Forecasting Commodity Prices: Futures Versus Judgment - Webs
Measuring the effect of oil prices on wheat futures prices
Transcript of Measuring the effect of oil prices on wheat futures prices
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Accepted Manuscript
Title: Measuring the effect of oil prices on wheat futures prices
Author: Phillip A. Cartwright Natalija Riabko
PII: S0275-5319(14)00022-1DOI: http://dx.doi.org/doi:10.1016/j.ribaf.2014.04.002Reference: RIBAF 306
To appear in: Research in International Business and Finance
Received date: 28-1-2014Revised date: 10-4-2014Accepted date: 24-4-2014
Please cite this article as: Cartwright, P.A., Riabko, N.,Measuring the effect of oilprices on wheat futures prices, Research in International Business and Finance (2014),http://dx.doi.org/10.1016/j.ribaf.2014.04.002
This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.
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1. Introduction
The primary purpose of this paper is too lend insight into the empirical validity of reverse regressions
hypothesizing that spot prices today help to predict forward rates in the future and the importance
of temporal aggregation in estimating such relationships. The study is interesting for reasons of
economics and political science as well as for econometrics and statistics. It is generally understood
that fluctuations in oil prices can endanger economic and political stability. Following Posner (2013),
these effects can be exacerbated to the extent that they are related to other commodities prices,
particularly those of food commodities. Hypothesizing transmission of effects from energy markets
to food commodity prices seems perfectly reasonable given the importance of energy as an input to
the growing, harvesting and transportation costs. Such cross‐market fluctuations are likely to be
particularly destabilizing for net importers of agricultural commodities including Middle Eastern
countries which import large quantities of wheat and corn. It is well known that the Middle East is of
critical importance for global oil production. It also imports a third of globally traded cereals. Middle
Eastern states consider food imports a strategic liability (Woertz, 2013).
As this research is focused on analyzing empirically the possible effects of oil prices on wheat futures
contract prices at a future point in time and to consider the effect of temporal aggregation on any
such relationship, the paper does not seek to directly address the issues concerning efficiency of
markets, although the paper might be viewed to take market efficiency as a working null hypothesis,
nor does it take on the task of determining price response to “fundamentals” such as weather as in
the work by Pindyck (2001). Further, while commodity prices such as wheat and oil exhibited
considerable volatility over the period of study (for example, the price of oil rising from 70 USD in
2006 to over 140 USD in 2008, declining to just under 40 USD in 2009 and rising to near 110 USD in
2011), this work does not address the possible rolls of basic supply and demand shifts versus the roll
of speculation (Knittel and Pindyck, 2013).
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Earlier work has focused on the relationship between the energy sector and agricultural commodities
has been published by Baffles (2007), Muhammad and Kebede (2009) and Saghaian (2010). Baffels
(2007) reports that among non‐energy commodities, oil prices have the highest pass‐through to food
commodities and fertilizers. Muhammed and Kebede (2009) find that the emerging ethanol market
has integrated oil and corn prices. Saghaian (2010) using time‐series and directed graph theory
approaches, finds a correlation between oil and commodity prices, but the evidence of a (Granger)
causal link is mixed. In a different, but related context, Roberts and Schlenker (2013) estimate
elasticities for caloric energy from the most prominent food commodities and consider price and
quantity implication arising from the expansion of ethanol demand.
Temporal aggregation has been a frequent topic of past and recent research in economics and
finance. Problems of temporal aggregation arise frequently in economics and econometric analysis
as a consequence of using macro or aggregated data to estimate underlying micro, or disaggregate
relationships. Results from research indicate that effects of temporal aggregation can adversely
impact statistical estimation, inference and dynamic lag structures. Evidence relevant to this analysis
(Engle, R.F. and Liu, T.C., 1972; Rowe, 1976; Cartwright and Lee, 1987; Marcellino, 1999; Silvestrini
and Veredas, 2005) indicates that time series aggregation will most certainly influence standard
errors on parameter estimates as standard errors are likely to increase with aggregation. Therefore,
t‐ratios are likely to change as well. While goodness‐of‐fit measures might increase with aggregation,
forecast accuracy with macro‐level aggregations might deteriorate owing to information loss due to
the averaging of observations associated with an underlying micro‐level structure. Generally, the
results show adverse effects of temporal aggregation on statistical estimation, inference and on
dynamic lag structures.
Following Silvestrini and Veredas (2005), aggregation of time series raises many issues relevant for
the practitioner. For example, if a time series can be characterized by a particular specification at one
level of aggregation, what is the correct specification at an alternative aggregation? If data are
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available at both micro and macro levels of aggregation, which series should be modeled? If
information loss owing to aggregation is of concern, how best measure the information loss?
Following this Introduction, Section 2 provides a description of the data. In order to develop
understanding of the consequences of aggregation when modeling commodities futures contract
price data, models are estimated and evaluated at increasingly higher levels of temporal aggregation;
daily, weekly and monthly. Section 3 reports results from feasible generalized least squares (FGLS)
modeling. Models are estimated excluding and including the spot oil price. In addition, the model
including the oil price is estimated using a random coefficient framework (Swamy, 1970). Following
the early work of Cartwright and Lee (1987), analysis is made based upon aggregation and estimated
basic errors of the coefficients.
