- Email: [email protected]
On the ‘efficiency’ of futures markets
On the ‘efficiency’ of futures markets Another view Anthony E. Bopp and Scott Sitzer One dejinition of market eficiency holds that today’s price of an item contains all the price information that there is about that item. That is, today’s price contains irzformation about peoples ’ expectations about the future. However, in estimating price equations analysts almost exclusively look to the past in developing price forecasting models. This paper examines the question.. how do futures prices affect today’s price for heating oil? That is, do futures markets add to the eficiency of markets by conveying information from the future to the present? Keywrd.~: Market
efficiency;
Futures
market:
The ‘efficiency’ of futures markets has been investigated by many researchers by considering two hypotheses. One states that present and past prices cannot be used to find a more accurate forecast of the next price than today’s price - the random walk hypothesis. The second states that forecasts of a future price can never be much more accurate than the random walk forecast -today’s price. Numerous studies about these hypotheses or variations about them (does a risk premium invalidate them or does it exist; are these trading day biases, etc) have been reported (see Taylor [8], for example), but another important function of a futures market has been overlooked in these studies. The future should cast a shadow on present decisions and futures markets are a source of information about the future (Dasgupta and Heal [ 11; Griffith and Steele [4]). In any type of market structure (from competitive to monopolistic) events expected to take place in the future can affect decisions made today. In the area of exhaustible resources this is especially true, but it is true in other areas as well. For example, suppose all OECD (major European, US, Japanese, and Canadian) governments passed a law that in 1990 all motor vehicles had to achieve 50 mpg or higher. The OPEC
Anthony E. Bopp is with Information and Decision Sciences, James Madison University, Harrisonburg, VA 22807, USA, and Scott Sitzer is a Senior Economist, Energy Information Administration, US Department of Energy, Washington, DC 20585, USA. Final manuscript
received
0140-9883/88/030199-07
18 December
$03.00
0
1987.
1988 Butterworth
Heating
oil prices
would know now that in the future their oil would be worth less. As a consequence they would find it more profitable to sell more oil now by lowering today’s price to encourage consumption today. Why hold a resource whose discounted value declines over time and in 1990 the nominal value further declines? The consequence of doing something with no legal impact until 1990 would be to affect very definitely production and consumption decisions now - hence a shadow from the future. Although the above example is dramatic, it is needed to offset a strong bias in forecasters to look to the expected consequences of past actions and events and how they are likely to affect the present or future. The equations found in most journals and in commercial forecasting models almost totally excludefuture variables but contain numerous past ones. The adaptive expectations and the partial adjustment hypotheses give rise to lagged variables and are found in numerous literatures. A whole literature exists exploring how to use lagged variables and the pitfalls encountered in such use. Even special statistics, such as the h-statistic, have been calculated to test hypotheses when lagged variables are present. However, the future can impact the present also and probable specification error or omitted variable problems exist when the role of the future is excluded, which it is routinely. There are two reasons why the future has been excluded from forecasting and modelling efforts. Prior to the Arab embargo in 1973-74 this generation did not consider seriously the future as having a negative impact on the present and the usual positive impact producers
& Co (Publishers)
Ltd
199
On the ‘eficiency
of futures
markets:
A. E. Bopp and S. Sitzer
assumed was the unpredictable effect of technological change. The Arab embargo caused people to look again at the exhaustible resouce theory (rediscover Hotelling [S]) and to enlarge their vision to look to the future. Also, especially in the energy area, no mechanism existed for peeking into the future. It was not until 1978 that distillate futures were traded and not until the 1980s that motor gasoline and crude oil price futures were also traded. Futures markets reflect the opinions of decision-makers about prices at a future date and that conveys information to decisionmakers today about setting price levels. Without a reliable indicator of future prices, decision-makers would be hard pressed to take the future into account today and modellers would have no mechanism to capture the future’s role in present decision-making. Futures markets change this situation completely. Decision-makers now will set today’s cash price, in part, based on the futures price, if they intend to maximize profits (see Farmer [2], for example). Modellers can use the same futures prices that are affecting current decisions as their mechanism to ‘account for’ the effects of changes in the future. The purpose of this paper is to examine distillate prices to determine if distillate futures prices affect today’s cash price. If so, then the distillate futures market will be shown to be serving an important role _ it will be translating producer and consumer views of the future into projections of future prices; which prices are used by oil and energy company executives today in setting today’s price. If so it will illustrate why modellers and forecasters should be looking to futures markets for information just as they look to history for related information.
A distillate price model In order to understand if the distillate futures market adds to the formation of present prices, it is necessary to be able to model adequately how prices are formulated. The price of distillate is determined by factors that affect both the demand and supply of that product, as well as factors that affect the inventory level of this fuel. A model will be specified that well captures these effects. After this model is specified, it will also be examined from a purely heuristic viewpoint and be found to be well-suited for this task from this viewpoint as well. An issue must be confronted at the outset. The term ‘distillate’ is usually applied to liquids that boil above 350°F in the refining process. They are classified as light, medium or heavy. Light and medium distillates can serve as diesel or jet fuel; all three types can serve as heating oil (Glasstone [ 11). At the production level the various ‘distillates’ are nearly perfect substitutes,
200
but once produced they are not so at the consumption level. Ideally all of the various distillate markets could be modelled separately with total distillate production at the refinery a constraint. However, separate inventory and production data is not reliably available at any detail level better than the ‘distillate’ category even though price and consumption data are available for many distillates. Therefore, here the fuel modelled is total distillate, as reported by the EIA in the MER Cl
11.
