Recent studies provide contradictory evidence about the impact of speculation on commodity prices. Rather than directly evaluating this relationship we instead use a novel approach to assess if speculation can inform our choice of factor inclusion in modelling oil futures

Alquist

Andreasson

Bates

Benjamini

Büyüksahin

Büyükşahin

Chantziara

Fabian Gogolin

Fattouh

Fearghal Kearney

Juvenal

Kilian

Morana

Working

Crossref

Economics Letters

Does speculation impact what factors determine oil futures prices?

Gogolin, F

Kearney, F

White Rose Research Online

This is a repository copy of Does speculation impact what factors determine oil futures prices?.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/140578/Version: Accepted VersionArticle:Gogolin, F and Kearney, F (2016) Does speculation impact what factors determine oil futures prices? Economics Letters, 144. pp. 119-122. ISSN 0165-1765 https://doi.org/10.1016/j.econlet.2016.05.0072016 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0license http://creativecommons.org/licenses/by-nc-nd/4.0/.eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Does Speculation Impact What Factors Determine OilFutures Prices?Fabian Gogolin∗ Fearghal Kearney†May 29, 2016AbstractRecent studies provide contradictory evidence about the impact of speculation oncommodity prices. Rather than directly evaluating this relationship we instead usea novel approach to assess if speculation can inform our choice of factor inclusion inmodelling oil futures.Keywords: Oil futures; speculation; fundamentals; statistical factorsJEL Classications: G10; G13; G15Highlights:• Consider observable and unobservable factors separately for oil futures prices• Assess each class of factors in subsamples split by speculative activity• Uncover signicant outperformance utilising a composite prediction framework1 IntroductionRecent sharp price declines in crude oil markets have increased the focus on whatfactors determine the observed market dynamics. Movements in commodity prices areoften attributed to speculation, with Morana (2013) and Juvenal and Petrella (2015) con-cluding that speculative shocks are a relevant determinant of oil price changes. However,evidence from Büyüksahin and Harris (2011), and Alquist and Gervais (2013) contradictthis, nding that the correlation between a speculation index and daily price changes isnear zero. We approach the question of speculative impact from a dierent perspective;∗Queen's Management School, Queen's University Belfast, BT9 5EE, UK. E-mail address:F.gogolin@qub.ac.uk.†Corresponding author at: Queen's Management School, Queen's University Belfast, BT9 5EE, UK.Tel.: +44 (0) 28 9097 4795; E-mail address: F.kearney@qub.ac.uk.1asking instead how speculation impacts the modelling accuracy of two distinct classesof factors proposed in oil futures literature. The two approaches we refer to are thosecomprised of observable fundamental macroeconomic, and unobservable latent principalcomponent, factors.Kilian and Murphy (2014) note that anyone buying crude oil not for current con-sumption, but for future use is a speculator from an economic point of view. In practice,we consider market participants who take positions to prot from an expected change inthe price of oil as speculators. Due to the increased nancialization of commodity fu-tures markets, it has been proposed that speculation is now a major component of prices.However, not all speculation is the same. Some speculators provide liquidity and assistin price discovery, meaning that a certain level of speculation is required for a marketto function correctly, whilst the activities of other speculators are said to destabilise themarket and distort prices (Fattouh et al. 2013).In this article, we refrain from dening a single value as a cuto point for high orexcessive levels of speculative activity, instead utilising a range of values corresponding toproxies for elevated levels of speculation. Our study contributes by nding that for ele-vated levels of speculative activity dierences between the model accuracy of fundamentaland latent factor approaches are uncovered; dierences that are not present over the fullsample period. The empirical analysis indicates that latent factors pick up additionalprice dynamics not captured by macroeconomic fundamentals, a main contribution ofour study. Through the proposal of a novel composite prediction framework we demon-strate that utilising speculative positions to inform factor selection leads to statisticallysignicantly increased accuracy in modelling oil futures price changes.2 Empirical approachWe follow previous literature (Büyüksahin and Harris 2011, Alquist and Gervais 2013,and Büyüksahin and Robe 2014) by adopting