings forecass, precision reflecs he exen o which curren earnings is informaive abou fuure earnings and herefore abou he firm value. Permanen changes in earnings provide a more precise signal abou fuure earnings han do ransiory earnings changes.

19 18 Figure 3 shows he sandard deviaion of analys forecass of naural gas invenory changes divided by he repored level of invenory. Consisen wih Gay e al. (2009), dispersion of analys forecass ends o be much larger during he wihdrawal season. The figure also shows a decline in he average level of forecas dispersion over our sample period. [Inser Figure 3 abou here] We examine wheher he effec of analys forecas dispersion on he esimaes of he marke response o he Naural Gas Sorage Repor announcemens is driven by measuremen error in invenory surprises. 14 The sample is pariioned ino wo pars based on wheher he analys forecas dispersion for a given announcemen is above or below is median value for he enire sample. Panels D and E of Table III show OLS and ITC esimaion resuls for hese wo subsamples. As expeced, he OLS esimae of he response of naural gas prices o sorage announcemens is much larger in he low dispersion subsample. The difference beween he wo esimaes is almos a facor of hree ( 5.0 in he low dispersion subsample versus 1.8 in he high dispersion subsample). The OLS R 2 of he naural gas regression is wice as high in he low dispersion subsample as in he high dispersion subsample. The ITC esimae of he response coefficien for naural gas ranges from abou 10.8 in he low dispersion subsample o 7.8 in he high dispersion subsample. The esimaed proporion of he variance of he measured invenory surprise due o noise is abou 77% in he high dispersion subsample compared o abou 52% in he low dispersion subsample. These resuls imply ha much of he effec of analys forecas dispersion on he OLS esimae of he response coefficien for naural gas is due o he aenuaion bias induced by measuremen error in invenory surprises. These findings also provide suppor for using he dispersion of analys 14 This secion focuses on he Naural Gas Sorage Repor announcemens because heir single invenory surprise allows for sraighforward pariioning of he sample based on analys forecas dispersion.

20 19 forecass as a proxy for measuremen error. Even afer correcing for he bias induced by noise in invenory surprises, he marke response o he news is somewha sronger in he low dispersion subsample. This finding is consisen wih he noion ha he forecas dispersion conveys informaion abou precision of analys forecass. 15 The fac ha forecas dispersion increases during he wihdrawal season raises he quesion wheher he difference in price response coefficiens beween he injecion and wihdrawal seasons is driven by variaion in forecas dispersion. We esimaed an OLS regression ha included he invenory surprise and he invenory surprise ineraced wih a dummy variable for he wihdrawal season. Consisen wih he resuls in Panels B and C of Table III, he coefficien of he ineracion erm is posiive and saisically significan. However, his coefficien becomes insignifican when an ineracion of he invenory surprise wih a dummy for high forecas dispersion is added o he model. 16 This finding suppors he conclusion ha he smaller OLS esimae of he response o invenory surprises during he wihdrawal season is due, a leas in par, o a larger proporion of noise in invenory surprises. A possible reason behind he somewha smaller ITC esimae of he price response during he wihdrawal season is lower precision of invenory surprises during wihdrawal. As menioned above, invenory changes during wihdrawal are driven primarily by demand shocks due o weaher. Such demand shocks end o be emporary (e.g., Pindyck, 2001), which implies lower precision of invenory surprises. 15 Hausch and Hess (2007) and Burgsahler and Chuk (2010) use he analys forecas dispersion as a proxy for precision. 16 These regression resuls are no abulaed o save space bu are available upon reques.

