Download Price Discovery through Crude Palm Oil Futures: An

Price Discovery through Crude Palm Oil
Futures: An Economic Evaluation 1
By
Fatimah Mohd. Arshad
Zainalabidin Mohamed
Faculty of Economics and Management
Universiti Putra Malaysia
43400 Serdang Selangor Malaysia
Email: [email protected]
[email protected]
Abstract
This paper examines the forward pricing efficiency of the local crude palm oil (CPO) futures market. In
an efficient market, the relevant signal to be used by -the producers, traders and processors is simply
the futures price. The forward pricing efficiency is measured in terms of the forecasting ability of
Malaysian crude palm oil futures price on physical price. The relative predictive power of futures price
is compared with the various forecasts estimated from proven forecasting techniques like moving
average, exponential smoothing, Box Jenkins and econometric. The relative predictability of futures
price as a forecast for spot prices during various months before delivery is also measured.
INTRODUCTION
Commodity futures trading was introduced in Malaysia in October 1980 with two major economic
purposes. Firstly, to provide an efficient price discovery mechanism for the palm oil industry and other
agricultural commodities. Secondly, to provide a hedging mechanism against the risk of price
instability. Like any other agricultural commodities, Malaysian palm oil is subjected to significant price
fluctuations. The palm oil prices fluctuated without any clear trend or cyclical pattern in the last three
decades. The degree of price instability can be measured by using instability indices. As shown in
Table 1, palm oil prices are relatively volatile in comparison to commodities like cocoa, rubber and
petroleum at the international market. Instability indices for the Malaysian market show similar
instability in the price palm oil as compared to other commodities.
The volatility in palm oil prices is a significant risk to producers, traders, consumers and others
involved in the production and marketing of palm oil. In situations of considerable uncertainty and high
risks, price forecasts are necessary to help decision-making. Accurate price forecasts are particularly
important to facilitate efficient decision making as there is time lag intervenes between making
decisions and the actual output of the commodity in the market (Mad Nasir and Fatimah, 1991).
Hence, futures market is necessary to provide the price discovery function for the market participants
to guide them in their production, consumption and financing decisions. Besides, hedging on futures is
one of the effective risk management strategies available to reduce the associated risks that
producers and traders are exposed to. In the past, local traders have to rely on quoted prices of
1
This is a reprint of Fatimah Mohd. Arshad and Zainalabidin Mohamed (1994): "Price Discovery through Crude
Palm Oil Futures Market: An Economic Evaluation," in Erdener Kaynak and Mohamed Sulaiman in proceedings
on Third Annual Congress on Capitalising the Potentials of Globalisation - Strategies and Dynamics of Business,
Penang: International Management Development Association (IMDA) and Universiti Sains Malaysia, pp. 73-92.
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
2
soybean in distance markets in Rotterdam, London and Chicago for price reference and hedging. This
often turned out to be unsatisfactory since soybean oil prices did not reflect the true characteristics of
palm oil market. Factors determining the changes in soybean oil market are different from those
affecting palm oil. Traders’ previous reliance on foreign futures market might have led to inefficient
price transmission (due to distance factor), irrational production and stockholding decisions.
The first commodity to be traded was crude palm oil (CPO), followed by rubber RSSI in 1983, SMR20
in 1986, tin in 1987 and recently cocoa in 1988. The performance of futures contract at Kuala Lumpur.
Commodity Exchange (KLCE) is presented in Table 2. The CPO market has been growing steadily
since 1980 with the average daily turnover increasing from 122 lots in 1980 to 776 lots in 1983.
However, in March 1984, the market suffered a temporary setback due to market defaults. The market
began to recover by early 1986. Since then trading recovered progressively. The CPO market
recorded high turnovers in 1991 totalling 321,072 lots with daily average of about 540 lots or 13,500
tonnes.