the Working (1960) T index as a proxy forspeculative activity. It is dened as follows:T = 1 +SSHL+HSif HS ≥ HLT = 1 +SLHL+HSif HS < HLwhere SS (SL) is the open interest of speculators (non-commercials rms) holding netshort (long) positions andHS (HL) is the open interest of hedgers (commercial rms) whohold a net short (long) position. The ratio is predicated on the concept that speculatorsare necessary only insofar as they constitute a counterparty for hedgers. As highlighted2by Büyüksahin and Harris (2011), what might be considered speculation in the marketcould simply be commercials not hedging or commercials taking a stance on future oilprice movements. As there is no one Working's T index value that indicates excessivespeculation we incrementally use values in the 50-90 percentile range as a measure ofincreasing levels of speculative activity.In order to consistently compare the performance of the distinct classes of factorswe specify structurally similar integrated models for both the unobservable principalcomponent factors, and the observable macroeconomic factors. Firstly, motivated byChantziara and Skiadopoulos (2008), we consider the following statistical model for oilfutures returns:△CLτt = β0 + β1PC1t−1 + β2PC2t−1 + β3PC3t−1 + εt,where PC1, PC2, and PC3 denote the rst, second, and third principal componentsof the WTI futures curve, and △CL denotes the log return of the continuous WTI crudeoil (CL) contract of maturity τ at time t. We refer to this model henceforth, as PC.Secondly, we consider a similarly constructed linear model, this time comprised of oilfutures macroeconomic factors from Andreasson et al. (2016):△CLτt = β0 + β1△SP500t−1 + β2△V IXt−1 + β3△USDt−1 + β4△EcPolt−1 + εt,where △SP500 denotes the log return of the S&P500 index, △V IX denotes the logchange in the VIX volatility index, △USD denotes the log return of the trade weightedUS dollar index, and △EcPol denotes the log change in the economic policy uncertaintyindex for the United States of America. We refer to this model henceforth, as Macro.1Finally, we produce a composite prediction informed by underlying speculative activity.This approach is motivated by Bates and Granger (1969) who were pioneers in arguingthat given the availability of more than one prediction of the same variable, it is rarely(if ever) optimal to identify the best of the competing predictions and use it in isolation.In line with Chantziara and Skiadopoulos (2008) we use daily WTI crude oil CL1-CL9prices obtained from the CME Group. The time period for our sample is January 2007to March 2016. The macroeconomic factors dataset comprises daily VIX quotes obtainedfrom CBOE, S&P500 index values obtained from Yahoo! Finance, and Trade Weighted1The aim of this article is to compare two distinct classes of factors, not to prescribe a specic funda-mental factor model for modelling crude oil futures. In comparison with literature outlining macroeco-nomic factors that model the dynamics of crude oil spot markets there is a relative paucity of literatureproposing relevant fundamental determinants of oil futures prices. In further testing we specify an alter-native model by including oil inventory (a factor popular in modelling oil spot prices) as an additionalmacroeconomic factor to those outlined in Anderson et al. (2016). However, regression results show thatthe inclusion of inventory is not signicant.3Table 1: Performance MeasuresPC RMSE PC MAE Macro RMSE Macro MAE RW RMSE RW MAECL1 0.0253 0.0174 0.0253 0.0175 0.0371 0.0255CL2 0.0234 0.0165 0.0234 0.0166 0.0343 0.0242CL3 0.0224 0.0159 0.0224 0.0159 0.0328 0.0232CL4 0.0217 0.0154 0.0216 0.0154 0.0318 0.0226CL5 0.0210 0.0149 0.0209 0.0149 0.0307 0.0219CL6 0.0204 0.0145 0.0204 0.0145 0.0298 0.0213CL7 0.0200 0.0141 0.0199 0.0142 0.0292 0.0208CL8 0.0196 0.0138 0.0195 0.0139 0.0286 0.0204CL9 0.0191 0.0135 0.0191 0.0135 0.0280 0.0199The predictive accuracy of the PC, Macro, and Random Walk (RW) models for each maturity WTI contract over the January2007 to March 2016 period are given. The results of a t-test of statistically signicantly better performance measures betweenPC and Macro factors, are indicated with asterisks (*) in the Macro and PC columns. * denotes signicance at the 10% level, **denotes signicance at the 5% level, and *** denotes signicance at the 1% level.US Dollar Index and US Economic Policy Uncertainty Index, both obtained from FRED.The Commitment of Traders Futures Only report obtained from the CFTC, is adopted tocalculate Working's T index values. Root mean squared error (RMSE) and mean absoluteerror (MAE) loss functions are employed to assess the predictive accuracy of each class offactors.2 As in all empirical studies considering multiple hypothesis tests