Distillate fuel is used in the residential, commercial, and industrial sectors as a heating and as a transport fuel. Very little distillate is used in the utility sector as a boiler fuel. At the consumption level, the following factors would be expected to influence distillate consumption: the real distillate price, heating degree days, an index of industrial activity, and the price of a competing heating fuel - the price of natural gas sold to industrial users. The production of distillate would be expected to be influenced by the real distillate price, the price of crude oil, and the price of a competing refinery output - the price of motor gasoline. Dynamic demand and production equations are specified as follows: Q = a,, + a, P, + a2 HDD + a3 JQM + a4 PNG
+a5Q(-11)+eI
(1)
S = be + b, P, + b, P, + b, P,,
+ b,S( - 1) + e2 (2)
where Q is the quantity demanded (plus exports) of distillate (bbl x 106/month), S is the domestic distillate production (plus imports), HDD is heating degree days and JQM is the Federal Reserve Board’s index of manufacturing activity. P,, P,,, P,, P,, are the wholesale price of distillate, the industrial price of natural gas, the wellhead crude oil price, the retail motor gasoline price - all deflated by the PPI. The lagged terms are added to each equation to reflect the fact that demand or production rates will not respond completely or instantly to changes in the other variables. The es are the respective error terms which are described when the price equation estimates are presented. Note that exports of distillate are less than 1% of demand and imports less than 10%. They are included so that the basic balancing identity demand plus exports must be identical to production plus imports less stock change can be used to obtain a single estimable equation for the distillate price. All data is from EIA’s MER [l l] except for HDD (NOAA [ll]); JQM (FRB [12]), and PPI (Commerce Department
ClOl). Demand and production relations by themselves do not determine the price of distillate because of two
ENERGY
ECONOMICS
July 1988
On th
more things: price expectations and inventories. In the inventory literature, it is established that firms should hold inventories up to the point where the marginal storage cost equals the difference between the current and the expected future price (Telser [9]; Working [13]; McCallum [6]). The historical problem with this optimality rule is that expected prices were never reliably known, so that operational versions of the rule relied on methods that could eliminate the expected price from the final set of calculations. For example, assuming linear marginal storage costs, the rule becomes: p,yt + 1) - P, = c(J + Cl I(t)
d(PDe - PO) [O < d < 1)
(4)
Through backward substitutions, Equations (3) and (4) can be solved for P,=(t + 1) and P,’ in terms of the known variables: I(t), 1(f - l), P,, and P,(t - 1) so that PDe(t + 1) and P,’ can be eliminated from Equation (4). Then the basic balancing equation:
Q = S+Z(t-1)+1(t)
(5)
can be used to obtain the following estimable equation:
P,=d,+d,P,(t-l),+d*Q(t-l),+d,S(t-1) + d,I(t1) + d, HDD + d, JQM + d, P,, + d,P, + e,
ECONOMICS
July 1988
A. E. Bopp and S. Sitxr
tion). It is related to stock levels (shortages or surpluses), weather, the state of the economy, and last month’s production and demand levels. There are sound practical and theoretical reasons why every one of these variables could be expected to affect distillate prices. In total, this equation will be seen to present a very sound description of distillate prices, and hence an excellent point of departure to examine if the future can affect present prices. The estimation of Equation (6) is presented in the next section. However, before viewing it, consider the impact of having a futures market. In that case, Equation (3)
P,y+l)-PD=
c,+c,
I
no ionger involves an unobservable term, P,‘( + l), because the expected futures price is in fact what is observed in the futures market. Thus, there is no need to introduce an operational equation so that P,’ can be eliminated. Equation (6) thus becomes the same as before, but now with PDP, the futures price, added to it. To be theoretically precise, Equation (6) would now not include Po( - 1) since it appears during the back substitutions when P,’ is eliminated. The previous price is theoretically included in the knowledge of the previous sales level, D( - 1). However, the previous price is still significant statistically along with crude prices and futures prices both in the equation. Therefore, three estimates of Equation (6) are presented: the historical version without futures prices; and two that include futures prices - one without the lagged price and one that also includes it. These are all presented in the next section.
+ d, P,, (6)
A number of points are in order. First, the adaptive expectations hypothesis is certainly operational in that P,’ is eliminated, but it makes changes in futures prices that are not related to current cash and current expected prices impossible. All expected changes in the future must be proportional to the difference of expected and cash prices now. Sometimes this might be a workable rule, but during periods of interest, it probably is not. No shadows from the future can be expected or cast to the present. Second, if this shortcoming (which is the point of this paper) is overlooked, then an heuristically complete estimation equation is forthcoming. The price of distillate is determined by other prices: crude oil (cost), natural gas (demand competition), and motor gasoline (supply competi-
ENERGY
markets:
(3)
where PDe is the expected future price and 1(t) the inventory level at the end of period t. Now if P,’ is not known then an operational rule is needed. If price expectations are assumed to be adaptive, then the following is obtained: P,‘(t+l)-P,‘=
‘c
Estimation results The previous section indicated that the real distillate price is likely to be a function of: itself lagged one period, demand plus exports lagged one period, production plus imports lagged once, the stock level of distillate lagged once, the current real crude oil price, the current index of manufacturing, a heating degree day variable, the real price of motor gasoline and the real industrial price of natural gas. Listed below are tables of estimation results based on this relationship. Corrections for auto-correlated disturbances were made in all of the estimations since all of the first set of estimates indicated the presence of auto-correlation through the calculated DW statistics, h-statistics (where appropriate), and from inspection of the estimated auto-correlation functions. Table 1 shows the results of the estimated equation
201
On the ‘qficietq~
’ qf‘,fitures murkets:
A. E. Bopp and S. Sitter
Table 1. No futures prices included dependent variable: real wholesale distillate price (RWDP).
Table 3. Six months futures price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
Variable
C
-8.38
- 1.09
c
RWDP( - 1) Demand( - 1) SUPPlY( - 1) Stock( - 1) Crude price JQINDM Weather Motor gas price Industrial gas price
0.238 0.001 -0.001 -0.005 0.956 3.78 0.0002 0.434 0.237
1.61 2.61 -2.41 -0.36 2.69 1.15 0.296 3.67 0.138
FWDP( - 1) Demand( - I) SUPPlY( - 1) Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 6 price
R2 = 0.97 DW = 2.05
F statistic
Coefficient
T-statistic
- 10.77 0.476 0.0011 - 0.0006 0.0029 0.587 4.57 - 0.00005 0.169 0.758 0.199
- 1.26 2.55 2.59 - 1.24 0.35 1.37 1.23 - 0.08 I .42 0.336 3.33
= 213.7 R2 = 0.972 DW = 1.64
F statistic
= 184.7
that employs no futures price. All signs are correct on u priori grounds (higher production or stock levels should depress prices). Four of the eight variables are statistically significant and the expected multicollinearity accounts for all of the important variables not having been found to be significant. The significant variables are: crude oil prices, gasoline prices, lagged production, and lagged demand. (Lagged price is significant at the 0.06 level but not at the 0.05 level.) These estimates are based on monthly data from 1980 to 1985. This equation presents a more than adequate description of what affects distillate prices. High inventory levels tend to push down prices while high crude prices, of course, inflate them. Most futures studies focus on one or two key variables in explaining distillate (or other prices), such as crude oil prices and/or inventory levels. Here a basis has been presented for allowing for eight variables to impact on
distillate prices. These variables, though, are not‘ complete in their accounting for distillate price movements - futures prices have not been included. Tables 24 show the results of adding a futures price to this equation: the 3, 6, and 9 months ahead futures prices. The closing price on the 15th of each month or closest trading day to it was used for the various contract lengths. Beginning and end of month dates were not picked for fear of a potential closing bias or end of month stock reporting bias. A single day was chosen over a monthly average because it was expected that these results would be used in an actual modelling exercise and the last available futures prices are the most efficient set to use. Yesterday’s futures price contains all of the information from the last month. Note a number of things. First in all three cases, the futures prices (3, 6, and 9 months ahead) are statistically significant - even after crude prices, stock levels
Table 2. Three month futures pricesdependent variable: real wholesale distillate price (RWDP).