Table 1: Degree of Price Instability of Selected Commodities
Market/Commodity
INTERNATIONAL a/
Soybean oil
Palm oil
Groundnut oil
Coconut oil
Cocoa
Rubber
Petroleum
MALAYSIA b/
Palm oil
Natural rubber
Cocoa
Sawn timber
Percent
deviation
from trend
Instability
Index
17.75
19.70
23.21
28.80
14.65
13.79
14.37
RMSD c/
18.23
16.04
18.45
8.99
1.92
2.24
2.26
3.12
1.46
1.42
1.16
MBI d/
2.09
1.77
2.09
0.93
Source: World Bank, as quoted in Larson, 1991 Fatimah et al., 1991
Note:
a/
b/
c/
d/
Figures based on monthly prices from January 1979 to June 1990
Figures based on annual prices from 1965 to 1989
Root Mean Squared Deviation Index of Instability
Macbean Index of Instability
The local market is relatively new compared to other established futures exchange in other parts of
the world. Hence, little research has been attempted to measure and assess its performance. One
area which is of particular importance is pricing efficiency which is defined as to degree to which a
commodity’s price reflects demand supply conditions in a market. There are at least two elements of
pricing efficiency: the degree to which a commodity’s price is determined by competitive forces and
the speed with which a commodity’s price or price quotation incorporates information about changes
in demand - supply conditions (Burns, 1983). In short, it refers to a market where there are many wellinformed traders whose action lead to market prices which reflect all information (Veeman and Taylor,
1987). If the futures market is efficient, then the futures prices should be unbiased estimate or
forecast of the cash price at contract maturity. Inaccuracies in forward pricing may cause a social
welfare loss due to misallocation of resources by producers using futures prices as expected prices in
their production decision (Kamara, 1982, and Cox, 1976).
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
3
This paper addresses the question on the forward pricing efficiency of the crude palm oil futures. The
following paragraphs present the theoretical framework of testing the necessary condition of pricing
efficiency. This is followed by discussion on the methodology, result and conclusions.
Table 2: Total turnover of futures contracts on the KLCE, 1980-94
(in lots)
Year
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
Crude Palm
Oil
5,600
32,587
56,237
191,685
50,757
822
41,303
131,303
207,185
261,871
241,984
320,199
255,870
358,176
567,898
RSS 1
SMR20
Palm
Kernel
-
-
-
1724
12499
4296
-
1020
79
2543
499
-
Tin
Cocoa
RBD
Palm
Olein
180
35 6441
15830 5772
10702 12016
3133 7721 10687
156 2376 2240
18
519
156
-
Crude
Palm
Kernel
Oil
2721
7172
208
Source: Kuala Lumpur Commodity Exchange
Note:
One lot is equal to 25 metric tonnes for crude palm oil, 10 tonnes for RSS1, SMR 20 and cocoa, 5 tonnes for tin and
15 tonnes for crude palm kernel oil. The crude palm oil contract was introduced in October 1980, RSS1 rubber in September
1983, SMR20 in March 1986, tin in October 1987, cocoa in August 1988, rbd olein in February 1990 and crude palm kernel oil
in October 1992.
FUTURES MARKET EFFICIENCY
Many studies on futures market efficiency utilised Fama’s concept developed in 1970. According to
Fama, an efficient market generates price, which at any point in time always reflect all available
information. Thus at any point in time, t, an actual price will emerge that reflects current information on
supply and demand. Simultaneously, current expectation concerning future levels of supply and
demand will be fed into the today’s futures price of a contract maturing in time t + j. According to
Tucker and Fuller (1986), the market is efficient if futures prices exhibit two separate though related
characteristics. First, futures price will follow a random walk model as suggested by Samuelson
(1965). In a perfect market, futures price will behave as a martingale model. Second, futures prices
will provide the best available estimates of the subsequent actual prices (or matured futures prices).
Whether futures prices are unbiased estimates of subsequent cash prices remain an empirical issue,
Roberts (1967) and Fama (1970) classified three different types of test concerning efficiency of
market as “weak”, “semi-strong” and “strong” form. Weak form test relies on a historical set of prices
often reduce to test on the randomness of prices. The market is said to be semi-strong if prices reflect
all publicly available information as it is released. A strong-form test examines if any particular group
in the market has monopolistic access to information as it is released. Most studies on efficiency tend
to cluster around the “semi-strong” form using linear regression.
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
4
Many studies have examined the pricing accuracy of futures market. Tomek and Gray (1970),
Leuthold (1974) and Kofi (1973) investigated the forecasting ability of futures markets within the
context of allocative efficiency. Tomek and Gray compared price relationships of two storable
commodities, corn and soybeans, with non-storable commodity, Maine potatoes. All three are
produced seasonally but while stocks of corn and soybean are held continuously from harvest to
harvest, stocks of potatoes are not. They found that corn and soybean market prices are relatively a
better forecast then potato market prices. The difference in pricing performance between these
markets indicates the significance of stock on the price spread and the influence of expectations on
the price level.