Section 4 addresses issues of time series dynamics and causality. In order to gain further
understanding of model estimation and performance, a time series approach is applied to the data
for the U.S. and France. The results for GARCH applications at the daily, weekly and monthly
aggregation levels are reported including diagnostics and forecast measures. The summary and
conclusions from the empirical work appear in the final section.
2. Data
Data are collected on futures wheat contract price and spot prices for the United States and France.
These markets are considered to be the largest for which reliable market data are available on a daily
weekly and monthly basis. .
Data are collected on a daily basis beginning on 1 January 2006 through 31 December 2011. This
period was chosen so as to begin the analysis prior to the onset of the financial crisis beginning in
2008 and ending with the final period of availability. The empirical analysis is based on the daily,
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weekly and monthly FOB prices for U.S. Soft Red Winter (Gulf ports), and French Soft Wheat (Rouen)
as well as on the corresponding products futures prices. All the price series are quoted in nominal
USD per ton. The US daily spot prices and U.S. daily futures rates were taken from the International
Grain Council (IGC) data base which corresponds to Eurostat data. The French daily spot prices and
French daily futures prices were taken from the International Grain Council (IGC) data base which
also corresponds to Eurostat data.
The ICE Brent Crude daily spot rates and daily futures rates were taken from the International Grain
Council (IGC) data base which corresponds to International Exchange (ICE) data. The ICE Brent Crude
Futures contract is a deliverable contract based on EFP delivery with an option to cash settlement.
Data have been aggregated from daily data (n=1538) to weekly (n=308) and monthly data series
(n=68) in order to guarantee correspondence between the series over time. Descriptive statistics for
the daily series are shown in Table 1.
INSERT TABLE 1 ABOUT HERE
3. Basic and Random Coefficient Models
3.1 Basic and Extended Models
Since the 1970s, empirical research on financial markets behavior and performance has emphasized
the efficient market hypothesis, which states that given a particular financial contract with developed
forward and spot markets, the forward price reflects all information possessed by persons active in
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that market. Therefore, in an open market, the forward price should be an unbiased predictor of the
future spot price (Dornbusch, 1976). Empirical tests of this relationship take the form of a regression
1) )()()( kttfkts k +++=+ εβα
where
s (t) = the spot price on contracts of wheat at period t
f (t) = the average of the 30‐day forward contract price recorded at period t‐1
ε (t) = error disturbance assumed distributed normal and independently with zero mean
variance σ2
=k number of periods into the future from the time period t.
The efficient market hypothesis implies that the estimate of the constant term is not significantly different from zero and the estimate of β is not significant different from 1.0.
Note that f k(t) is 30‐day forward price. Weekly and monthly data series were constructed from daily
data averaging not only time intervals taking into account multiple contracts week‐ends and trading
holidays.
In this paper, interest is in the reverse regression, i.e.
2 ) )()()( kttsktf k +++=+ εβα or more generally, interest is in the model
2’) )(')(')('')( ktttsktf k ++++=+ εδβα
where
f (t) = the average of the 30‐day forward contract price recorded at period t
s (t) = the spot price on contracts of wheat at period t
o (t) = the spot price for oil (Brent) at period t
ε (t) = error disturbance assumed distributed normal and independently with zero mean variance σ2
=k number of periods into the future from the time period t.
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A precedent for such reverse regressions has been established in exchange rate models by Campbell
and Shiller (1987), Engel and West (2005), and Chen, Rogoff and Rossi (2008). Fundamentally, 2)
points to the notion that today’s market state (and past realizations in the dynamic case considered
below) effects the forward‐looking variable. More, technically, the idea is that the price today
reflects expectations of future changes in market conditions, so it should be a useful predictor.
Moreover, in the context of this research, if contemporaneous or strict causality from the spot to the
futures market is owing to supply chain transmission of effects from energy markets to food
commodity prices, the monthly unit of aggregation can be entertained as appropriate. Following
Chen, Rogoff and Rossi (2008), it is recognized that endogeneity problems in such models make
sorting out of the dynamic causality difficult, if not impossible, and parameter instability exacerbates
the statistical problems. In this paper, alternative methods and specifications are applied in an effort
to gain insight into the issues of aggregation, acknowledging that the issues of causal interpretation
are not resolved.
3.1.1 Daily Data
United States
In order to indicate the behavior of the series under analysis, generally and specifically, the daily
futures contract price data for the United States are shown in Figure 1. The spot price data is plotted
in Figure 2. The Brent oil spot price data are plotted in Figure 3. For the U.S. futures market, the
results of the basic model show a Durbin‐Watson statistic of .071 indicating the presence of first‐
order autocorrelation justifying the application of feasible generalized least squares (FGLS) using the
STATA prais command. Diagnostic statistics are indicated throughout the course of the paper. Details
concerning diagnostics and extensive data plots are available from the authors on request.
Figure 1. Price, U.S. Daily Wheat Futures (USD/MT), 2006‐2011
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INSERT FIGURE 1 ABOUT HERE
Figure 2. Price, U.S. Daily Wheat Spot (USD/MT), 2006‐2011
INSERT FIGURE 2 ABOUT HERE
The FGLS results show the spot price for wheat at (t) is significant. The specification is extended to
include the spot oil price as an exogenous variable. The Brent oil spot price data are plotted in Figure
3. Estimation of Equation 2’ yields promising results, but the presence of autocorrelation as indicated
by the Durbin‐Watson statistic of .070 suggests the application of the FGLS procedure. In this
specification, the estimated value for the coefficient on the wheat spot is significant, while the
coefficient on the spot oil price is clearly insignificant.
