The Feasibility of Cross Hedging Wood Pulp with CME Lumber Futures
Min Su Kim
1, Seon-Woong Kim
1and Byung-Sam Yoon
1†Received August 5, 2019; Received in revised form October 1, 2019; Accepted October 2, 2019
ABSTRACT
The main objective of the study is to determine whether the CME (Chicago Mercantile Exchange) random length lumber futures can be used as a cross hedging vehicle for the wood pulp (LBKP and NBKP). In order for the cross hedging to be feasible, there should be a strong positive relationship between the cash price and the futures price. This study conducted rigorous statistical analyses such as cointegration test and Granger causality test to examine the price relationship between CME lumber futures and wood pulp. The empirical analyses suggest that CME random length lumber futures can not be used as a potential cross hedging vehicle for wood pulp.
Keywords: Cross hedging, wood pulp, LBKP, NBKP, CME random length lumber futures, hedge ratio, hedging effectiveness
Printed in Korea http://dx.doi.org/10.7584/JKTAPPI.2019.10.51.5.38
1 Department of Agricultural Economics, Chungbuk National University, Chungbuk, 28644, Republic of Korea
† Corresponding Author: E-mail: [email protected]
1. Introduction
Wood pulp is the main component in the paper- making process. The pulp and paper industry in Korea depends heavily on imported pulp for the manufacturing of various paper products. In 2017, the volume of pulp consumed was 2,738,179 metric tons, of which 82.8% was imported and 86.3% was chemical pulp.
It is well known that the wood pulp market is highly volatile. In fact, it has become increasingly unstable. Wood pulp costs directly impact paper prices, and ultimately impact the profitability of the pulp and paper industry. An increase in wood
pulp price leads to decreasing profits for paper companies that buy wood pulp as raw material.
Thus, paper companies face the risk of rising pulp prices. On the other hand, a decrease in wood pulp price leads to decreasing profits for pulp companies that sell wood pulp as the final product. Thus, pulp companies face the risk of falling pulp prices. Effec- tive price risk management is critical for the pulp and paper industry.
When it comes to hedging price risk in commodities, the most commonly used derivative instruments are futures contracts. While paper companies that use wood pulp can hedge against increasing wood pulp prices through buying wood pulp futures contracts,
pulp companies that produce wood pulp can hedge against decreasing wood pulp prices by selling wood pulp futures contracts. Unfortunately, there is no viable futures market for wood pulp, conse- quently pulp and paper companies cannot directly hedge their price risks in the wood pulp futures market.[1] However, there is a wood-related futures contract traded in the Chicago Mercantile Exchange (CME), CME random length lumber futures. Thus, pulp and paper companies may have the opportunity to cross hedge wood pulp prices with CME random length lumber futures contract.[2]
This paper aims to determine the feasibility of cross hedging wood pulp with CME random length lumber futures. In order for the CME lumber futures to be used as a potential cross hedging vehicle for wood pulp, there should be close price correlations between wood pulp and CME lumber futures. In other words, they should have similar price move- ments.
2. Materials and Methods
2.1 Data
Cash prices for wood pulp are obtained from the Korean Statistical Information Service (KOSIS).
The cash prices are monthly average prices for LBKP (laubholz bleached kraft pulp) and NBKP (nadelholz bleached kraft pulp) respectively. While LBKP is the hardwood kraft pulp made from trees with leaves, NBKP is the softwood kraft pulp made from trees with needles. The prices are based on cost, insurance and freight (CIF) values for imported wood pulp and quoted in dollars per ton ($/t).
Futures prices are the daily settlement prices of the CME random length lumber futures (RLLF).[3]
The futures prices are obtained from a computer database compiled by Reuters. Since cash prices are monthly average prices, monthly averages of the nearby futures price series are used for futures
prices. When constructing nearby futures price se- ries by continuously connecting the nearby futures prices, we roll over to the next nearest contract on the last trading day (LTD) of CME RLLF.
The sample period extends from January 2003 through June 2019, resulting in 198 observations.
The natural logarithms of the prices multiplied by 100 are used, since both cash and futures returns are conventionally assumed to follow log-normal distributions.
2.2 Methods and procedures
[4]2.2.1 Unit root test
A unit root test is used to test whether a time se- ries variable is non-stationary and possesses a unit root. The presence of a unit root is tested by employing the ADF (augmented Dickey-Fuller) test using Eq. 1. The null hypothesis is that the time series has a unit root and is non-stationary.
yt t
t y i yt i t
i p -1
1 1
[1]
where yt=(St, Ft), St is the cash price for LBKP and NBKP respectively, and Ft is the CME RLLF price. t represents a time trend, and ∆ represents a dif- ferencing operator such that ∆yt=yt-yt-1.