Kofi’s study provides further support to Tomek and Gray’s finding. He examined the Chicago’s futures
market for wheat and maine potatoes during 1953-69. He found that storable commodity, in this case
wheat, provides relatively reliable forecasts of cash prices at any point in time. Kofi also shows that
the longer the horizon, the worse the futures market performs as a predictor of spot prices.
Leuthold (1972) also found that futures price were efficient forecasts of spot prices for only nearmaturity dates. He also compared futures prices of live beef representing a non-storable commodity
with corn - a commodity with continuous production. Despite the clear differences between the two
with respect to storage and production, he found no significant difference in the pricing accuracy.
He showed that futures prices were efficient forecasters of spot prices for only near-maturity dates.
Stein (1981), carried the analysis a step further and placed emphasis not only the biasedness of
futures market forecasts but, in addition, on the variance of the forecast error. Stein concluded that
futures prices earlier than four months prior to delivery are useless forecasts of closing prices.
Leuthold and Hartman (1979) examined monthly average of daily futures prices of live hogs during
1971-78. Using an economic forecasting model as a performance norm, they found that the futures
market “has not at all times fully reflected available information”. Peck (1975) found that futures prices
for eggs are as accurate as several econometric models examined.
Giles and Goss (1980) studied the forward pricing functions of wool using General Instrumental
Variables Estimator (GIVE). The results support the view that lagged futures prices are unbiased
estimates of delivery date spot prices for wool with lags one to 12 months, and for live cattle with lags
from one to three months. This hypothesis is generally accepted for wool (except for lags of 3 or 12
months) and is rejected for beef. Recent works by Bigman, et al., (1983) further support the previous
findings on futures price efficiency, i.e., the market is inefficient for the more distant futures contract.
Rausser and Carter (1983) examined the forecasting accuracy of futures prices versus multivariate
and ARIMA models, for various soybean futures. They found that the multivariate and ARIMA models,
“outperform” the futures market for soybean meal but not for soybean oil both for long and short-run
horizons. Earlier, Rausser and Just (1979) examined the forecasting accuracy of futures prices
versus’ econometric models for average quarterly cash price of several agricultural commodities.
Their findings are inconclusive as to the strength of each model. They indicate striking differences
among markets, reflecting different degrees of available information. Besides, there is a sufficient
degree of information independence between the econometric forecasts and futures price.
Fatimah and Zainal (1991) have estimated the relative forecasting accuracy of futures price on spot
.compares to well proven statistical techniques like Box-Jenkins, econometric and various types of
moving averages. They have shown that futures prices outperforms the other techniques in
forecasting forward price.
In view of the mixed results and findings, it is highly probable that in the event of inaccuracies in
forward pricing it may have caused social losses in particular in the non-inventory markets. Social
losses are minimised in an inventory market where futures prices are reliable forecasts. Kamara
(1982) indicated that more research is needed to determine the magnitude of the avoidable social
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
5
losses, and more important, to examine the causes of these inefficiencies. Some of the factors that
contribute to the inefficiencies are lack of a sufficient proportion of producers and firms actively trading
in the market (suggested by Leuthold and Hartman, 1979 and Cappoza and Cornell, 1979). Other
causes of inefficiences include irrational trades, poor-quality speculation, tax effects, misinterpretation
of information, transaction costs and others. Theoretical and empirical research in this area is yet to
be explored.
METHODOLOGY
For the purpose of analysis, this paper utilises, firstly, the traditional efficient market model ( Hanson
and Hodrick, 1980, Bilson, 1981 and Birgman et al., 1983). Secondly, the recent efficient market
hypothesis by Rausser and Carter (1983) where it is contended that inefficiency implies that a model
does exist whose forecasts are more accurate than the futures market. Following are brief review of
the two models.
Traditional Efficient Market Model
Empirical tests of futures market efficiency are commonly formalised as regression tests that assume
models of the form
ST = a + bFt,T + Ut
(1)
Where ST is the spot price at delivery date, Ft,T is futures price quoted at time t for delivery date of T,
and Ut is an error term.
The hypothesis that the market is efficient is formalised in this model by the null hypothesis Ho : a = 0
and b =1, under the assumption that Ut , the random disturbances, are independent and identically
2
distributed with Ut N ( O, σ ).