Table 4. Nine month future price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
Variable
C
- 8.98
- 1.26
C
RDWP(-I) Dcmand( - I ) SUPPlY( - 1) Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 3 price
0.51 0.0008 - 0.0005 - 0.0025 0.383 4.49 0.0001 0.142 0.741 0.243
3.06 2.18 - 1.09 -0.36
RWPD(Dcmand(
R2 = 0.975 DW = 1.81
F statistic
202
I .05 I.61 0. I 7 1.38 0.38 4.98 = 210.1
I) - I)
Coefficient
T-statistic
- 7.46 0.382
-0.82 2.01 2.41 - Ia 0.475
0.001 I -0.0005 0.0042 0.713 3.58 -0.OC01 0.188 0.096 0.165
SUPPlY( - 1)
Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 9 price
F statistic
R* = 0.971 DW = 1.5
ENERGY
1.71 0.864 -0.26 1.39 0.039 2.72 = 176.5
ECONOMICS
July 1988
On the ‘qficienc~~’
other prices, weather, supply and demand are accounted for. Second, the futures prices tend to lose significance in going from 3 to 6 to 9 months ahead but all three are significant. An inspection of the 3 and 9 month contract prices reveals more precision of ‘feeling’ about what will happen three months from now than about what will happen in nine months. (See Appendix). That is, often the one and three month series reflect fractional cent changes and rarely are they unchanged month to month. Often nine month prices are recorded as whole numbers and show less sensitivity than the three month prices. However, in terms of explaining current prices all of the futures prices are significant while such variables as stock levels and weather are not found to be significant here. This does not mean that these variables are not important but that over the estimation period, 198&85, there was not enough variability in these series to predict reliably their impact on distillate prices. Futures prices, though, were found to be significant over the same period. Table 5 presents the results of estimating an equation that contains a three month ahead future price but no lagged cash price. On balance this equation
Table 5. No lagged distillate price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
C Demand( 1) SUPPlY( 1) Stocks(l) Crude prices JQINDM Weather Motor gas price Industrial gas price Futures 3 price
-5.11 0.0006 -0.00007 -0.0014 1.15 -3.14 0.0006 0.344 - 1.32 0.185
-0.57 1.69 -0.134 -0.164 3.31 0.74 I .05 3.12 -0.61 3.41
RZ = 0.969 DW = 1.50
F statistic
= 185.3
of.futures
markets:
A. E. Bopp and S. Sit=er
has about the same explanatory power as the equation reported in Table 1. That is, it is a toss up if the choice is between employing an historical or a futures variable, when all other important variables have been accounted for. Finally, two issues remain. First, would the forecasts generated from those reported in Table 2 (which include the futures information) differ much from those reported in Table 1 (the usual modelling approach)? It could be that adding another term to the equation merely causes all the coefficient estimates to re-adjust to accomodate the extra term. That is not the case here. A number of simulations were attempted and usually they produced forecasts that were 5-10% different. Consider two of them. In the first all values are held constant over 1986 except the futures price which is forecast to decline by 10%. In this case, the equation in Table 2 with the future price included generated forecasts about 4% lower than those in Table 1, that contained no futures price. A second scenario was constructed where crude prices rose by lo%, motor gasoline by 8%, natural gas by 6%, and future prices by 20%. The first case might be labelled a continued OPEC decline one while this second one is a return to power for OPEC. In the second case, the futures model generates price estimates 8% higher in 1986 than the model without futures. Five cents a gallon is $2.10 a barrel - a huge difference. As for the second question, which one is more accurate, there is never a perfect answer to that question. Any specific historical period may be an anomaly and there is no assurance that historical accuracy will be translated into future accuracy. However, consider Table 6 which shows the 3, 6, and 9 month futures prices over the last half of 1985 and the actual 1986 prices. Late in 1985 the futures market began to see correctly the price downturn in 1986. Using EIA assumptions from the summer of 1985, the equation in Table 2 would have actually generated a forecast decline of 7% in the first half of 1986 over the last half of 1985. Using the same assumptions, Table 1’s forecast for the first half of 1986 projected a 2%
Table 6. Distillate prices: cash and futures (cents/gallon).
Date 1985.07 1985.08 1985.09 1985.10 1985.11 1985.12 1986:Ol 1986:02 1986:03
ENERGY
Cash 70.30000 72.OOOOO 77.OOOOO 81.70000 84.90000 83.2OCKM 73.70000 56.40000 51.9OcQO
ECONOMICS
July 1988
3 months 70.24000 75.77000 77.28000 83.11000 85.70000 71.29000
6 months 72.OOMIO 75.90000 72.20000 74.43000 74.28000 64.50000
9 months 68.3OOOCI 69.75000 68.20000 72.5oooO 71.90000 63.80000
203
On the ‘qficienq~ ’ of‘.fitures
markets:
A. E. Bopp and S. Sitter
drop in prices. Actual prices fell by 18%. Again, a single episode does not prove anything but also no amount of looking backwards would give an analyst the insight into the 1986 distillate market that looking at futures prices late in 1985 did. Nothing in the distillate market in 1985 gave a clue as to what would happen in 1986 except for the futures prices.
future to explain the present, of course, much room for further study in this area.