2.2.2 Cointegration test
Given that two time series variables are inte- grated of order one, or I(1), respectively, a cointe- gration test is used to test whether they share a common stochastic trend. To determine the cointegration relationship or long-run equilibrium relationship between wood pulp and CME lumber futures prices, the Johansen’s multivariate cointe- gration procedure is used. The Johansen approach proceeds with the general form of a pth order vector autoregressive (VAR) model.
yt= + 1yt-1++ p t py- + t [2]
where yt is a vector consisting of the cash price (St) for LBKP and NBKP, respectively, and the CME RLLF price (Ft). δ is a vector of constants. εt is a vector of error terms satisfying the multivariate normal white noise process with mean 0 and finite covariance matrix Σ.
The VAR(p) above can be reparameterized into a vector error correction model (VECM) of the fol- lowing form.
yt= +yt-1+ 1 yt-1++ p-1 yt p-(-1) t [3]
Where
i
i p
j i j p
1 1
I , and i
i
i p
j i j p
1 1
I , i
Johansen3) shows that the coefficient matrix Π contains essential information about the cointe- grating or equilibrium relationship between the variables. Specifically, the rank of Π matrix is equal to the number of independent cointegrating vectors. Based on the mathematical properties that the rank of a matrix is equal to the number of its characteristic roots (eigenvalues) that differ from zero, the test for cointegration between the vari- ables can be conducted using the following maxi- mum eigenvalue test (λmax) statistics.
max(r r, 1) Tln(1 r 1) [4]
The maximum eigenvalue statistic (λmax) tests the null hypothesis that the number of cointegrating vectors is r against an alternative hypothesis of r+1 cointegrating vectors. In this study, yt=(St,Ft)' and so the number of variables is 2. The hypothesis of cointegration between St and Ft is equivalent to the hypothesis that r=1. If r=0, then the two vari- ables are not cointegrated.
2.2.3 Vector autoregressive (VAR) model and granger causality
The temporal relationship between the variables
can be studied simultaneously by estimating the vector autoregressive (VAR) model. In order to determine the price discovery and lead-lag rela- tionship between the wood pulp and CME RLLF prices, the following VAR model is estimated.
St s St i Ft j st
si
i m
sj j
m
1 1
- - [5]
Ft f fi St Ft j ft i
m
j fj
m
1 1
-1 - [6]
where ∆ denotes the differencing operator. ∆St is the change in cash price for LBKP and NBKP re- spectively, and ∆Ft is the change in CME RLLF price. εst and εft denote white noise errors with zero mean and constant variance.
In Eqs. 5-6, Granger causality tests are associ- ated with tests on the significance of the coeffi- cients γsj and the βfi conditional on the optimal lag length m.[5] In Eq. 5, if all the coefficients of γsj are equal to zero, implying that past changes of Ft have no explanatory power for the current change of St, then Ft does not Granger cause St. This is formally tested using the null hypothesis H0 : γs1 = γs2 = ⋯ = γfm
= 0 and concluded that Ft Granger causes St if the hypothesis is rejected.
Similarly, in Eq. 6, if all the coefficients of βfi are equal to zero, implying that past changes of St
have no explanatory power for the current change of Ft, then St does not Granger cause Ft. This is formally tested using the null hypothesis H0 : βf1 = βf2
= ⋯ = βfm = 0 and concluded that St Granger causes Ft if the hypothesis is rejected.
2.2.4 Estimation of the cross hedge ratio and hedging effectiveness
To estimate the risk-minimizing cross hedge ratio, a conventional method that estimates the following linear regression model is used.4)
St 0 1 Ft t [7]
3.1 Results of unit root tests
Table 1 presents the results of the ADF test to determine the presence of a unit root in the prices of wood pulp (LBKP and NBKP) and CME RLLF.
While the null hypothesis of a unit root (or non- stationarity) can not be rejected in price levels, the ADF test performed on the first differences indi- cates that the first difference of each series is sta- tionary. The results confirm that both the wood pulp and CME lumber futures price series are dif- ference stationary, or I(1), integrated of order one.
where ∆St is the change in cash price for LBKP and NBKP respectively, ΔFt is the change in CME RLLF price, and εt is random error term.
The ordinary least squares (OLS) estimator of β1
provides an estimate for the risk-minimizing cross hedge ratio. From the estimated regression model, a measure of hedging effectiveness, R2, is also ob- tained. R2 is the proportion of the total variation in the wood pulp price changes statistically related to the CME RLLF price changes. Unless the cash and futures prices are closely related to each other, the cross hedging effectiveness would be very low.