about a singledataset, there is a risk of falsely inferring signicance, known as data snooping bias. Weexplicitly address this issue through the use of a formal multiple comparisons framework,namely the false discovery rate as proposed by Benjamini and Hochberg (1995), to checkour results for robustness and uncover instances of truly signicant outperformance.3 Findings and analysisAfter tting both models to the data we measure the predictive accuracy of eachclass of factors. We can see from the RMSE and MAE measures in Table 1 that theperformance of the PC and Macro factors are almost identical across the term structureof the WTI futures curve over the full sample period. As expected, a formal t-test of bothperformance measures for each of the maturity contracts fails to yield any signicantoutperformance. Random Walk performance metrics are also provided for benchmarkpurposes, indicating that the accuracy of both models are better than would be expectedby chance alone.We now examine if underlying speculative activity has any impact on the factorsdetermining WTI futures returns. We do this by referring to observations with Working'sT values of greater than or equal to 50, 60, 70 , 80, and 90 percentile full sample indexlevels respectively, as most speculative with all other observations being categorised2The squared error and absolute error for each observation are used to conduct the t-tests.4Table 2: Performance Measures for Speculation SubsamplesPercentile Working's T PC RMSE PC MAE Macro RMSE Macro MAELeast speculative subsample50% <1.1153 0.0151 0.0108 0.0151 0.010960% <1.1243 0.0161 0.0116 0.0162 0.011770% <1.1346 0.0174 0.0124 0.0174 0.012480% <1.1472 0.0191 0.0135 0.0189 0.013590% <1.1639 0.0203 0.0143 0.0202 0.0143Most speculative subsample50% ≥1.1153 0.0263 0.0195 0.0262 0.019560% ≥1.1243 0.0275 0.0204 0.0274 0.020470% ≥1.1346 0.0286 0.0216 0.0286 0.021680% ≥1.1472 0.0290 0.0217 0.0292 0.022090% ≥1.1639 0.0296 0.0225 0.0302 0.0230RMSE and MAE denote the RMSE and MAE performance measures averaged over contracts of CL1-CL9 maturity. They aregiven for the PC and Macro factors using subsample periods based on increasing percentile Working's T index (1960) values asgiven in the Working's T column. Observations with Working's T values greater than or equal to 50-90 percentile levelsrespectively are categorised as most speculative with other periods being referred to as least speculative.as  least speculative. For example, if we use the 90% percentile calculated Working's Tindex as the cuto point, we refer to observations with Working's T values greater than orequal to 1.1639 as most speculative (228 days) and all other periods as  least speculative(2057 days). Table 2 splits the analysis into these subsample periods based on speculativeactivity. Firstly, analysing the 50-90 percentile least speculative Working's T subsamplewe again observe very little dierence in terms of predictive accuracy between the adoptionof Macro and PC factors. The strongest indication of a divergence in performance is forthe least speculative 80% of the sample where we observe an RMSE value of 0.0191versus 0.0189 for Macro and PC respectively, providing an initial suggestion that Macrofactors outperform in less speculative periods. The results for the subsample periodswith elevated levels of speculative activity are more clear-cut however. Using the mostspeculative 10-20% of the sample, we see that PC factors outperform Macro factors withMAE metrics of 0.0217 vs. 0.0220, and 0.0225 vs. 0.0230, for 80 and 90 percentileWorking's T values, respectively. This demonstrates that in the sample's most speculativeperiods it is advantageous to adopt PC factors whereas other more benign periods aremore accurately modelled using Macro factors.To both highlight and formally test this dynamic we construct a simple combinedmodel (Combo model, henceforth) utilising the prediction from the PC factor model forthe subsample of observations with calculated Working's T values of greater than or equalto the 90 percentile gure and the prediction from the Macro factor model in all other5Table 3: Combo Model Performance MeasuresCombo RMSE Combo MAE Macro RMSE Macro MAE PC RMSE PC MAECL1 0.0252 0.0175 0.0253** 0.0175** 0.0253 0.0174CL2 0.0233 0.0165 0.0234* 0.0166** 0.0234 0.0165CL3 0.0223 0.0159 0.0224* 0.0159** 0.0224 0.0159CL4 0.0216 0.0154 0.0216* 0.0154** 0.0217* 0.0154CL5 0.0208 0.0149 0.0209* 0.0149** 0.0210* 0.0149CL6 0.0203 0.0145 0.0204* 0.0145** 0.0204* 0.0145CL7 0.0198 0.0141 0.0199* 0.0142** 0.0200* 0.0141CL8 0.0194 0.0138 0.0195** 0.0139*** 0.0196* 0.0138CL9 0.0190 0.0135 0.0191* 0.0135** 0.0191 0.0135Overall 0.0213 0.0151 0.0214*** 0.0152*** 0.0214*** 0.0151The predictive accuracy of the