21 Decoupling of Naural Gas and Peroleum Markes This subsecion examines wheher he cross-commodiy effecs of peroleum and naural gas invenory announcemens have diminished in recen years. Figure 4 shows weekly spo prices of naural gas and energy equivalen prices of WTI crude oil. 17 Afer collapsing during he 2008 recession, oil prices resumed heir climb. Naural gas prices, however, have drifed downwards, afer briefly rading a energy pariy wih oil in December This divergence beween naural gas and oil prices is consisen wih Ramberg and Parsons (2012), who show ha he coinegraing relaionship beween he wo prices is no sable hrough ime. By June 2012, he energy equivalen price of oil exceeded he price of naural gas by a facor of six. This apparen decoupling of oil and gas prices has coincided wih he shale gas boom in he U.S. [Inser Figure 4 abou here] We begin by compuing he daily realized correlaion beween naural gas and crude oil fuures reurns as follows: 18 RC m i1 m i1 R o, i R R 2 o, i g, i R 2 g, i, (9) where R o, i and R, are coninuously compounded reurns of he mos acively raded crude oil g i and naural gas fuures conracs, respecively, in a 5-minue inraday inerval i, and m is he number of such inervals in rading day. 17 Naural gas prices are normally quoed in dollars per million Briish hermal unis (BTU). One barrel of WTI crude oil conains million BTUs. Therefore, energy equivalen price of crude oil can be compued by dividing he WTI crude price per barrel by This measure is proposed by Andersen, Bollerslev, Diebold, and Labys (2001) and used by Wang, Wu, and Yang (2008), among ohers.

22 21 Figure 5 shows a pronounced decline in he correlaion beween he wo energy fuures markes in lae The Quand-Andrews breakpoin es for one or more unknown srucural breakpoins idenifies a shif in he mean of he realized correlaions on December 11, The mean of he realized correlaion declined from abou 0.45 before December 2009 o abou 0.09 afer his srucural break. This decline is srongly saisically significan. [Inser Figure 5 abou here] Table IV presens correlaions of even-window reurns in he four energy fuures markes. The correlaions beween naural gas and peroleum fuures reurns afer he release of he Naural Gas Sorage Repor are boh economically meaningful and srongly saisically significan during he period before December 11, These correlaions become small and saisically insignifican in he more recen period. A similar paern is observed for reurn correlaions around he Peroleum Saus Repor announcemens, alhough wo of he hree correlaion coefficiens for naural gas remain saisically significan afer December [Inser Table IV abou here] Table V shows esimaes of he effec of he naural gas invenory announcemens on he four energy commodiy fuures markes before and afer he decoupling of oil and naural gas markes. The OLS esimae of he response coefficien for naural gas is more han wice as high in he more recen period compared o he period before December 11, However, he ITC esimaes of his coefficien show lile change from one subperiod o he nex. The larger aenuaion bias in he OLS esimae in he firs subperiod is due o a greaer proporion of noise in he measured invenory surprises. The proporion of he variance of he measured invenory surprise due o noise declines from abou 78% o abou 53% in he more recen subperiod.

23 22 The Naural Gas Sorage Repor is he EIA s only repor designaed a Principal Federal Economic Indicaor. The repor received his designaion in January In mid-2008, he EIA modified is weekly underground naural gas sorage sample and sample selecion procedure o reflec changes in he indusry and improve daa qualiy. 20 According o he EIA, he new procedure improves he accuracy of sorage esimaes. The finding of less noise in he naural gas invenory surprises in he pos-2009 period may be due o increased accuracy of sorage daa. Boh he OLS and ITC esimaes of he effec of he naural gas invenory news on peroleum commodiies are saisically significan during he period before December 11, Afer his dae, however, none of he cross-commodiy effecs are saisically significan. The OLS R 2 esimaes for crude oil, gasoline and heaing oil are close o zero in he period afer December 11, These findings are consisen wih decoupling of peroleum and naural gas markes. [Inser Table V abou here] OLS and ITC esimaion resuls for he Peroleum Saus Repor announcemens before and afer December 11, 2009 are repored in Table VI. Boh OLS and ITC esimaes indicae ha invenory surprises have a significan effec on naural gas prices during he firs subperiod. In he second subperiod, however, naural gas fuures prices show significan response only o gasoline invenory surprises. The OLS R 2 of he naural gas regression falls from abou 26% before December 11, 2009 o only abou 5% hereafer. [Inser Table VI abou here] Some power plans and indusrial users of energy have he capabiliy o swich beween naural gas and peroleum producs. This fuel subsiuion links naural gas and oil prices (e.g., 19 See hp://ir.eia.gov/ngs/wngsrevaluaion.pdf 20 See hp://ir.eia.gov/ngs/samplechg.hml for a deailed discussion of hese changes.