3. Results and Discussion
Fig. 1. illustrates the movements of the wood pulp price (NBKP on the upper line and LBKP on the middle line) and CME RLLF price (on the lower line) over the period of January 2003 through June 2019. Visual inspection of the graph does not sug- gest that wood pulp price and CME lumber futures price tend to move together over time. This is later confirmed using rigorous statistical tests.
Fig. 1. Movements of the wood pulp (LBKP, NBKP) price and CME random length lumber futures (RLLF) price, January 2003 through June 2019.
Table 1. Results of unit root tests
Variable ADF-statistic Critical Value Price Levels
LBKPt -3.67 -4.01
NBKPt -3.11 -4.01
RLLFt -2.75 -4.01
First Differences
ΔLBKPt -6.10** -3.46
ΔNBKPt -9.45** -3.46
ΔRLLFt -11.81** -3.46
1) ** indicates statistical significance at the 1% level.
3.2 Results of Johansen cointegration tests
Table 2 reports the results of Johansen cointe- gration tests for two pairs of price series, in other words, LBKP vs. RLLF, and NBKP vs. RLLF. The maximum eigenvalue (λmax) statistics indicate that the null hypothesis of r=0 (no cointegration) can- not be rejected at the 5% significance level. The results show evidence of zero cointegrating vector between the two price series, suggesting that there exists no cointegration or long-run equilibrium relationship between the wood pulp (LBKP and NBKP) and CME RLLF.
3.3 Results of vector autoregressive (VAR) model and granger causality
Table 3 presents the results of the vector autore- gressive (VAR) model between LBKP and RLLF, and Granger causality test. On the left-hand side of Table 3, the coefficients of lagged terms ΔRLLFt-j
are not statistically significant at all. The result indicates that the lumber futures market does not lead the wood pulp (LBKP) market in the lead-lag relationships. The Granger causality test also fails to reject the null hypothesis that RLLF does not Granger cause LBKP.
On the right-hand side of Table 3, the coeffi- cients of lagged terms ΔLBKPt-i are not statisti- cally significant, and the Granger causality test also fails to reject the null hypothesis that LBKP does not Granger cause RLLF at the 5% signifi- cance level. The results generally show that Granger causality runs in no direction between LBKP and CME RLLF.
Table 4 presents the results of vector autoregres- sive (VAR) model between NBKP and RLLF, and Granger causality test. The results are exactly the same with those of VAR model between LBKP and RLLF, presented in Table 3. The results presented in Tables 3-4 suggest that the wood pulp and CME random length lumber futures are independent and
Table 2. Results of Johansen cointegration tests
LBKP vs. RLLF NBKP vs. RLLF
H0 HA λmax Critical value H0 HA λmax Critical value
r=0 r=1 12.13 14.26 r=0 r=1 10.22 14.26
r=1 r=2 3.99 3.84 r=1 r=2 4.21 3.84
1) Critical values are obtained from MacKinnon et al.5)
Table 3. Results of vector autoregressive (VAR) model and granger causality (LBKP vs. RLLF)
Variable ΔLBKPt ΔRLLFt
Coefficient t-statistic Coefficient t-statistic
Intercept 24.0770 2.06* 64.6475 2.53*
ΔLBKPt-1 1.3136 19.52** 0.1787 1.22
ΔLBKPt-2 -0.3606 -5.43** -0.2264 -1.56
ΔRLLFt-1 0.0310 0.95 1.1179 15.74**
ΔRLLFt-2 -0.0201 -0.62 -0.1778 -2.51*
Granger causality test
F-statistic P-value F-statistic P-value
H0: RLLF does not Granger cause LBKP H0: LBKP does not Granger cause RLLF
0.8923 0.4114 2.0153 0.1361
1) * and ** indicate statistically significant at the 5% and 1% level respectively.
there exists no correlation between them.
3.4 Results of estimating cross hedge ratio and hedging effectiveness
Table 5 presents the results of the linear regres- sion model estimated to obtain the risk-minimiz- ing cross hedge ratio and hedging effectiveness.
The estimated slope coefficients (β1) are 0.0112, 0.0194 respectively, and their correspondent t-statistics show that the estimated hedge ratios are not statistically significant. This implies that the movements in CME RLLF prices cannot explain the movements in wood pulp (LBKP and NBKP) prices.
On the other hand, R2s are 0.0005, 0.0018 respec- tively, indicating that the variation in wood pulp (LBKP and NBKP) prices is little explained by the variation in CME RLLF prices. The result suggests that the variation in wood pulp prices accounted for by the estimated regression model is not signifi- cant.