PC, Macro, and Combo models over the January 2007 to March 2016 period for each maturityWTI contract are given. Overall denotes the performance metric value averaged over all of the CL1-CL9 maturities. Theresults of a t-test of statistically signicantly outperformance for the Combo model over the Macro and PC models respectively,are indicated with asterisks (*) in the Macro and PC columns. * denotes signicance at the 10% level, ** denotes signicance atthe 5% level, and *** denotes signicance at the 1% level.cases.3 The full sample results for the combo model are given in Table 3. Comparing thecalculated performance measures across the term structure we can see that the adoption ofthe Combo model results in almost systematic improvements relative to the standalone PCand Macro models. We evaluate this improvement in RMSE and MAE metrics formallythrough the application of a t-test. We nd that supplementing the Macro factors withPC factors in the most speculative periods results in a signicantly more accurate modelthan using Macro factors alone. The tests also show that the Combo model signicantlyoutperforms the PC factor model, however, this outperformance is loss function specic.To address the multiple comparisons problem, p-value adjustments are undertaken inline with Benjamini and Hochberg (1995) (full results available upon request). Afterexplicitly controlling for possible false discoveries 16 of the 25 instances of statisticalsignicant identied in Table 3 are said to be truly signicant, adding an additional layerof statistical rigour in support of our ndings.3 Formally, the Combo model is dened as:△CLτt= θMacrotˆMacroτt+θPCtˆPCτt+ εt,θMacrot1 if WTt < WT%tile0 if WTt ≥ WT%tile,θPCt1 if WTt ≥ WT%tile0 if WTt < WT%tile,whereWTt is the calculated Working's T index value for day t, andWT%tile is the 90 percentile Working'sT cuto value.64 ConclusionAs opposed to directly analysing the relationship between speculative activity and oilfutures pricing dynamics we take a dierent approach; examining the impact speculativeactivity has on the predictive accuracy of two distinct classes of factors in determiningthe returns on oil futures contracts. The similarity in the performance measures observedfor both classes of factors over the full period suggests that principal components capturebroadly similar variance to that characterised by the Macro fundamental factors. Whenwe split the analysis into subsamples by levels of speculation we nd that in the sample'smost speculative periods it is advantageous to adopt predictions based on the PC factorsand in other periods to use Macro factor predictions. We demonstrate this further throughthe empirical evaluation of a simple combined model that utilises both PC and Macrofactors, leading us to hypothesise that the latent factors approach can be interpreted asincorporating a proxy for the pricing impact elevated levels of speculation have on themarket. Future research might focus on other asset classes and/or seek to economicallyexploit the dynamic presented here by assessing the protability of an out-of-sampletrading strategy.ReferencesAlquist, R., O. Gervais, et al. 2013. The Role of Financial Speculation in Driving thePrice of Crude Oil. Energy Journal 34:3554.Andreasson, P., S. Bekiros, D. K. Nguyen, and G. S. Uddin. 2016. Impact of speculationand economic uncertainty on commodity markets. International Review of FinancialAnalysis 43:115127.Bates, J., and C. Granger. 1969. The Combination of Forecasts. Journal of the OperationalResearch Society 20:451468.Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. Journal of the Royal Statistical Society. SeriesB (Methodological) pp. 289300.Büyüksahin, B., and J. H. Harris. 2011. Do speculators drive crude oil futures prices.Energy Journal 32:167202.Büyük³ahin, B., and M. A. Robe. 2014. Speculators, commodities and cross-marketlinkages. Journal of International Money and Finance 42:3870.7Chantziara, T., and G. Skiadopoulos. 2008. Can the dynamics of the term structureof petroleum futures be forecasted? Evidence from major markets. Energy Economics30:962985.Fattouh, B., L. Kilian, and L. Mahadeva. 2013. The Role of Speculation in Oil Markets:What Have We Learned So Far? Energy Journal 34:733.Juvenal, L., and I. Petrella. 2015. Speculation in the oil market. Journal of AppliedEconometrics 30:621649.Kilian, L., and D. P. Murphy. 2014. The role of inventories and speculative trading in theglobal market for crude oil. Journal of Applied Econometrics 29:454478.Morana, C. 2013. Oil price dynamics, macro-nance interactions and the role of nancialspeculation. Journal of Banking & Finance 37:206226.Working, H., et al. 1960. Speculation on hedging markets. Food Research Institute Studies1:185220.8