24 23 Brown and Yücel, 2008) and is likely o conribue o he cross-commodiy effecs of oil and gas invenory announcemens. The increased shale gas producion in he U.S. in recen years has led o a glu of naural gas and sen gas prices o hisorical lows relaive o oil prices. As a resul, mos faciliies wih fuel-swiching capabiliy have probably swiched o naural gas. Only a large move of oil and gas prices owards energy pariy would induce hese faciliies o consider swiching o oil producs. Price changes caused by invenory news are small compared o he recenly observed deviaion of oil and gas from energy pariy. Under hese condiions, invenory surprises should have lile effec on he relaive araciveness of oil and gas as subsiue fuels. This may conribue o our finding ha he fundamenal link beween peroleum and naural gas markes has weakened in recen years. 5. Summary and Conclusion This sudy examines he effec of unexpeced changes in invenory on energy prices. Using inraday fuures daa and invenory surprises for peroleum producs and naural gas, we esimae he price response coefficiens using radiional even sudy regressions and Rigobon and Sack s ITC mehodology. The resuls show ha he noise in esimaed invenory surprises and he resuling aenuaion bias in OLS esimaes are quie large. The ITC coefficien esimaes are abou wice as large as OLS esimaes for peroleum commodiies and abou four imes as large as OLS esimaes for naural gas. Thus, energy prices are more srongly influenced by excess supply and demand shocks han shown in previous sudies. Our resuls help o explain why we ofen observe large movemens in energy prices in response o moderae demand and supply shocks. These findings are a sep owards showing ha high volailiy of energy prices can be explained by fundamenal news.

25 24 Appendix. Momen Condiions Used in ITC Esimaion of he Response of Crude Oil, Gasoline, Heaing Oil, and Naural Gas Fuures Prices o Peroleum Saus Repor Announcemens This esimaion uses an ITC model wih four markes and hree invenory surprises. Esimaing his model involves 34 unknown parameers. The variance-covariance marix of reurns and invenory surprises provides 38 momen condiions: 1). 2). 3). 4). 5). 2, 2, 2, 6). 2, 2, 2, 7). 2, 2, 2, 8). 2, 2, 2, 9). 10). 11). 12).,,, 13).,,, 14).,,, 15).,,, 16).,,, 17).,,, 18).,,, 19).,,,

26 25 20).,,, 21).,,, 22).,,, 23).,,, 24).,,, 25).,,, 26).,,, 27).,,,,, 28).,,,,, 29).,,,,, 30).,,,,, 31).,,,,, 32).,,,,, 33).,, 34).,, 35).,, 36).,, 37).,, 38).,,

27 26 References Abarbanell, J. S., Lanen, W. N., & Verrecchia, R. E. (1995). Analyss forecass as proxies for invesor beliefs in empirical research. Journal of Accouning and Economics, 20, Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001). The disribuion of exchange rae volailiy. Journal of he American Saisical Associaion, 96, Bauer, R., & Orazem, P. (1994). The raionaliy and price effecs of U.S. Deparmen of Agriculure forecass of oranges. Journal of Finance, 49, Baumeiser, C., & Peersman, G. (2012). The role of ime-varying price elasiciies in accouning for volailiy changes in he crude oil marke. Journal of Applied Economerics, forhcoming. Boudoukh, J., Richardson, M., Shen, J., & Whielaw, R. F. (2007). Do asse prices reflec fundamenals? Freshly squeezed evidence from he FCOJ marke. Journal of Financial Economics, 83, Brown, S. P. A., & Yücel, M. K. (2008). Wha drives naural gas prices? Energy Journal, 29, Burgsahler, D., & Chuk, E. (2010). Earnings precision and he relaions beween earnings and reurns. Working paper, Universiy of Washingon. Büyükşahin, B., & Harris, J. H. (2011). Do speculaors drive crude oil fuures? Energy Journal, 32, Chang, C., Daouk, H., & Wang, A. (2009). Do invesors learn abou analys accuracy? A sudy of he oil fuures marke. Journal of Fuures Markes, 29, Charah, A., Miao, H., & Ramchander, S. (2012). Does he price of crude oil respond o macroeconomic news? Journal of Fuures Markes, 32, Chiou-Wei, S., Linn, S., & Zhu, Z. (2010). Fundamenal news and he behavior of commodiy prices: Price discovery and jumps in U.S. naural gas prices, Working Paper, Universiy of Oklahoma. Gay, G. D., Simkins, B. J., & Turac, M. (2009). Analys forecass and price discovery in fuures markes: The case of naural gas sorage. Journal of Fuures Markes, 29, Halova, M. W. (2012). Gas does affec oil: Evidence from inraday prices and invenory announcemens. Working paper, Washingon Sae Universiy. Hansen, L. P. (1982). Large sample properies of Generalized Mehod of Momens esimaors. Economerica, 50,