INSERT FIGURE 3 ABOUT HERE
Figure 3. Price, Daily Brent Oil Spot (USD/BBL), 2006‐2011
France
Following precisely the same procedures as those adopted for the U.S. data, the models are
estimated using data for the French market. OLS results for the basic model yield a Durbin‐Watson
statistic of .047, and the FGLS procedure is applied as above. The results show that the coefficient
estimate for the wheat spot price is insignificant. Again, as above, the model is extended to include
the oil price variable.
For specification 2’) the autocorrelation‐corrected model shows the coefficient estimates on spot
wheat and oil are significant. The transformed Durbin‐Watson statistic indicates that the first‐order
serial correlation has been mitigated.
3.1.2 Weekly Data
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United States
The U.S. data are aggregated to the weekly level on a calendar day basis. That is, the weekly data are
aggregated and averaged so as to correspond to the trading days in the daily data set. The
aggregated weekly futures series is plotted in Figure 4. The basic model of futures contract prices at
t+k as a function of spot wheat prices at t is estimated and corrected for first‐order autocorrelation.
The correction is based on the Durbin‐Watson statistic of .201 from the estimation of the initial
specification indicating the presence of first‐order autocorrelation. The FGLS results yield an
insignificant estimate on the spot wheat price.
INSERT FIGURE 4 ABOUT HERE
Figure 4. Price, U.S. Weekly Wheat Futures (USD/MT), 2006‐2011
The basic model is extended to include the aggregated spot oil price, which is plotted in Figure 5. The
estimation yields a Durbin‐Watson statistic of .236 indicating the presence of first‐order
autocorrelation. The FGLS procedure is applied. The transformed regression yields an insignificant
result for the estimated spot price for wheat, but the coefficient estimate on the price of oil
significant at the 99 percent confidence level.
INSERT FIGURE 5 ABOUT HERE
Figure 5. Price, Weekly Brent Oil Spot (USD/BBL), 2006‐2011
France
The basic model of futures contract prices for France as a function of spot prices is estimated and the
Durbin‐Watson statistic of .312 for the untransformed model indicates the presence of first‐order
autocorrelation. The FGLS procedure is applied. In sharp contrast to the daily results, and the results
for the U.S. data, the coefficient estimate for spot wheat is significant at the 99 percent confidence
level.
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The model is extended to include the oil price variable. While the initial results from the model
appear promising; the presence of autocorrelation as indicated by the Durbin‐Watson statistic of
.314 suggests application of the FGLS procedure. In contrast to the case using daily data and the U.S.
weekly data, both of the coefficient estimates for the spot price variable and the oil variable are
significant at the 99 percent confidence level. The standard errors of the estimates have also
increased markedly as predicted by theory and shown in Table 1.
3.1.3 Monthly Data
United States
The U.S. monthly futures price is shown in Figure 6. The spot oil price data are plotted in Figure 7.
Estimation of the initial specification indicates first‐order autocorrelation with the Durbin‐Watson
statistic of .841.
INSERT FIGURE 6 ABOUT HERE
Figure 6. Price, U.S. Monthly Wheat Futures (USD/MT), 2006‐2011
INSERT FIGURE 7 ABOUT HERE
Figure 7. Price, Monthly Brent Oil Spot (USD/BBL), 2006‐2011
The estimated FGLS model coefficient on spot wheat is positive and significant at the 90 percent
confidence level. The coefficient estimate was also significant at the daily level, but insignificant at
the weekly level of aggregation. The model including the oil price variable shows coefficient
estimates significant at the 99 percent confidence level. The standard errors for the estimated
coefficient for the spot wheat and oil prices are systematically larger with increasing levels of
aggregation as is goodness‐of‐fit measured by the adjusted R2 confirming theory‐based expectations.
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France
For France, based upon the previous results, the presence of first‐order autocorrelation is indicated
by the Durbin‐Watson Statistic of .841, so the model is re‐estimated using the FGLS procedure. The
coefficient on the spot wheat price is significant at the 99 percent confidence level and the
magnitude of the coefficient estimate is significantly larger in magnitude relative to the daily and
weekly models. Temporal aggregation of the data has significant implications for model results. It
appears that the level of smoothing achieved by aggregating the data for France achieves
significantly better model performance.
The model including the spot oil price performs extremely well overall. The parameter coefficient
estimates for the spot wheat and oil are significant at the 99 and 90 percent confidence levels,
respectively. Again, consistent with previous, the standard errors of the estimated coefficient for the
spot wheat and oil prices are systematically larger with increasing levels of aggregation, confirming
theory‐based expectations. Further, it is notable that the adjusted R2 for the transformed FGLS
model of .917 is also markedly higher than models estimated at the daily and weekly levels.