References
Conclusions This paper has examined the efficiency of futures markets from a different perspective. It has looked at how the future sends ‘signals’ back to the present and how the futures market is the mechanism for those transmissions. It has attempted to alert forecasters to look into the future by looking at futures markets when predicting current cash prices because profitmaximizing decision makers will be doing the same. For the case chosen to illustrate the points here, distillate prices, it was seen that even when crude oil prices, inventory levels, weather, and other important variables were accounted for, that futures prices still make a significant positive contribution to describing past price changes and that forecasts that were generated taking futures prices into account proved different and in the first part of 1986 better than did those that ignored the future. Refinements on looking to the
constitute
7 8 9
10 11 12 13
P. Dasgupta and G. Heal, Economic Theon! and Eshaztible Resources, Cambridge University Press, 1979. R. Farmer, ‘Futures market and petroleum supply’, US Government Printing Office, DOEjEIA-0486, 1986. S. Glasstone, Energy Deskbook. National Technical Information Center, DOE/IR/05114-1, 1982. J. Griffith and H. Steele, Energy Economics and Policy, Academic Press, 1980. H. Hotelling, ‘The economics of exhaustible resources’, Journal of Political Economy, Vol 39, 193 1, pp 137-l 75. B. McCallum, ‘Competitive price adjustments: an empirical study’, American Economic Review, Vol64, 1974. pp 56-65. National Oceanic and Atmospheric Administration, Historical Climatology Series 5-l and 5-2, 1986. S. Taylor, ‘The behavior of futures prices over time’, Applied Economics, Vol 17, 1985, pp 713-734. L. Telsen, ‘Futures trading and the storage of cotton and wheat’, Journal of PoIitical Economy, Vol66, 1958, pp 233-255. US Department of Commerce, Survey of Current Business, March 1986. US Department of Energy, Energy Information Administration, Monthly Energy Review, March 1986. US Federal Reserve Board, Industrial Production, March 1986. H. Working, ‘The theory of the price of storage’, American Economic Review, Vol39,1949, pp 1254-1262.
Appendix Table 7. Cash and futures prices. Futures prices Date 1980.0 I 1980.02 1980.03 1980.04 1980.05 1980.06 1980.07 1980.08 1980.09 1980.10 1980.1 I 1980. I2 1981.01 1981.02 1981.03 1981.04 1981.05 1981.06 1981.07 1981.08 1981.09 1981.10 1981.1 I
204
Cash price 75.2OWO 79.OoOoO 80.40000 81.OOOoO 81.400@0 82.50000 83.OOOOO 82.90000 83.OOOOO 83.70000 86.lOoOo 91.3OOoO 98.60000 106.0000 106.3000 105.2000 104.0000 103.0000 102.7000 102.2000 101.6000 101.1000 102.3000
3 months 77.50000 77.65000 75.50000 79.6OOoO 81.2OfMO 79.90000 81.10000 77.85000 79.9OOca 85.36000 97.m 95.8oOoO 102.9500 99.85000 95.65000 97sOoOO 93.57000 92.63000 96.55000 98.70000 97.34000 101.6700 102.6900
6 months 81.OOOOO 81.60000 81.OOOOO 84.7OOoo 87.OOOOO 86.20000 86.26ooo 82.3OWO 84.75000 89.OOOOo 100.9000 99.3ooOO 104.7000 102.0500 99.20000 102.5000 99.1OoOO 96.55000 101.1500 103.9cKKl 100.9000 101.6000 100.6500
ENERGY
ECONOMICS
9 months 89.25000 9o.OOooO 86.OOOOO 92.75000 93.oOoOO 92.OOOW 9o.OooOO 84.5OooO 86.OoOOO 9l.ooooo 102.9700 100.6000 103.6000 106.6MKl 106.5000 108.3000 103.8000 100.5000 105.Oooo 106.5000 102.3000 103.2500 102.1500
July 1988
On the ‘qficirncy
’ qffitures
markets:
A. E. Bopp and S. Sitxr
Table 7. Cash and futures prices (contd). Futures
prices 6 mouths
Date
Cash price
3 months
1981.12 1982.01 1982.02 1982.03 1982.04 1982.05 1982.06 1982.07 1982.08 1982.09 1982.10 1982.1 I 1982.12 1983.01 1983.02 1983.03 1983.04 1983.05 1983.06 1983.07 1983.08 1983.09 1983.10 1983.11 1983.12 1984.01 1984.02 1984.03 1984.04 1984.05 1984.06 1984.07 1984.08 1984.09 1984.10 1984.11 1984.12 1985.01 1985.02 1985.03 1985.04 1985.05 1985.06 1985.07 1985.08 1985.09 1985.10 1985.1 I 1985.12
102.6000 99.OOOw 94.OOOOO 86.6OoOO 83.30000 86.50000 89.80000 9l.OOOOO 90.3OOOo 92.OOOOO 96.50000 97.3OOOo 89.50000 85.70000 80.10000 76.OOOOO 78.90000 80.90000 80.90000 81.70000 83.40000 85.10000 83.50000 82.60000 80.70000 87.50000 89.20000 81.30000 82.80000 83.20000 82.400043 79.40000 77.80000 8O.OOOOO 80.80000 79.40000 77.10000 75.70000 75.20000 76.40000 79.3OOoO 76.50000 72.90000 70.3OoOO 72.OC000 77.OOOOO 81.70000 84.90000 83.20000
97.71000 91.32000 80.55000 69.78000 86.99000 94.25000 92.01000 89.51000 90.02000 98.33000 102.8300 93.07000 85.20000 78.88000 71.89000 72.76000 77.20000 76.99000 83.32000 85.53000 88.60000 86.73000 83.lOOOo 80.19000 74.54000 78.18000 74.57000 76.53000 77.77000 81.12000 78.83000 78.35000 79.36000 82.32000 80.19000 79.33000 73.39000 68.75000 69.86OCKI 71.83000 73.36000 70.20000 68.OOOOO 70.25000 75.77000 77.28000 83.11000 85.70000 71.29000
Source: Cash prices - MER; futures
ENERGY
ECONOMICS
prices - NY Mercantile
July 1988
Exchange,
94.35000 90.12000 82.10000 72.40000 8990000 97.20000 94.50000 9l.OOoOO 91.10000 96.5WOO 97.42000 89.OOOOO 82.50000 78.lOOOo 72.60000 74.38000 79.75ooo 8O.OOOOO 85.61000 87.65000 89.2OCKtO 84.36000 78.5OOCIO 75.30000 72.40000 76.OOOOO 75.20000 78.90000 80.65000 84.40000 82.65000 81.70000 8O.OOOOO 80.10000 75.50000 72.35000 69.45000 66.83000 69.30000 72.45ooO 75.OOoOO 72.7OOW 70.25000 72.OOOOO 75.90000 72.20000 74.43000 74.28000 64.5wOO
9 months 95.90000 92.20000 89.OOOOO 76.OWOO 92.25000 101.0000 96.50000 91.OOOoO 87.50000 90.50000 93.OOOccl 94.10000 82.90000 80.80000 76.30000 77.OOOOO 81.97000 81.OCWO 84.75ooO 83.OOOOO 86.OOOOO 82.9OOoO 76.90000 75.15000 74.45OOLl 77.95000 77.7OOoO 81.85000 84.OOOOO 86.35000 80.40000 77.4000 77.20000 78.OOOOO 74.OOOOO 71.OoOOo 75.OOOOO 72.60000 73.OOooO 75.60000 78.OOOOO 74.90000 70.65OW 68.30000 69.75000 68.2CWO 72.50000 71.90000 63.80000
Daily Futures Report.