Gogolin, Fabian

Kearney, Fearghal

Queen's University Belfast Research Portal

Does Speculation Impact What Factors Determine Oil Futures Prices?Gogolin, F., & Kearney, F. (2016). Does Speculation Impact What Factors Determine Oil Futures Prices? DOI:10.1016/j.econlet.2016.05.007Published in:Economics LettersDocument Version:Peer reviewed versionQueen's University Belfast - Research Portal:Link to publication record in Queen's University Belfast Research PortalPublisher rights© 2016 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/, which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.General rightsCopyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or othercopyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associatedwith these rights.Take down policyThe Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made toensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in theResearch Portal that you believe breaches copyright or violates any law, please contact openaccess@qub.ac.uk.Download date:09. Sep. 2018Does Speculation Impact What Factors Determine OilFutures Prices?Fabian Gogolin∗ Fearghal Kearney†May 29, 2016AbstractRecent studies provide contradictory evidence about the impact of speculation oncommodity prices. Rather than directly evaluating this relationship we instead usea novel approach to assess if speculation can inform our choice of factor inclusion inmodelling oil futures.Keywords: Oil futures; speculation; fundamentals; statistical factorsJEL Classiﬁcations: G10; G13; G15Highlights:• Consider observable and unobservable factors separately for oil futures prices• Assess each class of factors in subsamples split by speculative activity• Uncover signiﬁcant outperformance utilising a composite prediction framework1 IntroductionRecent sharp price declines in crude oil markets have increased the focus on whatfactors determine the observed market dynamics. Movements in commodity prices areoften attributed to speculation, with Morana (2013) and Juvenal and Petrella (2015) con-cluding that speculative shocks are a relevant determinant of oil price changes. However,evidence from Büyüksahin and Harris (2011), and Alquist and Gervais (2013) contradictthis, ﬁnding that the correlation between a speculation index and daily price changes isnear zero. We approach the question of speculative impact from a diﬀerent perspective;∗Queen's Management School, Queen's University Belfast, BT9 5EE, UK. E-mail address:F.gogolin@qub.ac.uk.†Corresponding author at: Queen's Management School, Queen's University Belfast, BT9 5EE, UK.Tel.: +44 (0) 28 9097 4795; E-mail address: F.kearney@qub.ac.uk.1asking instead how speculation impacts the modelling accuracy of two distinct classesof factors proposed in oil futures literature. The two approaches we refer to are thosecomprised of observable fundamental macroeconomic, and unobservable latent principalcomponent, factors.Kilian and Murphy (2014) note that anyone buying crude oil not for current con-sumption, but for future use is a speculator from an economic point of view. In practice,we consider market participants who take positions to proﬁt from an expected change inthe price of oil as speculators. Due to the increased ﬁnancialization of commodity fu-tures markets, it has been proposed that speculation is now a major component of prices.However, not all speculation is the same. Some speculators provide liquidity and assistin price discovery, meaning that a certain level of speculation is required for a marketto function correctly, whilst the activities of other speculators are said to destabilise themarket and distort prices (Fattouh et al. 2013).In this article, we refrain from deﬁning a single value as a cutoﬀ point for high orexcessive levels of speculative activity, instead utilising a range of values corresponding toproxies for elevated levels of speculation. Our study contributes by ﬁnding that for ele-vated levels of speculative activity diﬀerences between the model accuracy of fundamentaland latent factor approaches are uncovered; diﬀerences that are not present over the fullsample period. The empirical analysis indicates that latent factors pick up additionalprice dynamics not captured by macroeconomic fundamentals, a main contribution ofour study. Through the proposal of a novel composite prediction framework we demon-strate that utilising speculative positions to inform factor selection leads to statisticallysigniﬁcantly increased accuracy in modelling oil futures price changes.2 Empirical approachWe follow previous literature (Büyüksahin and Harris 2011, Alquist and Gervais 2013,and Büyüksahin and Robe 2014) by adopting the Working (1960) T index as a proxy forspeculative activity. It is deﬁned as follows:T = 1 +SSHL+HSif HS ≥ HLT = 1 +SLHL+HSif