3.2 Random Coefficient Model
3.2.1 Daily Data
In this section, the data for the U.S. and France are pooled in order to estimate the random
coefficient model as original proposed by Swamy (1970). The algorithm xtrc in STATA is used for this
procedure. It is hypothesized that there exists parameter heterogeneity, which is treated as
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stochastic variation. For purposes of this research, the focus is on testing the nonconstancy of
parameters over markets. It might also be on interest to test the model for constancy over time as
well as groups for reasons which are evident based on the time series analysis below. The model as
proposed by Hsiao might well be applied in future research. A detailed discussion of the model can
be found in Judge, et al. (1985).
The daily data are as above indicating the use of n=3016 (2 x 1508) covering the periods 2006 – 2011.
Based on the Wald test, the coefficients are jointly significant and the test of parameter constancy as
proposed by Swamy (1970) confirms randomness in the parameter estimates. The coefficient
estimate for the spot oil price is not significant, however. Practically, this result rejects the
homogeneity of the combined or pooled market. That is, under the assumption of one efficient
market, the expectation is that the parameters would be stable indicating market homogeneity.
Given a priori knowledge of the autocorrelation based upon estimation of the single country models,
a time trend variable was introduced into the specification. This had marginal effect on the estimates
of the coefficients for spot wheat and oil, but the estimate on the time trend, while small (.010) is
significant at the 99 percent confidence level.
3.2.2 Weekly Data
The weekly aggregated data are as previously indicated and n=608 (2 x304) covering the periods
2006 – 2011. Again, the estimate for the oil spot price is significant. As is the case for the daily data,
for the weekly model, based on the Wald test, the coefficients are jointly significant and the test of
parameter constancy as proposed by Swamy (1970) confirms nonconstancy of the parameter
estimates. While the spot wheat variable is significant 99 percent confidence level the coefficient
estimate for the spot price is not significant. As for the weekly data, a linear time trend variable is
included. Addition of the trend variable does not substantially alter the results recorded without it;
however, the coefficient estimate is significant at the 1 percent significance level.
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3.2.3 Daily Data
The monthly aggregated data are as previously indicated and n=134 (2 x 67). The coefficient
estimates are significant at the 90 percent confidence level or better and the results confirm
nonconstancy of the parameters.
The results to this point are summarized in Table 2. To this point, the results suggest a relationship
between the wheat futures price at period t+k and the spot wheat price at period t consistent with
the reverse regression specification. However, this relationship varies considerably with temporal
aggregation. The spot oil price is generally significant across levels of aggregation. These results
certainly seem to confirm the results of Saghaian (2010) that the markets are correlated, but this
does not constitute a claim of causality. Moreover, the temporal aggregation interval is important in
detecting any such relationship. Generally, temporal aggregation results in increased standard errors
on the estimated coefficients and impacts the goodness‐of‐fit. It is noteworthy that there is
considerable statistical heterogeneity between the markets, which suggests that even where a
correlation between futures and spot wheat prices is detected, the correlation is not necessarily
stable over the U.S. and French markets.
INSERT TABLE 2 HERE
4. Dynamic Models
It is well‐known that intertemporal effects in financial models are prevalent. Intertemporal effects
have been recognized at least since the work of Merton (1976) and Black (1976). As these papers
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demonstrate the inadequacy of static models, this research extends the results above to estimate a
dynamic formulation of 2’) shown in Equation 3.
3) )('')('')('')1('')( ktttsktfktf kk ++++−++=+ εδβφα Where f (t+k‐1) indicates the lagged dependent variable at time t+k‐1. The coefficient 0≥φ <1 and
the error term is assumed to have zero expectation and constant variance.
The model is well‐known in economics and finance as a lagged dependent variable (LDV) model, or
more specifically, Equation 3) is a restricted form of the autoregressive distributed lag (ADL) in which
the specification above implies an infinite geometric lag on the variables s(t) and o(t) based upon
application of a Koyck) transformation. A more detailed discussion of the infinite geometric lag is
provided by Judge, Griffiths, Hill, Lütkepohl and Lee (1985).
Based on the results above, there is reason a priori to expect autocorrelation in the residuals giving
rise to issues potentially more complex than those considered in conjunction with the basic model.
This is troublesome as the estimates are likely to be biased and inconsistent. For this reason, an
alternative approach to estimation, GARCH, is applied. Following the work of Engle (2002) and
Bollerslev, Engle, and Nelson (1994), the MGARCH procedure is applied to estimate the parameters
the multivariate generalized autoregressive heteroscedastic model. In this procedure the conditional
variances are modeled as univariate generalized autoregressive conditionally heteroscedastic models
and the covariances are modeled as nonlinear functions of the conditional variances (Engle, 2002).
Preliminary research using the U.S. and French data investigated application of both the dynamic
conditional correlation (dcc) multivariate as well as the diagonal vech multivariate GARCH models
(Lütkepohl, 2005). Differences in the outcomes were insignificant. The STATA mgarch dcc provides
sufficient generality or flexibility for purposes of this paper. Further, while only the final selected
specifications are reported, for the GARCH (p,q) models alternative values for p and q were tested.
The popularity of the GARCH(1,1) model is indicated by a significant body of literature, e.g. Nelson
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(1990). The GARCH (1,1) model is The GARCH(1,1) model has often been found sufficient to capture
the main features of the volatility process.