Consider now a sequence of futures prices FI,T, FIt+1, T, FT-1,T quoted at consecutive trading dates for
the same delivery date. If the market is efficient than in each regression of the form
ST = ai + bi FT - I, T + UT – I
i = 1, ….., T-1
The efficiency hypothesis implies ai = 0 and bi = 1 for all i,
where I denotes the number of weeks (or days) before
accumulation of information with the passage of time
monotonically with T-i. Hence, the prices on near futures
spot prices than the more distant futures contract prices.
(2)
where UT – I are serially uncorrelated and
delivery at which futures is quoted. The
2
suggest that R value should increase
contract should better estimates delivery
Rausser and Carter Efficient Market Hypothesis
According to Rausser and Carter, market efficiency can be tested by examining the relative accuracy
of futures price forecasting ability compared to established and well proven forecasting methods. If
there exists a model that is more accurate than the futures market forecasts, the market is than
considered as inefficient. This condition however is only necessary. According to the writers,
sufficiency can be obtained by including the condition that the cost of building and utilizing the model
is less than the incremental benefits appropriately adjusted by risk (relative cost/benefit). Both
conditions are necessary and sufficient for the inefficiency property of commodity futures market. In
view of limited literature on the second condition, this paper will only focus on the first necessary
condition of inefficiency. That is, if there exist a more accurate forecasting model than the futures
market, than the market is not effectively processing information and thus is relatively inefficient.
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
6
This study compares the forecasting ability of futures prices on physical prices with four major
forecasting techniques namely univariate (Box-Jenkins), exponential smoothing, moving average and
econometric modelling. The following are brief review of the selected techniques.
Futures price on physical price
The futures prices of each contract month (quoted between one to four months ahead) are compared
to the physical prices of the contract at the maturity date. The forecasting ability of the futures price is
measured by the error of the two prices which is defined as the difference between the physical and
futures price. The underlying hypothesis that is to be tested here is futures price is an accurate
forecast of forward physical. Hence statistical error should be at its minimum (the measurement of
which is described in the later paragraphs).
Univariate Box-Jenkins model is based on the model developed by Box and Jenkins (1976). This
study utilises the model identified by Fatimah and Ghaffar (1987) for CPO price series which is in the
form of seasonal moving average which can be presented as follows:
2
6
6
12
(1 – B) – (1 – B ) t = (1 – B)(1 – B – B ) or
MA (0,1,1) (0,2,2)6
The chosen model is then fitted into the data from January 1982 to December 1990 which produces
the following estimates (with t – values in the parentheses)
1
θ = - 0.29067 (- 2.848)
2
θ = 1.2240 (12.959)
12
θ = - 0.44687 (- 4.7455)
Fatimah and Ghaffar (1987) have shown that the identify model is highly efficient in short term
forecasting in particular up to one to three months ahead. Forecasting studies by cooper (1973),
Bourke (1979) and Brandt and Bessler (1981) indicate the superiority of the Box-Jenkins univariate
model over econometric model despite being criticized as devoid of economic theory. The proven
superiority of this method would provide a suitable basis of comparison on the ability of futures market
to discover accurate forward prices. If the univariate approach could generate better forecast than the
consensus of the futures market participants, it can be deduced that some elements of inefficiencies
might have led to irrational decision.
Exponential smoothing method utilised here are: Holt Winter’s Additive Seasonal and Holt Winter’s
Multiplicative Seasonal approaches (Winters, 1960 and Chatfield, 1978). These models are chosen as
they may easily be generalised to deal with time series containing trend and seasonal variation.
The Holt Winter’s Method is based on three smoothing equation for this method is as follows:
St = αXt + (1 - α) (S t-1 + bt-1) / I t-L
Where X is the series, I is seasonal adjustment factor, L is the length of seasonality and b is trend
component. The ex-post forecast equation for m period ahead (test period) is as follows:
F
t+m
= (S t + bt M)
I t-L+m
Moving average method is a simple forecasting technique to determine the influence of past data on
the mean (as a forecast) and to ascertain the number of past observations will be included in a mean
(Markridakis, 1983). Moving average is used to describe this procedure as each new observation
becomes available, a new average is computed by discarding the oldest observation and including the
latest one. Ale moving average is then used to forecast for the next period. This study has computed
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
7
two to six months moving averages (MA) both for physical and futures prices. The forecasts are then
compared to actual cash and futures prices.