20.5
efficiency;
Futures
market:
The ‘efficiency’ of futures markets has been investigated by many researchers by considering two hypotheses. One states that present and past prices cannot be used to find a more accurate forecast of the next price than today’s price - the random walk hypothesis. The second states that forecasts of a future price can never be much more accurate than the random walk forecast -today’s price. Numerous studies about these hypotheses or variations about them (does a risk premium invalidate them or does it exist; are these trading day biases, etc) have been reported (see Taylor [8], for example), but another important function of a futures market has been overlooked in these studies. The future should cast a shadow on present decisions and futures markets are a source of information about the future (Dasgupta and Heal [ 11; Griffith and Steele [4]). In any type of market structure (from competitive to monopolistic) events expected to take place in the future can affect decisions made today. In the area of exhaustible resources this is especially true, but it is true in other areas as well. For example, suppose all OECD (major European, US, Japanese, and Canadian) governments passed a law that in 1990 all motor vehicles had to achieve 50 mpg or higher. The OPEC
Anthony E. Bopp is with Information and Decision Sciences, James Madison University, Harrisonburg, VA 22807, USA, and Scott Sitzer is a Senior Economist, Energy Information Administration, US Department of Energy, Washington, DC 20585, USA. Final manuscript
received
0140-9883/88/030199-07
18 December
$03.00
0
1987.
1988 Butterworth
Heating
oil prices
would know now that in the future their oil would be worth less. As a consequence they would find it more profitable to sell more oil now by lowering today’s price to encourage consumption today. Why hold a resource whose discounted value declines over time and in 1990 the nominal value further declines? The consequence of doing something with no legal impact until 1990 would be to affect very definitely production and consumption decisions now - hence a shadow from the future. Although the above example is dramatic, it is needed to offset a strong bias in forecasters to look to the expected consequences of past actions and events and how they are likely to affect the present or future. The equations found in most journals and in commercial forecasting models almost totally excludefuture variables but contain numerous past ones. The adaptive expectations and the partial adjustment hypotheses give rise to lagged variables and are found in numerous literatures. A whole literature exists exploring how to use lagged variables and the pitfalls encountered in such use. Even special statistics, such as the h-statistic, have been calculated to test hypotheses when lagged variables are present. However, the future can impact the present also and probable specification error or omitted variable problems exist when the role of the future is excluded, which it is routinely. There are two reasons why the future has been excluded from forecasting and modelling efforts. Prior to the Arab embargo in 1973-74 this generation did not consider seriously the future as having a negative impact on the present and the usual positive impact producers
& Co (Publishers)
Ltd
199
On the ‘eficiency
of futures
markets:
A. E. Bopp and S. Sitzer
assumed was the unpredictable effect of technological change. The Arab embargo caused people to look again at the exhaustible resouce theory (rediscover Hotelling [S]) and to enlarge their vision to look to the future. Also, especially in the energy area, no mechanism existed for peeking into the future. It was not until 1978 that distillate futures were traded and not until the 1980s that motor gasoline and crude oil price futures were also traded. Futures markets reflect the opinions of decision-makers about prices at a future date and that conveys information to decisionmakers today about setting price levels. Without a reliable indicator of future prices, decision-makers would be hard pressed to take the future into account today and modellers would have no mechanism to capture the future’s role in present decision-making. Futures markets change this situation completely. Decision-makers now will set today’s cash price, in part, based on the futures price, if they intend to maximize profits (see Farmer [2], for example). Modellers can use the same futures prices that are affecting current decisions as their mechanism to ‘account for’ the effects of changes in the future. The purpose of this paper is to examine distillate prices to determine if distillate futures prices affect today’s cash price. If so, then the distillate futures market will be shown to be serving an important role _ it will be translating producer and consumer views of the future into projections of future prices; which prices are used by oil and energy company executives today in setting today’s price. If so it will illustrate why modellers and forecasters should be looking to futures markets for information just as they look to history for related information.
A distillate price model In order to understand if the distillate futures market adds to the formation of present prices, it is necessary to be able to model adequately how prices are formulated. The price of distillate is determined by factors that affect both the demand and supply of that product, as well as factors that affect the inventory level of this fuel. A model will be specified that well captures these effects. After this model is specified, it will also be examined from a purely heuristic viewpoint and be found to be well-suited for this task from this viewpoint as well. An issue must be confronted at the outset. The term ‘distillate’ is usually applied to liquids that boil above 350°F in the refining process. They are classified as light, medium or heavy. Light and medium distillates can serve as diesel or jet fuel; all three types can serve as heating oil (Glasstone [ 11). At the production level the various ‘distillates’ are nearly perfect substitutes,
200
but once produced they are not so at the consumption level. Ideally all of the various distillate markets could be modelled separately with total distillate production at the refinery a constraint. However, separate inventory and production data is not reliably available at any detail level better than the ‘distillate’ category even though price and consumption data are available for many distillates. Therefore, here the fuel modelled is total distillate, as reported by the EIA in the MER Cl
11.