The above results, in general, suggest that CME
RLLF cannot be used as a cross hedging vehicle for wood pulp. The results could be somewhat foreseen considering the distinct differences between wood pulp and CME lumber in terms of tree species (hardwood vs. softwood), usage (paper production vs. residential home construction), and the like.
4. Conclusions
The primary goal of this study is to determine the cross hedging potential of CME RLLF for wood pulp. The Johansen cointegration tests showed that wood pulp and CME lumber futures were not cointegrated. This suggests that there is no long- run equilibrium relationship between them. The results from Granger causality tests showed no evidence of a causal relationship between wood pulp and CME RLLF. This implies that there is no correlation between them. The results of the re- gression model also showed that the estimated hedge ratios were not statistically significant and Table 4. Results of vector autoregressive (VAR) model and granger causality (NBKP vs. RLLF)
Variable ΔNBKPt ΔRLLFt
Coefficient t-statistic Coefficient t-statistic
Intercept 24.9592 2.30* 56.4672 2.25*
ΔNBKPt-1 1.3220 19.70** -0.0094 -0.06
ΔNBKPt-2 -0.3667 -5.57** -0.0279 -0.18
ΔRLLFt-1 0.0165 0.54 1.1251 15.75**
ΔRLLFt-2 -0.0089 -0.29 -0.1814 -2.55*
Granger causality test
F-statistic P-value F-statistic P-value
H0: RLLF does not Granger cause NBKP H0: NBKP does not Granger cause RLLF
0.4052 0.6674 0.6403 0.5283
1) * and ** indicate statistically significant at the 5% and 1% level respectively.
Table 5. Results of estimating cross hedge ratio and hedging effectiveness
Variable No. of Obs. β0 β1 R2
ΔLBKPt vs. ΔRLLFt 197 0.2483 (0.93) 0.0112 (0.32) 0.0005
ΔNBKPt vs. ΔRLLFt 197 0.2645 (1.04) 0.0194 (0.59) 0.0018
1) Numbers in parenthesis represent t-statistics.
the variation in wood pulp prices was not well explained by the variation in CME RLLF prices. As a conclusion, CME RLLF cannot be used effectively as a cross hedging vehicle for wood pulp.
Literature Cited
1. Till, H., Why haven’t pulp futures contracts succeeded? A case study, Journal of Gover- nance and Regulation 4(4):515-523 (2015).
2. CME Group, Softwood and Hardwood Pulp Futures and Options, Chicago, USA (2009).
3. Johansen, S., Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models, Econometrica 59:1551- 1580 (1991).
4. Ederington, L. H., The hedging performance of the new futures markets, Journal of Finance 34:157-170 (1979).
5. MacKinnon, J. G., Haug, A. A., and Michelis, L., Numerical distribution functions of likeli- hood ratio tests for cointegration, Journal of Applied Econometrics 14:563-577 (1999).
Notes
[1] Thus far, there have been several attempts to launch futures contracts on wood pulp around the world, but all ended up in failure.1) Most recently, the CME Group launched a softwood pulp futures contract in September 2007. The exchange launched a hardwood pulp futures contract in
January 2009 as well.2) Both contracts were per- manently delisted in 2012 due to the lack of liquid- ity (trading volume). The pulp and paper industry is probably the last of the major commodity mar- kets without a functioning futures exchange where hedging would be carried out.
[2] Cross hedging is used when the same futures contract is not available on the commodity being hedged. A futures contract that is similar, but not identical to the underlying commodity, with a high degree of price correlation is used as a substitute.
[3] The contract unit for CME random length lumber futures is 110,000 board feet, and the price is quoted in dollars per 1,000 board feet (mbf). The minimum tick size is $0.10 per 1,000 board feet (=$11.00/contract). The contract months are January, March, May, July, September and Novem- ber. The last trading day is the business day prior to the 16th calendar day of the contract month.
The trading hours are from 9:00 a.m. to 3:05 p.m.
central time, Monday through Friday. The final settlement is done by physical delivery. The deliv- ery unit of each lumber contract consists of 2 inches thick by 4 inches wide, with random lengths ranging from 8 to 20 feet. The primary deliverable lumber species is Western Spruce- Pine-Fir (SPF) including Hem-Fir, Engelmann Spruce and Lodgepole Pine. The producing mills must be located in the states of Oregon, Washing- ton, Idaho, Wyoming, Montana, Nevada, California, or the Canadian provinces of British Columbia or Alberta.
[4] The following tests are conducted using the soft- ware package EViews 10.
[5] The optimal lag length m was determined using the Akaike Information Criterion (AIC).