Econometric model is based on the model developed by Mad Nasir et al. (1993) which is
represented by the following equation:
PPO t = (SPO t , TC t , PPO t-1 , PPO t-2 , DUM)
where PPO t is the price of crude palm oil, at time t SPO t is the ending stock of palm oil, TC t is the
total consumption of palm oil and DUM is a dummy variable of price fluctuation over the period to
capture the seasonally.
Econometric model for the estimated equation of the price of crude palm oil is as follows:
PPO
t
=
242.316
- 0.18772 SPO
(-2.709)
(1.572)
(5.672)
(7.321)
(-2.266)
- 167.517 FEB
- 178. I81 MAR
- 81.618 APR
- 118.667 MAY
- 179.861 JUN
(-2.637)
(-2.432)
(-1.297)
(-2.341)
(-3.023)
- 213.474 JUL
- 145.160 AUG
- 101.747 SEP
- 102.595 OCT
- 81.850 NOV
(-3.233)
(-2.182)
(-1.885)
(-1.783)
(-1.161)
t
+ 0.21615 TC
t
+ 1.255 PPO
t-1
- 0.360 PPO
t-2
- 94.995 DEC
(-1.598)
2
R = 0.9015
D.W. = 2.0746
SER = 103.928
2
The estimated equation seems to fit the data well, as evident by the R and T-values which are
0.9015 and 2.0746 respectively. All estimated coefficients have the expected signs, except for the
estimation for consumption variable where it is insignificant. The model postulates that CPO prices
are significantly related to the three variables considered, i.e.., stock levels, total consumption and
lagged price. In particular, the model shows that price are highly sensitive to changes in stock level
where the short-run price flexibilities to stock levels was estimated to be -1.41. Some forecasting
studies (Lcuthold et al., 1970) and Gellatly (1979) indicate that econometric method provides superior
prediction compared to other techniques. Unlike univariate forecasting, econometric model takes into
account other major macro-economic variables into its system. It renders the method as more realistic
as it is able to capture the dynamics of the structural changes in the market.
The constructed models or techniques are then examined on the basis of whether each significantly
“outperforms” the forecasting ability of the futures price. Performance of the model is measured by the
validity of its estimate on the basis of its forecasting power. ‘Re results are evaluated using indicators
like the Root Mean Square Simulation Terror (RMSE), Root Mean Square Percentage Error (RMSPE)
and Theil’s inequality coefficient (U-statistic).
The RMSE for the variable A, given by
RMSE = [ 1/T Σ
T
t=1
2 1/2
(P t – A t ) ]
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
8
where T is number of periods in the simulation, P is the predicted value and A is the actual value. It
measures the deviation of the predicted value from its actual time path. The RMPSE is defined as;
RMPSE = [ 1/T Σ
T
t=1
2 1/2
(P t – A t /A t ) ]
The Theil’s inequality coefficient (U-statistic) is defined as follows:
1
2
U = 1 Σ t=1 (P t – A t )
T
------------------------1
2
1 Σ t=1 (P t ) + 1 Σ
T
T
T
t=1
2
( At )
In the case of perfect fit, the Theil’s coefficient takes the value of zero. The value of one indicates the
prediction technique is no better than. a naive no change model i.e.
P t = At
or
Pt=1 = A
t
Data
This study utilised monthly prices of CPO both for physical and futures deliveries published by PORLA
(Palm 0il Registration and Licensing Authority) and KLCE respectively. The data for CPO futures and
spot prices were collected for the period of 1983 until 1992 (except for Box Jenkins and econometric
analysis which utilised different time framework as indicated earlier).
RESULTS
Traditional Efficient Market Model
The traditional efficient market model given in equation (1) tests the hypothesis that futures prices
reflecting the subjective through rational expectation of traders, are unbiased estimates of futures spot
prices. This study compares the “price” performance of various months of CPO futures contract one to
1
five months before delivery. The results for January and May contracts are presented in Tables 3 and
4 and the rest of the future contracts are presented in Appendix 1. The results for the two contracts
confirm that the shorter the future contract i.e., the shorter is time distance between the quoting date
2
2
of the futures price and delivery date - the higher the value of R , as expected. For instance, the R
value one month before delivery is 0.94 for January contract (0.93 for May). This value reduces to
0.71 at five months before delivery for January contract (0.61 for May). In the case of January, the
2
2
value of R reduces sharply on the quoting date of 4 months before delivery. The value of R reduces
sharply on the second month for May contracts.
The slope coefficients are significantly different from unity for all the months before delivery for the 2
contracts as expected. The standard error increases as the time distance is further from delivery date.