Distillate fuel is used in the residential, commercial, and industrial sectors as a heating and as a transport fuel. Very little distillate is used in the utility sector as a boiler fuel. At the consumption level, the following factors would be expected to influence distillate consumption: the real distillate price, heating degree days, an index of industrial activity, and the price of a competing heating fuel - the price of natural gas sold to industrial users. The production of distillate would be expected to be influenced by the real distillate price, the price of crude oil, and the price of a competing refinery output - the price of motor gasoline. Dynamic demand and production equations are specified as follows: Q = a,, + a, P, + a2 HDD + a3 JQM + a4 PNG
+a5Q(-11)+eI
(1)
S = be + b, P, + b, P, + b, P,,
+ b,S( - 1) + e2 (2)
where Q is the quantity demanded (plus exports) of distillate (bbl x 106/month), S is the domestic distillate production (plus imports), HDD is heating degree days and JQM is the Federal Reserve Board’s index of manufacturing activity. P,, P,,, P,, P,, are the wholesale price of distillate, the industrial price of natural gas, the wellhead crude oil price, the retail motor gasoline price - all deflated by the PPI. The lagged terms are added to each equation to reflect the fact that demand or production rates will not respond completely or instantly to changes in the other variables. The es are the respective error terms which are described when the price equation estimates are presented. Note that exports of distillate are less than 1% of demand and imports less than 10%. They are included so that the basic balancing identity demand plus exports must be identical to production plus imports less stock change can be used to obtain a single estimable equation for the distillate price. All data is from EIA’s MER [l l] except for HDD (NOAA [ll]); JQM (FRB [12]), and PPI (Commerce Department
ClOl). Demand and production relations by themselves do not determine the price of distillate because of two
ENERGY
ECONOMICS
July 1988
On th
more things: price expectations and inventories. In the inventory literature, it is established that firms should hold inventories up to the point where the marginal storage cost equals the difference between the current and the expected future price (Telser [9]; Working [13]; McCallum [6]). The historical problem with this optimality rule is that expected prices were never reliably known, so that operational versions of the rule relied on methods that could eliminate the expected price from the final set of calculations. For example, assuming linear marginal storage costs, the rule becomes: p,yt + 1) - P, = c(J + Cl I(t)
d(PDe - PO) [O < d < 1)
(4)
Through backward substitutions, Equations (3) and (4) can be solved for P,=(t + 1) and P,’ in terms of the known variables: I(t), 1(f - l), P,, and P,(t - 1) so that PDe(t + 1) and P,’ can be eliminated from Equation (4). Then the basic balancing equation:
Q = S+Z(t-1)+1(t)
(5)
can be used to obtain the following estimable equation:
P,=d,+d,P,(t-l),+d*Q(t-l),+d,S(t-1) + d,I(t1) + d, HDD + d, JQM + d, P,, + d,P, + e,
ECONOMICS
July 1988
A. E. Bopp and S. Sitxr
tion). It is related to stock levels (shortages or surpluses), weather, the state of the economy, and last month’s production and demand levels. There are sound practical and theoretical reasons why every one of these variables could be expected to affect distillate prices. In total, this equation will be seen to present a very sound description of distillate prices, and hence an excellent point of departure to examine if the future can affect present prices. The estimation of Equation (6) is presented in the next section. However, before viewing it, consider the impact of having a futures market. In that case, Equation (3)
P,y+l)-PD=
c,+c,
I
no ionger involves an unobservable term, P,‘( + l), because the expected futures price is in fact what is observed in the futures market. Thus, there is no need to introduce an operational equation so that P,’ can be eliminated. Equation (6) thus becomes the same as before, but now with PDP, the futures price, added to it. To be theoretically precise, Equation (6) would now not include Po( - 1) since it appears during the back substitutions when P,’ is eliminated. The previous price is theoretically included in the knowledge of the previous sales level, D( - 1). However, the previous price is still significant statistically along with crude prices and futures prices both in the equation. Therefore, three estimates of Equation (6) are presented: the historical version without futures prices; and two that include futures prices - one without the lagged price and one that also includes it. These are all presented in the next section.
+ d, P,, (6)
A number of points are in order. First, the adaptive expectations hypothesis is certainly operational in that P,’ is eliminated, but it makes changes in futures prices that are not related to current cash and current expected prices impossible. All expected changes in the future must be proportional to the difference of expected and cash prices now. Sometimes this might be a workable rule, but during periods of interest, it probably is not. No shadows from the future can be expected or cast to the present. Second, if this shortcoming (which is the point of this paper) is overlooked, then an heuristically complete estimation equation is forthcoming. The price of distillate is determined by other prices: crude oil (cost), natural gas (demand competition), and motor gasoline (supply competi-
ENERGY
markets:
(3)
where PDe is the expected future price and 1(t) the inventory level at the end of period t. Now if P,’ is not known then an operational rule is needed. If price expectations are assumed to be adaptive, then the following is obtained: P,‘(t+l)-P,‘=
‘c
Estimation results The previous section indicated that the real distillate price is likely to be a function of: itself lagged one period, demand plus exports lagged one period, production plus imports lagged once, the stock level of distillate lagged once, the current real crude oil price, the current index of manufacturing, a heating degree day variable, the real price of motor gasoline and the real industrial price of natural gas. Listed below are tables of estimation results based on this relationship. Corrections for auto-correlated disturbances were made in all of the estimations since all of the first set of estimates indicated the presence of auto-correlation through the calculated DW statistics, h-statistics (where appropriate), and from inspection of the estimated auto-correlation functions. Table 1 shows the results of the estimated equation
201
On the ‘qficietq~
’ qf‘,fitures murkets:
A. E. Bopp and S. Sitter
Table 1. No futures prices included dependent variable: real wholesale distillate price (RWDP).
Table 3. Six months futures price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
Variable
C
-8.38
- 1.09
c
RWDP( - 1) Demand( - 1) SUPPlY( - 1) Stock( - 1) Crude price JQINDM Weather Motor gas price Industrial gas price
0.238 0.001 -0.001 -0.005 0.956 3.78 0.0002 0.434 0.237
1.61 2.61 -2.41 -0.36 2.69 1.15 0.296 3.67 0.138
FWDP( - 1) Demand( - I) SUPPlY( - 1) Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 6 price
R2 = 0.97 DW = 2.05
F statistic
Coefficient
T-statistic
- 10.77 0.476 0.0011 - 0.0006 0.0029 0.587 4.57 - 0.00005 0.169 0.758 0.199
- 1.26 2.55 2.59 - 1.24 0.35 1.37 1.23 - 0.08 I .42 0.336 3.33
= 213.7 R2 = 0.972 DW = 1.64
F statistic
= 184.7
that employs no futures price. All signs are correct on u priori grounds (higher production or stock levels should depress prices). Four of the eight variables are statistically significant and the expected multicollinearity accounts for all of the important variables not having been found to be significant. The significant variables are: crude oil prices, gasoline prices, lagged production, and lagged demand. (Lagged price is significant at the 0.06 level but not at the 0.05 level.) These estimates are based on monthly data from 1980 to 1985. This equation presents a more than adequate description of what affects distillate prices. High inventory levels tend to push down prices while high crude prices, of course, inflate them. Most futures studies focus on one or two key variables in explaining distillate (or other prices), such as crude oil prices and/or inventory levels. Here a basis has been presented for allowing for eight variables to impact on
distillate prices. These variables, though, are not‘ complete in their accounting for distillate price movements - futures prices have not been included. Tables 24 show the results of adding a futures price to this equation: the 3, 6, and 9 months ahead futures prices. The closing price on the 15th of each month or closest trading day to it was used for the various contract lengths. Beginning and end of month dates were not picked for fear of a potential closing bias or end of month stock reporting bias. A single day was chosen over a monthly average because it was expected that these results would be used in an actual modelling exercise and the last available futures prices are the most efficient set to use. Yesterday’s futures price contains all of the information from the last month. Note a number of things. First in all three cases, the futures prices (3, 6, and 9 months ahead) are statistically significant - even after crude prices, stock levels
Table 2. Three month futures pricesdependent variable: real wholesale distillate price (RWDP).