28 27 Hausch, N., & Hess, D. (2007). Bayesian learning in financial markes: Tesing for he relevance of informaion precision in price discovery. Journal of Financial and Quaniaive Analysis, 42, Imhoff, E. A., & Lobo, G. J. (1992). The effec of ex ane earnings uncerainy on earnings response coefficiens. Accouning Review, 67, Karali, B., & Thurman, W. N. (2009). Announcemen effecs and he heory of sorage: An empirical sudy of lumber fuures. Agriculural Economics, 40, Kilian, L., & Murphy, D. (2012). The role of invenories and speculaive rading in he global marke for crude oil. Journal of Applied Economerics, forhcoming. Linn, S. C., & Zhu, Z. (2004). Naural gas prices and he gas sorage repor: Public news and volailiy in energy fuures markes. Journal of Fuures Markes, 24, Pindyck, R. S. (2001). The dynamics of commodiy spo and fuures markes: A primer. Energy Journal, 22, Ramberg, D. J. & Parsons, J. E. (2012). The weak ie beween naural gas and oil prices. Energy Journal, 33, Rigobon, R., & Sack, B. (2008). Noisy macroeconomic announcemens, moneary policy, and asse prices. In J. Y. Campbell (Ed.), Asse Prices and Moneary Policy (pp ). Chicago: Universiy of Chicago Press. Roll, R. (1984). Orange juice and weaher. American Economic Review, 74, Schwarz, T. V., & Szakmary, A. (1994). Price discovery in peroleum markes: Arbirage, coinegraion, and he ime inerval of analysis. Journal of Fuures Markes, 14, Tang, K., & Xiong, W. (2012). Index invesmen and he financializaion of commodiies. Financial Analyss Journal, 68, Wang, T., Wu, J., & Yang, J. (2008). Realized volailiy and correlaion in energy fuures markes. Journal of Fuures Markes, 28, Whie, H. (1980). A heeroskedasiciy-consisen covariance marix esimaor and a direc es for heeroskedasiciy. Economerica, 48,

29 28 Table I Summary Saisics for Invenory Surprise and Fuures Reurns Panel A. Invenory Surprises (%) Mean Median S. deviaion Minimum Maximum Peroleum Saus Repor Crude Oil Gasoline Disillaes Naural Gas Sorage Repor Panel B. Fuures Reurns (%) Announcemen Days Non-announcemen Days Mean Median S. deviaion Mean Median S. deviaion Peroleum Saus Repor Crude Oil Gasoline Heaing Oil Naural Gas Naural Gas Sorage Repor Naural Gas Crude Oil Gasoline Heaing Oil The invenory surprises are compued as he difference beween he acual and expeced change in invenory, divided by he invenory level. The coninuously compounded fuures reurns are compued in he inraday even window surrounding he invenory announcemen. The even window is from 5 minues before o 10 minues afer he announcemen ime. Non-announcemen day reurns are compued in he same ime inerval one day before he Peroleum Saus Repor announcemens and wo days before he Naural Gas Sorage Repor announcemens. The sample period is from July 16, 2003 hrough June 27, The number of observaions is 435.