The Durbin Watson (DW) statistics show no serial correlation for all the months before delivery, while
F statistics (for testing the simultaneous hypothesis H0 : a = 0 and b = 1) is significant at 5%
1
January and May contracts were chosen for discussion as January appears to be the most active month (total
turnover for January contract between 1983 – 92 was 167 113) while the month of May represent the least active
month ( a total of 107 430 lots were traded for May futures contracts).
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
9
significant level. The same pattern of results (with minor variations) are observed for other contract
months (Appendix I).
Table 3 : Regression Results between Spot and Future Prices for Crude Palm Oil
(January) Futures Contract
2
Months
Before
Delivery
Constant
Slope
R
S.E
D.W
F Stat
1
-38.66
1.06
(8.60)
0.94
89.15
2.03
62.42
2
-241.09
1.34
(8.77)
0.89
110.37
2.03
77.03
3
-404.53
1.50
(4.24)
0.89
150.76
2.08
13.52
4
-391.46
1.45
(6.12)
0.85
145.79
1.83
21.10
5
-478.24
1.60
(3.54)
0.71
220.20
2.04
7.68
Table 4 : Regression Results between Spot and Futures Prices for Crude Palm Oil
(May) Futures Contract
Months
before
Delivery i
2
Constant
Slope
R
S.E.
D.W.
F Stat.
1
-403.32
1.53
(8.87)
0.93
145.49
1.69
23.55
2
600.30
0.36
(3.20)
0.85
114.38
1.85
204.97
3
-698.68
1.93
(3.40)
0.73
291.57
1.99
4.61
4
-478.48
1.75
(3.20)
0.68
272.64
2.24
7.67
5
-284.78
1.56
(3.75)
0.61
268.73
2.10
14.09
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 5 : Comparison of Forecasts Generated by Selected Forecasting Techniques
Technique
Predictive Statistic Technique
RMSE
1.
2.
RMPE
U-Stat
Futures prices on spot price
Ex-post ( test period )
( 1 month ahead )
( 2 month ahead )
( 3 month ahead )
( 4 month ahead )
Moving Average ( MA ) on spot price
109.83
184.76
213.56
223.86
9.94
16.97
20.22
21.62
0.25
0.87
1.43
1.67
MA2
MA3
MA4
MA5
MA6
Moving Average ( MA ) on futures price
( quotes 1 month ahead )
64.39
104.10
127.13
144.74
164.06
5.79
9.60
12.15
14.16
16.27
0.50
0.80
0.99
1.15
1.31
142.19
159.08
170.07
184.71
202.14
13.06
15.08
16.51
18.16
19.98
1.11
1.26
1.37
1.49
1.62
MA2
MA3
MA4
MA5
MA6
( quoted 3 months ahead )
194.20
198.42
206.76
220.09
233.73
18.25
19.04
20.09
21.46
22.89
1.54
1.59
1.67
1.77
1.88
MA2
MA3
MA4
MA5
MA6
( quoted 4 months ahead )
214.54
219.43
230.15
242.72
254.39
20.60
21.32
22.39
23.69
25.06
1.74
1.79
1.87
1.97
2.07
229.87
240.71
253.06
264.66
275.32
22.44
23.48
24.75
26.12
27.52
1.90
1.98
2.07
2.17
2.26
109.55
108.00
10.65
10.14
0.94
0.91
114.80
110.99
110.00
10.72
10.12
10.82
0.93
0.89
0.89
MA2
MA3
MA4
MA5
MA6
( quoted 2 months ahead )
3.
(a)
(b)
(a)
(b)
4.
MA2
MA3
MA4
MA5
MA6
Smoothing method fitted value on futures
price
Holt’s Winter Addictive Seasonal
Holt’s Winter Multiplicative Seasonal
Ex-post on spot price
Holt’s Winter Addictive Seasonal
Holt’s Winter Multiplicative Seasonal
Box-Jenkins on spot price Ex-post
10
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
11
Rausser and Carter Efficient Market Model
Table 2 provides the summary of predictive statistics for forecasts by the selected techniques. The
best forecasting model would be the one that produces the lowest, RMPSE and U-statistic. Based on
these criteria, it was found that futures price quoted one month advance provides a relatively accurate
forecast of spot prices with RMSE of 109.83, RMPSE 9.94 and U-Statistic of 0.25. The next efficient
model is moving average (of two months) for spot price where the evaluative statistic are 64.39
(RMSE), 5.79 (RMPSE) and 0.5 (U-Statistics). This is followed by futures price (2 months ahead),
Moving average of 3 months for spot price and econometric method which yielded RMSE, RMPSE
and U-Statistics that are almost within the same range. Box-Jenkins and the smoothing techniques
provide less impressive forecasts with U-statistics ranging between 0.91 – 0.94. Moving average for
futures price fail to provide good results as the predictive statistics are relatively large (in particular the
U-statistics are more than 1).