Table 4. Nine month future price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
Variable
C
- 8.98
- 1.26
C
RDWP(-I) Dcmand( - I ) SUPPlY( - 1) Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 3 price
0.51 0.0008 - 0.0005 - 0.0025 0.383 4.49 0.0001 0.142 0.741 0.243
3.06 2.18 - 1.09 -0.36
RWPD(Dcmand(
R2 = 0.975 DW = 1.81
F statistic
202
I .05 I.61 0. I 7 1.38 0.38 4.98 = 210.1
I) - I)
Coefficient
T-statistic
- 7.46 0.382
-0.82 2.01 2.41 - Ia 0.475
0.001 I -0.0005 0.0042 0.713 3.58 -0.OC01 0.188 0.096 0.165
SUPPlY( - 1)
Stocks( - I) Crude price JQINDM Weather Motor gas price Industrial gas price Futures 9 price
F statistic
R* = 0.971 DW = 1.5
ENERGY
1.71 0.864 -0.26 1.39 0.039 2.72 = 176.5
ECONOMICS
July 1988
On the ‘qficienc~~’
other prices, weather, supply and demand are accounted for. Second, the futures prices tend to lose significance in going from 3 to 6 to 9 months ahead but all three are significant. An inspection of the 3 and 9 month contract prices reveals more precision of ‘feeling’ about what will happen three months from now than about what will happen in nine months. (See Appendix). That is, often the one and three month series reflect fractional cent changes and rarely are they unchanged month to month. Often nine month prices are recorded as whole numbers and show less sensitivity than the three month prices. However, in terms of explaining current prices all of the futures prices are significant while such variables as stock levels and weather are not found to be significant here. This does not mean that these variables are not important but that over the estimation period, 198&85, there was not enough variability in these series to predict reliably their impact on distillate prices. Futures prices, though, were found to be significant over the same period. Table 5 presents the results of estimating an equation that contains a three month ahead future price but no lagged cash price. On balance this equation
Table 5. No lagged distillate price dependent variable: real wholesale distillate price (RWDP).
Variable
Coefficient
T-statistic
C Demand( 1) SUPPlY( 1) Stocks(l) Crude prices JQINDM Weather Motor gas price Industrial gas price Futures 3 price
-5.11 0.0006 -0.00007 -0.0014 1.15 -3.14 0.0006 0.344 - 1.32 0.185
-0.57 1.69 -0.134 -0.164 3.31 0.74 I .05 3.12 -0.61 3.41
RZ = 0.969 DW = 1.50
F statistic
= 185.3
of.futures
markets:
A. E. Bopp and S. Sit=er
has about the same explanatory power as the equation reported in Table 1. That is, it is a toss up if the choice is between employing an historical or a futures variable, when all other important variables have been accounted for. Finally, two issues remain. First, would the forecasts generated from those reported in Table 2 (which include the futures information) differ much from those reported in Table 1 (the usual modelling approach)? It could be that adding another term to the equation merely causes all the coefficient estimates to re-adjust to accomodate the extra term. That is not the case here. A number of simulations were attempted and usually they produced forecasts that were 5-10% different. Consider two of them. In the first all values are held constant over 1986 except the futures price which is forecast to decline by 10%. In this case, the equation in Table 2 with the future price included generated forecasts about 4% lower than those in Table 1, that contained no futures price. A second scenario was constructed where crude prices rose by lo%, motor gasoline by 8%, natural gas by 6%, and future prices by 20%. The first case might be labelled a continued OPEC decline one while this second one is a return to power for OPEC. In the second case, the futures model generates price estimates 8% higher in 1986 than the model without futures. Five cents a gallon is $2.10 a barrel - a huge difference. As for the second question, which one is more accurate, there is never a perfect answer to that question. Any specific historical period may be an anomaly and there is no assurance that historical accuracy will be translated into future accuracy. However, consider Table 6 which shows the 3, 6, and 9 month futures prices over the last half of 1985 and the actual 1986 prices. Late in 1985 the futures market began to see correctly the price downturn in 1986. Using EIA assumptions from the summer of 1985, the equation in Table 2 would have actually generated a forecast decline of 7% in the first half of 1986 over the last half of 1985. Using the same assumptions, Table 1’s forecast for the first half of 1986 projected a 2%
Table 6. Distillate prices: cash and futures (cents/gallon).
Date 1985.07 1985.08 1985.09 1985.10 1985.11 1985.12 1986:Ol 1986:02 1986:03
ENERGY
Cash 70.30000 72.OOOOO 77.OOOOO 81.70000 84.90000 83.2OCKM 73.70000 56.40000 51.9OcQO
ECONOMICS
July 1988
3 months 70.24000 75.77000 77.28000 83.11000 85.70000 71.29000
6 months 72.OOMIO 75.90000 72.20000 74.43000 74.28000 64.50000
9 months 68.3OOOCI 69.75000 68.20000 72.5oooO 71.90000 63.80000
203
On the ‘qficienq~ ’ of‘.fitures
markets:
A. E. Bopp and S. Sitter
drop in prices. Actual prices fell by 18%. Again, a single episode does not prove anything but also no amount of looking backwards would give an analyst the insight into the 1986 distillate market that looking at futures prices late in 1985 did. Nothing in the distillate market in 1985 gave a clue as to what would happen in 1986 except for the futures prices.
future to explain the present, of course, much room for further study in this area.
References
Conclusions This paper has examined the efficiency of futures markets from a different perspective. It has looked at how the future sends ‘signals’ back to the present and how the futures market is the mechanism for those transmissions. It has attempted to alert forecasters to look into the future by looking at futures markets when predicting current cash prices because profitmaximizing decision makers will be doing the same. For the case chosen to illustrate the points here, distillate prices, it was seen that even when crude oil prices, inventory levels, weather, and other important variables were accounted for, that futures prices still make a significant positive contribution to describing past price changes and that forecasts that were generated taking futures prices into account proved different and in the first part of 1986 better than did those that ignored the future. Refinements on looking to the
constitute
7 8 9
10 11 12 13
P. Dasgupta and G. Heal, Economic Theon! and Eshaztible Resources, Cambridge University Press, 1979. R. Farmer, ‘Futures market and petroleum supply’, US Government Printing Office, DOEjEIA-0486, 1986. S. Glasstone, Energy Deskbook. National Technical Information Center, DOE/IR/05114-1, 1982. J. Griffith and H. Steele, Energy Economics and Policy, Academic Press, 1980. H. Hotelling, ‘The economics of exhaustible resources’, Journal of Political Economy, Vol 39, 193 1, pp 137-l 75. B. McCallum, ‘Competitive price adjustments: an empirical study’, American Economic Review, Vol64, 1974. pp 56-65. National Oceanic and Atmospheric Administration, Historical Climatology Series 5-l and 5-2, 1986. S. Taylor, ‘The behavior of futures prices over time’, Applied Economics, Vol 17, 1985, pp 713-734. L. Telsen, ‘Futures trading and the storage of cotton and wheat’, Journal of PoIitical Economy, Vol66, 1958, pp 233-255. US Department of Commerce, Survey of Current Business, March 1986. US Department of Energy, Energy Information Administration, Monthly Energy Review, March 1986. US Federal Reserve Board, Industrial Production, March 1986. H. Working, ‘The theory of the price of storage’, American Economic Review, Vol39,1949, pp 1254-1262.