It is notable that for this research, estimation of complex structures for p>1 and q>1 frequently
resulted in highly unstable or indeterminate solutions owing to the flat surface of the relevant log
likelihood function. While the models are estimated over alternative levels of aggregation, Drost and
Nijman (1993) have shown that classical GARCH assumptions are not robust to the specification of
the sampling interval. Issues concerning time series modelling of commodity prices, and oil prices in
particular can be found in the papers by Chen and Lin (2014) and Bopp and Lady (1991).
4.1 Daily Data
United States
Applying general procedure for time series methods, the autocorrelation (ACF) and partial
autocorrelation functions (PACF) for the U.S. futures data are estimated (Granger and Newbold,
1986). The ACF shows significant values at long lags, while PACF exhibits a significant value at lag 1.
The clear indication is that the futures data are non‐stationary and the data first‐differenced. The
ACF for the first‐differenced data has a significant value at lag 1 and the PACF decays rapidly.
Based on the Phillips‐Perron test for a unit root, the model to be estimated is based on the data
series as in the previous sections, but first differenced. The relationship of interest is the futures price
f k (t+k) as a function of f k (t+k‐1), s (t) and o (t) for each market individually. In order to understand
the empirical relationships between the differenced variables of interest, pair wise Granger (1969)
causality tests. For the U.S., testing variables up to lag 3 gives indications of causal relationships
between the differenced U.S. futures price on the lagged values of the variable itself, as well as weak
evidence of causality from the futures variable, the differenced U.S. spot price, and the differenced
oil price variable. There is also considerable evidence of endogeneity amongst the variables of
interest. That is, evidence that the spot wheat and oil prices are at lease contemporaneously related
even if strict causality is not indicated. In particular, there is evidence of strong causality from both
the futures price and the spot price to the differenced oil price.
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Using the STATA mgarch dcc algorithm, a GARCH (1, 1) model is estimated. The multivariate dcc‐
GARCH model of Engle (2002) allows the conditional correlations to be time varying. Overall the
model performs reasonably well. The differenced oil price variable is significant. The ARCH and
GARCH terms are significant at any level of confidence. A logarithmic transformation was applied,
but the results indicating persistent heteroscedasticity were not significant altered. This is an
observation to be held for comparison with the models for weekly and monthly data.
To test the ex‐post forecasting accuracy of the U.S. model, the sample is divided into two periods,
estimating the model over the first 1129 observations and holding out the remaining 379
observations for forecasting. It is well‐known that there are many issues and solutions to the
selection of hold‐out samples and metrics designed for testing predictive accuracy of time series
models. For purposes of this research, it seems reasonable that 75% of the observations are used for
estimation and the remaining 25% for out‐of‐sample forecasting. A recent discussion of related
issues and solutions can be found in Fildes and Petropoulos (2013). The mean squared forecast error
for the estimation period is 27.77. The mean squared forecast error for the hold‐out sample period is
30.08, yielding an in‐sample to hold‐out ratio of .902.
France
The procedures applied to the U.S. daily data are followed precisely using the data for France. As
above based on the ACF and PACF for the data series on the prices for futures contracts, the data are
first differenced. The results for the Phillips‐Perron test for a unit root suggest estimation with first
differences of the series for France, although differences of logarithms might also be considered.
Based on the Granger causality tests, it appears that the differenced futures series is caused by the
lagged values of itself the differenced spot price series, but not the differenced oil price series.
There is considerable causality as between the differenced spot price, the futures price variables
and the difference oil price. Despite some ambiguity in the choice of transformation, the GARCH (1,
1) specification is estimated. As reported in Table 2, excepting for the ARCH and GARCH terms, the
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model has very little explanatory power at least as a consequence of inclusion of the lagged futures
price spot price and oil variables.
As in the previous subsection, in order to test the forecasting accuracy of the model, the sample is
divided into two periods; the first 1127 observations and holding out the remaining 381 observations
for ex‐post forecasting. Reestimation of the model over the indicated sample period does yield a
coefficient estimate on spot oil significant at the 95 percent confidence level. The mean squared
forecast error for the estimation period is 6.25. The mean squared forecast error for the hold‐out
sample period is 15.03, yielding an in‐sample to out‐of‐sample mean squared error ratio of ratio of
.416. The result is not unusual given the generally poor performance of the model.
4.2 Weekly Data
United States
The weekly aggregated time series data for the U.S. have an ACF which decays slowly and becomes
insignificant only at long lags. The PACF shows a significant values at lag 1. The series clearly
indicates significant heteroscedasticity. The Peron‐Philips test for a unit root indicates estimation
with the first‐differenced series.
The Granger causality tests are run for the differenced futures price, the futures price lagged 1
period, the spot price and the oil price variable. The results indicate that the differenced futures price
is Granger‐caused by its own lagged value as well as the differenced spot wheat price and the
differenced spot oil price. Further, there is evidence that the differenced futures price, the
differenced spot wheat price and differenced spot oil price is Granger caused by the other variables
under consideration. Thus, as expected, endogeneity is an issue.
A GARCH (1, 1) model is estimated using the weekly U.S. data. The model seems to perform quite
well relative to the daily model with the exception of the pronounced period of variation between
periods 100 and 150. In order to test the forecasting accuracy of the model, the first 226
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observations are used for estimation and the remaining 78 observations are used to test one‐step
ahead forecasting accuracy. The mean squared error of the forecasts over the estimation period is
95.19. Interestingly, and owing to the volatility toward the end of the estimation period, the mean‐
squared forecast error for the hold‐out period is 78.05 resulting in the in‐sample to out‐of‐sample
error ratio of 1.22.