HS < HLwhere SS (SL) is the open interest of speculators (non-commercials ﬁrms) holding netshort (long) positions andHS (HL) is the open interest of hedgers (commercial ﬁrms) whohold a net short (long) position. The ratio is predicated on the concept that speculatorsare necessary only insofar as they constitute a counterparty for hedgers. As highlighted2by Büyüksahin and Harris (2011), what might be considered speculation in the marketcould simply be commercials not hedging or commercials taking a stance on future oilprice movements. As there is no one Working's T index value that indicates excessivespeculation we incrementally use values in the 50-90 percentile range as a measure ofincreasing levels of speculative activity.In order to consistently compare the performance of the distinct classes of factorswe specify structurally similar integrated models for both the unobservable principalcomponent factors, and the observable macroeconomic factors. Firstly, motivated byChantziara and Skiadopoulos (2008), we consider the following statistical model for oilfutures returns:4CLτt = β0 + β1PC1t−1 + β2PC2t−1 + β3PC3t−1 + εt,where PC1, PC2, and PC3 denote the ﬁrst, second, and third principal componentsof the WTI futures curve, and 4CL denotes the log return of the continuous WTI crudeoil (CL) contract of maturity τ at time t. We refer to this model henceforth, as PC.Secondly, we consider a similarly constructed linear model, this time comprised of oilfutures macroeconomic factors from Andreasson et al. (2016):4CLτt = β0 + β14SP500t−1 + β24V IXt−1 + β34USDt−1 + β44EcPolt−1 + εt,where 4SP500 denotes the log return of the S&P500 index, 4V IX denotes the logchange in the VIX volatility index, 4USD denotes the log return of the trade weightedUS dollar index, and 4EcPol denotes the log change in the economic policy uncertaintyindex for the United States of America. We refer to this model henceforth, as Macro.1Finally, we produce a composite prediction informed by underlying speculative activity.This approach is motivated by Bates and Granger (1969) who were pioneers in arguingthat given the availability of more than one prediction of the same variable, it is rarely(if ever) optimal to identify the best of the competing predictions and use it in isolation.In line with Chantziara and Skiadopoulos (2008) we use daily WTI crude oil CL1-CL9prices obtained from the CME Group. The time period for our sample is January 2007to March 2016. The macroeconomic factors dataset comprises daily VIX quotes obtainedfrom CBOE, S&P500 index values obtained from Yahoo! Finance, and Trade Weighted1The aim of this article is to compare two distinct classes of factors, not to prescribe a speciﬁc funda-mental factor model for modelling crude oil futures. In comparison with literature outlining macroeco-nomic factors that model the dynamics of crude oil spot markets there is a relative paucity of literatureproposing relevant fundamental determinants of oil futures prices. In further testing we specify an alter-native model by including oil inventory (a factor popular in modelling oil spot prices) as an additionalmacroeconomic factor to those outlined in Anderson et al. (2016). However, regression results show thatthe inclusion of inventory is not signiﬁcant.3Table 1: Performance MeasuresPC RMSE PC MAE Macro RMSE Macro MAE RW RMSE RW MAECL1 0.0253 0.0174 0.0253 0.0175 0.0371 0.0255CL2 0.0234 0.0165 0.0234 0.0166 0.0343 0.0242CL3 0.0224 0.0159 0.0224 0.0159 0.0328 0.0232CL4 0.0217 0.0154 0.0216 0.0154 0.0318 0.0226CL5 0.0210 0.0149 0.0209 0.0149 0.0307 0.0219CL6 0.0204 0.0145 0.0204 0.0145 0.0298 0.0213CL7 0.0200 0.0141 0.0199 0.0142 0.0292 0.0208CL8 0.0196 0.0138 0.0195 0.0139 0.0286 0.0204CL9 0.0191 0.0135 0.0191 0.0135 0.0280 0.0199The predictive accuracy of the PC, Macro, and Random Walk (RW) models for each maturity WTI contract over the January2007 to March 2016 period are given. The results of a t-test of statistically signiﬁcantly better performance measures betweenPC and Macro factors, are indicated with asterisks (*) in the Macro and PC columns. * denotes signiﬁcance at the 10% level, **denotes signiﬁcance at the 5% level, and *** denotes signiﬁcance at the 1% level.US Dollar Index and US Economic Policy Uncertainty Index, both obtained from FRED.The Commitment of Traders Futures Only report obtained from the CFTC, is adopted tocalculate Working's T index values. Root mean squared error (RMSE) and mean absoluteerror (MAE) loss functions are employed to assess the predictive accuracy of each class offactors.2 As in all empirical studies considering multiple hypothesis tests about a singledataset, there is a risk of falsely inferring signiﬁcance, known as data snooping bias. Weexplicitly address this issue through the use of a formal multiple