Appendix Table 7. Cash and futures prices. Futures prices Date 1980.0 I 1980.02 1980.03 1980.04 1980.05 1980.06 1980.07 1980.08 1980.09 1980.10 1980.1 I 1980. I2 1981.01 1981.02 1981.03 1981.04 1981.05 1981.06 1981.07 1981.08 1981.09 1981.10 1981.1 I
204
Cash price 75.2OWO 79.OoOoO 80.40000 81.OOOoO 81.400@0 82.50000 83.OOOOO 82.90000 83.OOOOO 83.70000 86.lOoOo 91.3OOoO 98.60000 106.0000 106.3000 105.2000 104.0000 103.0000 102.7000 102.2000 101.6000 101.1000 102.3000
3 months 77.50000 77.65000 75.50000 79.6OOoO 81.2OfMO 79.90000 81.10000 77.85000 79.9OOca 85.36000 97.m 95.8oOoO 102.9500 99.85000 95.65000 97sOoOO 93.57000 92.63000 96.55000 98.70000 97.34000 101.6700 102.6900
6 months 81.OOOOO 81.60000 81.OOOOO 84.7OOoo 87.OOOOO 86.20000 86.26ooo 82.3OWO 84.75000 89.OOOOo 100.9000 99.3ooOO 104.7000 102.0500 99.20000 102.5000 99.1OoOO 96.55000 101.1500 103.9cKKl 100.9000 101.6000 100.6500
ENERGY
ECONOMICS
9 months 89.25000 9o.OOooO 86.OOOOO 92.75000 93.oOoOO 92.OOOW 9o.OooOO 84.5OooO 86.OoOOO 9l.ooooo 102.9700 100.6000 103.6000 106.6MKl 106.5000 108.3000 103.8000 100.5000 105.Oooo 106.5000 102.3000 103.2500 102.1500
July 1988
On the ‘qficirncy
’ qffitures
markets:
A. E. Bopp and S. Sitxr
Table 7. Cash and futures prices (contd). Futures
prices 6 mouths
Date
Cash price
3 months
1981.12 1982.01 1982.02 1982.03 1982.04 1982.05 1982.06 1982.07 1982.08 1982.09 1982.10 1982.1 I 1982.12 1983.01 1983.02 1983.03 1983.04 1983.05 1983.06 1983.07 1983.08 1983.09 1983.10 1983.11 1983.12 1984.01 1984.02 1984.03 1984.04 1984.05 1984.06 1984.07 1984.08 1984.09 1984.10 1984.11 1984.12 1985.01 1985.02 1985.03 1985.04 1985.05 1985.06 1985.07 1985.08 1985.09 1985.10 1985.1 I 1985.12
102.6000 99.OOOw 94.OOOOO 86.6OoOO 83.30000 86.50000 89.80000 9l.OOOOO 90.3OOOo 92.OOOOO 96.50000 97.3OOOo 89.50000 85.70000 80.10000 76.OOOOO 78.90000 80.90000 80.90000 81.70000 83.40000 85.10000 83.50000 82.60000 80.70000 87.50000 89.20000 81.30000 82.80000 83.20000 82.400043 79.40000 77.80000 8O.OOOOO 80.80000 79.40000 77.10000 75.70000 75.20000 76.40000 79.3OOoO 76.50000 72.90000 70.3OoOO 72.OC000 77.OOOOO 81.70000 84.90000 83.20000
97.71000 91.32000 80.55000 69.78000 86.99000 94.25000 92.01000 89.51000 90.02000 98.33000 102.8300 93.07000 85.20000 78.88000 71.89000 72.76000 77.20000 76.99000 83.32000 85.53000 88.60000 86.73000 83.lOOOo 80.19000 74.54000 78.18000 74.57000 76.53000 77.77000 81.12000 78.83000 78.35000 79.36000 82.32000 80.19000 79.33000 73.39000 68.75000 69.86OCKI 71.83000 73.36000 70.20000 68.OOOOO 70.25000 75.77000 77.28000 83.11000 85.70000 71.29000
Source: Cash prices - MER; futures
ENERGY
ECONOMICS
prices - NY Mercantile
July 1988
Exchange,
94.35000 90.12000 82.10000 72.40000 8990000 97.20000 94.50000 9l.OOoOO 91.10000 96.5WOO 97.42000 89.OOOOO 82.50000 78.lOOOo 72.60000 74.38000 79.75ooo 8O.OOOOO 85.61000 87.65000 89.2OCKtO 84.36000 78.5OOCIO 75.30000 72.40000 76.OOOOO 75.20000 78.90000 80.65000 84.40000 82.65000 81.70000 8O.OOOOO 80.10000 75.50000 72.35000 69.45000 66.83000 69.30000 72.45ooO 75.OOoOO 72.7OOW 70.25000 72.OOOOO 75.90000 72.20000 74.43000 74.28000 64.5wOO
9 months 95.90000 92.20000 89.OOOOO 76.OWOO 92.25000 101.0000 96.50000 91.OOOoO 87.50000 90.50000 93.OOOccl 94.10000 82.90000 80.80000 76.30000 77.OOOOO 81.97000 81.OCWO 84.75ooO 83.OOOOO 86.OOOOO 82.9OOoO 76.90000 75.15000 74.45OOLl 77.95000 77.7OOoO 81.85000 84.OOOOO 86.35000 80.40000 77.4000 77.20000 78.OOOOO 74.OOOOO 71.OoOOo 75.OOOOO 72.60000 73.OOooO 75.60000 78.OOOOO 74.90000 70.65OW 68.30000 69.75000 68.2CWO 72.50000 71.90000 63.80000
Daily Futures Report.
20.5