France
The weekly aggregated time series data for France have an ACF which decays and becomes
insignificant at long lags. The PACF shows two significant values at lags 1 and 2. On the basis of this
result and the Phillips‐Perron test, first differences of the series are used for estimation purposes.
The results from the Granger causality tests are consistent with findings reported for the U.S. weekly
data. The GARCH (1, 1) specification is applied to the weekly French data. That is, while there is
indication of causality from the differenced lagged futures, spot and oil prices, the reverse is also
indicated. In order to test the forecasting accuracy of the weekly model, the first 226 observations
are used for estimation purposes and one‐step ahead forecasts are generated for the remaining 78
observations. The mean squared forecast error for the in‐sample estimation is 29.46. The mean
squared forecast error for the out‐of‐sample horizon is 50.75. The in‐sample to out‐of‐sample mean
squared error ratio is .580.
4.3 Monthly Data
United States
The time series are applied to the monthly U.S. data. Based on the patterns of the ACF and PACF for
the data series on the prices for futures contracts, the data are first differenced. The ACF and PACF
both show significant values at lag 1 and decay rapidly according to a sinusoidal pattern. Consistent
with previous findings, the data series show causality from the differenced and lagged futures, spot
and oil variables, but there is also pronounced evidence of two‐way causal relationships.
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The differenced wheat future series is approximately zero‐mean, and although some heteroscedastic
behavior is evident, the volatility of the series has been smoothed considerably by aggregation. The
GARCH(1,1) is estimated and the coefficient estimates are insignificant excepting for the GARCH
term. Estimation of the monthly model required overriding the search tolerance to achieve
convergence.
Forecasting performance of the model is tested based on using the first 49 data points for estimating
the model and retaining the remaining 18 as a hold‐out sample. The mean‐squared forecast error for
the estimation period is 327.69. For the out‐of‐sample period the mean‐squared forecast error is
238.59. The in‐sample to out‐of‐sample ratio is 1.373. The results are consistent with findings for the
weekly data.
France
Applying the time series procedures to the monthly data for France yields an ACF which exhibits a
sinusoidal pattern with the estimated values decreasing at increasing lags. The PACF exhibits a
significant value at lag 1. Diagnostic checks indicate first‐differencing of the data series. Testing the
series for the presence of a unit root suggests the series is stationary.
Granger causality tests estimated on the pairwise basis show evidence of two‐way causality between
the differenced spot price and the differenced futures price. There is also significant evidence of
causality from the differenced wheat futures and spot price to the differenced oil price. As with the
U.S. model the GARCH(1,1) is estimated although estimation of the monthly model required
overriding the search tolerance to achieve convergence
In order to compare the model with other time series specifications on the basis of forecast error,
the monthly model is estimated using the first 49 data periods and the remaining 18 are used for ex‐
post forecasting. The in‐sample mean squared forecast error is 17.93. The one‐step ahead forecasts
are generated for the 17 observations of the hold‐out sample. The mean squared forecast error for
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the one‐step ahead forecasts over the hold‐out sample is 110.08, yielding an in‐sample to out‐of‐
sample ratio of .163. Thus, the ratio is considerably smaller than the daily or weekly ratios. The poor
out‐of‐sample performance is due to very large prediction errors in periods 58, 59 and 62.
The results from estimation of the dynamic models are shown in Table 3. The sample sizes for the
daily, weekly and monthly modes are 1538, 308 and 68, respectively. The symbol Δ denotes first‐
difference of the series. These results show standard errors of the coefficients increasing with
aggregation levels. In terms of forecasting performance, the results are ambiguous. For the U.S.
models out‐of‐sample squared forecast error is greater that in‐sample squared error, but in sample
squared error exceeds out‐of‐sample for the weekly and monthly models. For France, the data
exhibit different behaviors at different levels of aggregation. This is consistently with the findings of
Drost and Nijman (1993).
INSERT TABLE 3 HERE
5. Summary and Conclusions
This paper analyzes empirically the possible relationship between wheat futures prices and spot oil
prices as well as the effects of temporal aggregation on the specification of alternative models of
wheat futures contract prices, and consequently, on the understanding of the empirical relationship
between the two markets (e.g., wheat and oil). Alternative models have been specified and tested.
Key observations from this research include the fact that the wheat futures prices and the spot oil
prices are correlated as pointed out by Saghaian (2010). The fact that the random coefficient model
leads to the rejection of the null hypothesis of parameter constancy does bring into question the
stability of that correlation, however.
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As to causality is seems clear that temporal aggregation does impact the relationships between the
variables as well as the model coefficient estimates, standard errors and forecasting accuracy. With
respect to coefficient estimates and the associated standard errors, consistent with theory, standard
errors tend to increase with the level of aggregation, so there are coincident implications for
accuracy and inference. Forecast accuracy is also impacted by aggregation, although in the context
of this paper, the results are mixed as concerns relative accuracy as measured by ratios of mean
squared forecast errors using selected periods for model estimation and computing one‐step ahead
forecast for a hold‐out sample. The hypothesis that the monthly unit of aggregation is “optimal”
based upon supply chain dynamics relating energy markets to food commodity prices does not find
unequivocal support in this research suggesting future research.