comparisons framework,namely the false discovery rate as proposed by Benjamini and Hochberg (1995), to checkour results for robustness and uncover instances of truly signiﬁcant outperformance.3 Findings and analysisAfter ﬁtting both models to the data we measure the predictive accuracy of eachclass of factors. We can see from the RMSE and MAE measures in Table 1 that theperformance of the PC and Macro factors are almost identical across the term structureof the WTI futures curve over the full sample period. As expected, a formal t-test of bothperformance measures for each of the maturity contracts fails to yield any signiﬁcantoutperformance. Random Walk performance metrics are also provided for benchmarkpurposes, indicating that the accuracy of both models are better than would be expectedby chance alone.We now examine if underlying speculative activity has any impact on the factorsdetermining WTI futures returns. We do this by referring to observations with Working'sT values of greater than or equal to 50, 60, 70 , 80, and 90 percentile full sample indexlevels respectively, as most speculative with all other observations being categorised2The squared error and absolute error for each observation are used to conduct the t-tests.4Table 2: Performance Measures for Speculation SubsamplesPercentile Working's T PC RMSE PC MAE Macro RMSE Macro MAELeast speculative subsample50% <1.1153 0.0151 0.0108 0.0151 0.010960% <1.1243 0.0161 0.0116 0.0162 0.011770% <1.1346 0.0174 0.0124 0.0174 0.012480% <1.1472 0.0191 0.0135 0.0189 0.013590% <1.1639 0.0203 0.0143 0.0202 0.0143Most speculative subsample50% ≥1.1153 0.0263 0.0195 0.0262 0.019560% ≥1.1243 0.0275 0.0204 0.0274 0.020470% ≥1.1346 0.0286 0.0216 0.0286 0.021680% ≥1.1472 0.0290 0.0217 0.0292 0.022090% ≥1.1639 0.0296 0.0225 0.0302 0.0230RMSE and MAE denote the RMSE and MAE performance measures averaged over contracts of CL1-CL9 maturity. They aregiven for the PC and Macro factors using subsample periods based on increasing percentile Working's T index (1960) values asgiven in the Working's T column. Observations with Working's T values greater than or equal to 50-90 percentile levelsrespectively are categorised as most speculative with other periods being referred to as least speculative.as  least speculative. For example, if we use the 90% percentile calculated Working's Tindex as the cutoﬀ point, we refer to observations with Working's T values greater than orequal to 1.1639 as most speculative (228 days) and all other periods as  least speculative(2057 days). Table 2 splits the analysis into these subsample periods based on speculativeactivity. Firstly, analysing the 50-90 percentile least speculative Working's T subsamplewe again observe very little diﬀerence in terms of predictive accuracy between the adoptionof Macro and PC factors. The strongest indication of a divergence in performance is forthe least speculative 80% of the sample where we observe an RMSE value of 0.0191versus 0.0189 for Macro and PC respectively, providing an initial suggestion that Macrofactors outperform in less speculative periods. The results for the subsample periodswith elevated levels of speculative activity are more clear-cut however. Using the mostspeculative 10-20% of the sample, we see that PC factors outperform Macro factors withMAE metrics of 0.0217 vs. 0.0220, and 0.0225 vs. 0.0230, for 80 and 90 percentileWorking's T values, respectively. This demonstrates that in the sample's most speculativeperiods it is advantageous to adopt PC factors whereas other more benign periods aremore accurately modelled using Macro factors.To both highlight and formally test this dynamic we construct a simple combinedmodel (Combo model, henceforth) utilising the prediction from the PC factor model forthe subsample of observations with calculated Working's T values of greater than or equalto the 90 percentile ﬁgure and the prediction from the Macro factor model in all other5Table 3: Combo Model Performance MeasuresCombo RMSE Combo MAE Macro RMSE Macro MAE PC RMSE PC MAECL1 0.0252 0.0175 0.0253** 0.0175** 0.0253 0.0174CL2 0.0233 0.0165 0.0234* 0.0166** 0.0234 0.0165CL3 0.0223 0.0159 0.0224* 0.0159** 0.0224 0.0159CL4 0.0216 0.0154 0.0216* 0.0154** 0.0217* 0.0154CL5 0.0208 0.0149 0.0209* 0.0149** 0.0210* 0.0149CL6 0.0203 0.0145 0.0204* 0.0145** 0.0204* 0.0145CL7 0.0198 0.0141 0.0199* 0.0142** 0.0200* 0.0141CL8 0.0194 0.0138 0.0195** 0.0139*** 0.0196* 0.0138CL9 0.0190 0.0135 0.0191* 0.0135** 0.0191 0.0135Overall 0.0213 0.0151 0.0214*** 0.0152*** 0.0214*** 0.0151The predictive accuracy of the PC, Macro, and Combo models over the January 2007 to March 2016 period for each maturityWTI contract are given. Overall denotes the performance metric value averaged over all of the CL1-CL9 maturities. Theresults of a