30 29 Table II Response of Energy Fuures Prices o Peroleum Saus Repor Announcemens Crude Oil Surprise Crude Oil -0.48*** (0.04) Gasoline -0.29*** (0.05) Heaing Oil -0.34*** (0.04) Naural Gas -0.17*** (0.03) OLS Esimaes Gasoline Disillae Surprise Surprise -0.31*** -0.17*** (0.04) (0.03) -0.66*** -0.13*** (0.05) (0.05) -0.23*** -0.35*** (0.04) (0.04) -0.16*** -0.13*** (0.03) (0.03) R 2 Crude Oil Surprise *** (0.15) *** (0.12) *** (0.10) ** (0.09) Gasoline Surprise -0.55*** (0.09) -1.25*** (0.13) -0.41*** (0.08) -0.24*** (0.06) ITC Esimaes Disillae Surprise -0.31*** (0.09) -0.23*** (0.09) -0.72*** (0.11) -0.23*** (0.07) 2 2 Proporion of Measured Surprise Due o Noise 59% 49% 55% z Pseudo- Average Raio R 2 ITC/OLS The able shows he esimaed responses of energy fuures reurns o Peroleum Saus Repor announcemens. The sample period is from July 16, 2003 hrough June 27, The number of observaions is 435. The response coefficiens are esimaed using (1) equaion-by-equaion OLS wih he Whie (1980) heeroskedasiciy consisen covariance marix and (2) idenificaion-hrough-censoring (ITC) approach. All variables are demeaned prior o esimaion. The null hypohesis of he Hansen (1982) es ha he over-idenifying resricions of he ITC model are valid is no rejeced a he 5% level. Sandard errors are shown in parenheses. *, **, *** indicae saisical significance a 10%, 5%, and 1% levels, respecively.

31 30 Table III Response of Energy Fuures Prices o Naural Gas Sorage Repor Announcemens OLS Esimaes Response Coefficien R 2 Response Coefficien ITC Esimaes Pseudo- R 2 Raio ITC/OLS Panel A. Full Sample (N=435) Naural Gas (0.25)*** (0.94)*** Crude Oil (0.08)* (0.12)*** Gasoline (0.07)** (0.13)** Heaing Oil (0.07)** (0.12)*** Panel B. Injecion Season (N=255) Naural Gas (0.43)*** (0.99)*** Crude Oil (0.08)*** (0.14)*** Gasoline (0.10)** (0.15)*** Heaing Oil (0.09)*** (0.13)*** Panel C. Wihdrawal Season (N=180) Naural Gas (0.31)*** (1.39)*** Crude Oil (0.11) (0.15)** Gasoline (0.09) (0.17) Heaing Oil (0.10) (0.15)** Panel D. Low Dispersion of Analys Forecass (N=218) Naural Gas (0.45)*** (1.04)*** Crude Oil (0.09)** (0.15)*** Gasoline (0.10) (0.14)** Heaing Oil (0.09)** (0.12)*** Panel E. High Dispersion of Analys Forecass (N=217) Naural Gas (0.24)*** (1.44)*** Crude Oil (0.09) (0.15)** Gasoline (0.08)* (0.17) Heaing Oil (0.09) (0.17)*** Proporion of Measured Surprise Due o Noise 2 2 z 73% 69% 73% 52% 77% The able shows he esimaed responses of energy fuures reurns o unexpeced changes in naural gas invenory. The sample period is from July 16, 2003 hrough June 27, The response coefficiens are esimaed using (1) equaion-by-equaion OLS wih he Whie (1980) heeroskedasiciy consisen covariance marix and (2) idenificaion-hrough-censoring (ITC) approach. All variables are demeaned prior o esimaion. The null hypohesis of he Hansen (1982) es ha he over-idenifying resricions of he ITC model are valid is no rejeced a he 5% level. Sandard errors are shown in parenheses. *, **, *** indicae saisical significance a 10%, 5%, and 1% levels, respecively.