As concerns market homogeneity, at least for the specification tested in this research, it appears that
conventional ordinary least squares (OLS) models using pooled data will be misspecified owing to
nonconstancy of the parameter estimates indicated by application of the Swamy (1970) random
coefficient model. Finally, consideration of a dynamic specification confirms the importance of
intertemporal effects.
From a practical perspective, it is clear that so‐called reverse regressions can be useful for
understanding and predicting prices of futures contracts and the relationship, contemporaneous and
causal, to other markets. However, the degree of significance of the relationship varies and
awareness of the effects of temporal aggregation is required owing to the deterioration of coefficient
estimates as aggregation increases. Such aggregation effects impact understanding the relationship
between markets. With respect to forecasting, this research points clearly toward the importance of
using historical data to test forecast accuracy for specific series at alternative aggregation levels
before generating out‐of‐sample predictions. As concerns future research, the authors intend to
extend the analysis with respect to methodology by applying the approach taken in this paper to
understanding the relationships between other energy prices and those of other food commodities.
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Acknowledgements The authors wish to thank James L. Smith, Southern Methodist University; C.F. Lee, Rutgers University; Octavio Escobar, ESG Management School; Ted Bos, University of Alabama‐Birmingham; Josse Roussel, Université Paris Dauphine as well as the Editor and an anonymous referee for helpful comments and criticisms.
References
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Granger, C.W.J. (1969). "Investigating Causal Relations by Econometric Models and Cross‐spectral Methods". Econometrica ,37 (3), 424–438. Index Mundi (2013), “Crude Net Balance by Country, 2009”. Available at http://www.indexmundi.com/blog/index.php/2013/05/03/crude‐oil‐exports‐and‐imports‐by‐country/ International Grains Council (2013), Information Services. Available at http://www.igc.int. Accessed November 1, 2013. Judge, G.G., Griffith, W.E., Hill, R.C., Lütkepohl, H. and Lee, T.C. (1985), The Theory and Practice of Econometrics, 2nd edition, New York: John Wiley. Knittel, C.R. and Pindyck, R. (2013), “The simple Economics of Commodity Price Speculation”, MIT Center for Energy and Environmental Policy Research, CEEPR WP 2013‐06. Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis, Berlin: Springer. Marcellino, M. (1999), “Some Consequences of Temporal Aggregation in Empirical Analysis”, Journal of Business and Economic Statistics, 17, 1, pp. 129‐136. Merton, R.C. (1976), “An Intertemporal Capital Asset Pricing Model”, Econometrica, 41, pp. 867‐887. Nelson, D.B. (1990), “ Stationarity and persistence in the GARCH(1,1) model”, Econometric Theory, 6, pp. 318–334. Muhammed, A. and Kebede, E. (2009), “The Emergence of an Agro‐Energy Sector: Is Agriculture Importing Instability from the Oil Sector?”, Choices, 24, 1, pp. 12‐15. Pindyck, R. (2001), “Dynamics of Commodity Spot and Futures Markets: A Primer”, The Energy Journal, 22, 3, pp. 1‐29. Posner, R. (2013), “Commodity Price Fluctuations‐Posner”, The Becker‐Posner Blog. August 4, 2013. Available at http://www.becker‐posner‐blog.com/2013/08/commodity‐price‐fluctuationsposner.html. Accessed July 23, 2013. Roberts, M.J. and Schlenker, W. (2013), “Identifying Supply and Demand Elasticties of Agricultural Commodities: Implications for the US Ethanol Mandate”, The American Economic Review, 103, 6, 2265‐2295. Robledo, C.W. (2002), Dynamic Econometric Modeling of the U.S. Wheat Grain Market, Ph.D. Dissertation, The Department of Agricultural Economics and Agribusiness, Louisiana State University and Agricultural and Mechanical College. Rowe, R. (1976), “The Effects of Temporal Aggregation Over Time on T‐Ratios and R2’s”, International Economic Review, 17, pp. 751‐757.