t-test of statistically signiﬁcantly outperformance for the Combo model over the Macro and PC models respectively,are indicated with asterisks (*) in the Macro and PC columns. * denotes signiﬁcance at the 10% level, ** denotes signiﬁcance atthe 5% level, and *** denotes signiﬁcance at the 1% level.cases.3 The full sample results for the combo model are given in Table 3. Comparing thecalculated performance measures across the term structure we can see that the adoption ofthe Combo model results in almost systematic improvements relative to the standalone PCand Macro models. We evaluate this improvement in RMSE and MAE metrics formallythrough the application of a t-test. We ﬁnd that supplementing the Macro factors withPC factors in the most speculative periods results in a signiﬁcantly more accurate modelthan using Macro factors alone. The tests also show that the Combo model signiﬁcantlyoutperforms the PC factor model, however, this outperformance is loss function speciﬁc.To address the multiple comparisons problem, p-value adjustments are undertaken inline with Benjamini and Hochberg (1995) (full results available upon request). Afterexplicitly controlling for possible false discoveries 16 of the 25 instances of statisticalsigniﬁcant identiﬁed in Table 3 are said to be truly signiﬁcant, adding an additional layerof statistical rigour in support of our ﬁndings.3 Formally, the Combo model is deﬁned as:4CLτt = θMacrot ˆMacroτt+θPCt ˆPCτt + εt,θMacrot1 if WTt < WT%tile0 if WTt ≥WT%tile ,θPCt1 if WTt ≥WT%tile0 if WTt < WT%tile ,whereWTt is the calculated Working's T index value for day t, andWT%tile is the 90 percentile Working'sT cutoﬀ value.64 ConclusionAs opposed to directly analysing the relationship between speculative activity and oilfutures pricing dynamics we take a diﬀerent approach; examining the impact speculativeactivity has on the predictive accuracy of two distinct classes of factors in determiningthe returns on oil futures contracts. The similarity in the performance measures observedfor both classes of factors over the full period suggests that principal components capturebroadly similar variance to that characterised by the Macro fundamental factors. Whenwe split the analysis into subsamples by levels of speculation we ﬁnd that in the sample'smost speculative periods it is advantageous to adopt predictions based on the PC factorsand in other periods to use Macro factor predictions. We demonstrate this further throughthe empirical evaluation of a simple combined model that utilises both PC and Macrofactors, leading us to hypothesise that the latent factors approach can be interpreted asincorporating a proxy for the pricing impact elevated levels of speculation have on themarket. Future research might focus on other asset classes and/or seek to economicallyexploit the dynamic presented here by assessing the proﬁtability of an out-of-sampletrading strategy.ReferencesAlquist, R., O. Gervais, et al. 2013. The Role of Financial Speculation in Driving thePrice of Crude Oil. Energy Journal 34:3554.Andreasson, P., S. Bekiros, D. K. Nguyen, and G. S. Uddin. 2016. Impact of speculationand economic uncertainty on commodity markets. International Review of FinancialAnalysis 43:115127.Bates, J., and C. Granger. 1969. The Combination of Forecasts. Journal of the OperationalResearch Society 20:451468.Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. Journal of the Royal Statistical Society. SeriesB (Methodological) pp. 289300.Büyüksahin, B., and J. H. Harris. 2011. Do speculators drive crude oil futures prices.Energy Journal 32:167202.Büyük³ahin, B., and M. A. Robe. 2014. Speculators, commodities and cross-marketlinkages. Journal of International Money and Finance 42:3870.7Chantziara, T., and G. Skiadopoulos. 2008. Can the dynamics of the term structureof petroleum futures be forecasted? Evidence from major markets. Energy Economics30:962985.Fattouh, B., L. Kilian, and L. Mahadeva. 2013. The Role of Speculation in Oil Markets:What Have We Learned So Far? Energy Journal 34:733.Juvenal, L., and I. Petrella. 2015. Speculation in the oil market. Journal of AppliedEconometrics 30:621649.Kilian, L., and D. P. Murphy. 2014. The role of inventories and speculative trading in theglobal market for crude oil. Journal of Applied Econometrics 29:454478.Morana, C. 2013. Oil price dynamics, macro-ﬁnance interactions and the role of ﬁnancialspeculation. Journal of Banking & Finance 37:206226.Working, H., et al. 1960. Speculation on hedging markets. Food Research Institute Studies1:185220.8

Does Speculation Impact What Factors Determine Oil Futures Prices?

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Does speculation impact what factors determine oil futures prices?

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