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TABLES TO ACCOMPANY
Measuring the effect of oil prices on wheat futures prices
Table 1. Descriptive Statistics Series Period Observations Mean Std Dev Min Max Wheat Futures, US Daily 1537 240.46 59.72 132.24 443.34 Wheat Spot, US Daily 1537 223.81 64.54 118.36 469.76 Wheat Futures, FR Daily 1537 169.88 38.82 88.70 263.31 Wheat Spot, FR Daily 1537 173.94 49.11 108.75 292.75 Brent Oil Spot Daily 1537 81.43 22.44 36.61 146.08 Wheat Futures, US Weekly 308 240.49 59.83 133.52 423.34 Wheat Spot, US Weekly 308 223.75 64.35 120.30 443.63 Wheat Futures, FR Weekly 308 169.88 38.82 105.79 252.90 Wheat Spot, FR Weekly 308 173.90 49.07 109.00 290.65 Brent Oil Spot Weekly 308 80.95 22.73 31.36 142.71 Wheat Futures, US Monthly 68 239.27 60.51 135.53 386.53 Wheat Spot, US Monthly 68 223.31 65.51 122.91 400.45 Wheat Futures, FR Monthly 68 171.22 40.05 111.20 255.66 Wheat Spot, FR Monthly 68 174.91 49.98 110.04 275.85 Brent Oil Spot Monthly 68 80.10 22.02 43.11 135.00
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Table 2. Summary Temporal Aggregation Effects, U.S. and France, 2006‐2011a
Daily n=1508 Weekly n=304 Monthly n=68 United States
β δ β δ β δ
Basic Model (FGLS)
.04 b (.02)
‐.02 (.05)
.29c (.09)
Extended Model (FGLS) Root MSE
.04 b (.02) 5.27
.02 (.08)
‐.002 ( .048) 9.36
.64c (.12)
.31b (.09)
17.84
.72c (.29)
France
Basic Model (FGLS)
.001 (.02)
.10c (.05)
.77c (.03)
Extended Model (FGLS) Root MSE
‐.002 (.02) 2.91
.03 (.04)
.12c (.04) 5.35
‐.15c (.07)
.76 c (.03) 6.85
.13c (.07)
Random Coefficient Model (Pooled)
.54c (.05 )
.90 c (.39)
.63c (.04)
.68 (.34)
.70c (.02)
.17 (.17)
a Standard errors are shown below the regression coefficient estimates in parentheses. b Indicates significance at the 10 percent level or better. cIndicates significance at the 5 percent level or better.
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Table 3 . Summary Temporal Aggregation Effects, GARCH (n, p) U.S. and France, 2006‐2011a
Daily Weekly Monthly
U.S. t=1508 t=304 t=67
Coefficient (variable)
Δf(t+k‐1) ‐.05 b ( .03)
‐.15 b (.06)
‐.09 (.23)
Δs(t) .0001 ( .02)
‐.023 b (.04)
.18 (.19)
Δo .03 (.07)
.38 b ( .11)
.20 (.35)
ARCH(1) .08 b (.01)
.16 b (.04)
.24 (.19)
GARCH(1) .92 b (.01)
.84 b (.04)
.65 b (.29)
MSFE (in‐sample) /MSFE(hold‐out)
.90 1.82 1.37 France
Coefficient (variable)
Δf(t+k‐1)
.03 (.03)
.31 b (.06)
‐.03 ( .24)
Δs(t) .01 (.02)
.03 ( .05)
.12 (.20)
Δo .03
(.03) ‐.01 b
( .04) . 07
(.35)
ARCH(1) .22 b
(.02) .32 b
(.07) ‐.01 (.03)
GARCH(1) .83 b (.01)
.72 b (.04)
1.05 b (.42)
MSFE (in‐sample) /MSFE(hold‐out)*** .42 .58 .16
aStandard errors are shown below the regression coefficient estimates in parentheses. b Indicates significance at the 5 percent level or better.
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Highlights
Wheat futures and oil prices are correlated, but not necessarily causally related
Correlation between market prices appears unstable
Temporal aggregation does impact the relationships between the market variables
Temporal aggregation impacts estimates, standard errors and forecasting accuracy
Reverse regressions are useful for understanding prices of futures contracts
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Figure 1. Price, U.S. Wheat Futures, Daily
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100.
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rice,
U.S
. Whe
at S
pot U
SD
/MT
0 500 1000 1500Time/Days
Figure 2. Price, U.S. Wheat Spot, Daily
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4060
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Spo
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0 500 1000 1500Time/Days
Figure 3. Price, Brent Oil Spot, Daily
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Pric
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.S. W
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0 100 200 300Time/Weeks
Figure 4. Price, U.S. Wheat Futures,Weekly
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Figure 5. Price, Brent Oil Spot, Weekly
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Pric
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Figure 6. Price, U.S. Wheat Futures, Monthly
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Figure 7. Price, Brent Oil Spot, Monthly
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US Daily Wheat Futures Price USD/MT US Daily Wheat Spot Price USD/MT
Daily Brent Oil Spot Price USD/BBL
Graphical Abstract_ Measuring the effect of oil prices on wheat
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Measuring the Effect of Oil Prices on Wheat Futures Prices*
Phillip A. Cartwrighta,* , Natalija Riabkob aESG Management School, Paris, France bFranceAgriMer, Market Studies Department
ABSTRACT This research lends insight into the empirical validity of reverse regressions hypothesizing that spot prices today help to predict forward rates in the future. This paper analyzes the possible relationship between wheat futures prices and spot oil prices considering the importance of the effects of temporal aggregation and alternative model specification for the understanding of the empirical relationships between the two markets. Evidence indicates that model specification and time series aggregation over daily, weekly and monthly aggregations influence standard errors on parameter estimates as standard errors are likely to increase with aggregation. Therefore, t‐ratios are likely to change as well. Further, while goodness‐of‐fit measures might increase with aggregation, forecast accuracy with macro‐level aggregations might deteriorate owing to information loss. The results indicate the presence of contemporaneous causality between the variables of interest, however, the presence of strict causality is mixed and the results do vary with units of time aggregation. Key Words: wheat prices, crude oil prices, temporal aggregation, causal structure, forecasting
JEL Classifications: Q1, Q4, G1, D4, C1
*Corresponding author at : ESG Management School, Paris, France. Tel: +33 622699665 Email addresses: [email protected] (P. Cartwright), [email protected] (N. Riabko)