Based on this finding, it is clear that futures price outperforms the other techniques in forecasting
forward price. The nearer the maturity date its forecasting ability improves significantly. For instance,
two months before maturity date, the RMPSE value is 16.97 compares 9.94 for one month in
advance. Despite the superiority acclaimed by Box-Jenkins and econometric methods, their predictive
performance is fairly reasonable.
CONCLUSION
Based on the results of the above analysis, it can be safely concluded that futures market does not
exhibit strong evidence of inefficiency. It appears that futures market is able to establish forward
prices efficiently particularly as the expiry date approaches. This means that the market utilizes and
processes information efficiently, hence the price discovered at any point in time, can be taken as
reflecting the current supply and demand as well as available information.
REFERENCE
Bigman, D., D. Goldfarb and E. Schechtman (1983), “ Futures Market Efficiency and the Time Content
of Information Sets”, Journal of Futures Market, 3(3):321 – 34.
Bilson J. E. O. (1981). “ The Speculative Efficiency Hypothesis”, Journal of Business, 54:435 – 51.
Bourke, I.J. (1970). “Comparing the Box – Jenkins and Econometric Techniques of Forecasting Beef
Process,” Review of Marketing and Agricultural Economics, Vol.47.
Box G.E.O. and G.M. Jenkins (1976), Time Series Analysis, San Francisco:Holden Day.
Brandt J.A. and D.A. Bessler (1981), “Composite Forecasting: An Application with the US Hog
Prices”, American Journal of Agricultural Economics, 63.
Burns S.M. (1983), “Futures Market and Marketing Efficiency” in Streit M.E. “Futures Markets:
Modelling, Managing and Monitoring Futures Trading”, London: Basil Blackwell.
Cappoza, D.R. and B. Cornell (1979) “Treasury Bill Pricing in the Spot and Futures Markets”, Review
of Economics and Statistics, 72: 513-20.
Chatfield, C. (1978), The Holt-Winters Forecasting Procedure, Applied Statistics, 27:264-79.
Cooper R. L. (1972). “The Predictive Performance of Quarterly Econometric Model of the US in
Hickman (Ed.). Econometric Models of Cyclical Behaviour, New York: Columbia University
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
12
Press.
Cox, C.C., )1976), “Futures Trading and Market Information”,Journal of Political Economy, 84 (Dec.):
1215 – 37.
Fama, F. (1970), “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of
Finance, 25 May: 383-417.
Fatimah Mohd Arshad and Zainalabidin Mohamed (1991), “The Efficiency of the Crude Palm Oil
Futures Market in Establishing Forward Prices”, Journal of Malaysian Agricultural Economics,
8:25-39.
Fatimah Mohd Arshad, Mohd Shahwahid Hj Othman, Mad Nasir Shamsudin and Zulkifli Senteri
(1991), “ Marketing of Agricultural Primary Commodities – The Need for a New Approach”,
paper presented at the National Seminar on Agricultural Primary Commodities: An Agenda for
Structural Adjustments, organised by MIER, UKM and CAPS (UPM), 3-4 June, Kuala Lumpur.
Fatimah Mohd Arshad and Roslan Abd Ghaffar (1986), “Crude Palm Oil Price Forecasting: Box
Jenkins Approach”, PERTANIKA 9(3): 359-67
Gellatly, C. (1972), ‘Forecasting N.S.W. Beef Production: An Evaluation of Alternative Technique,”
Review Marketing and Agricultural Economic, 47.
Giles, D.E.A. and B.A. Goss (1980), “The Predictive Quality of Futures Prices with an Application to
the Sydney Wool Futures Markets,” Australian Economic Papers 9(35): 291-300.
Hansen, L.P., and Hodrick, R.J. (1980), “Forward Exchange Rates as Optimal Prediction of Future
Spot Rate: An Econometric Analysis,” Journal of Political Economy, 88:829-53.
Kamara, A. (1 982), “Issues in Futures Market. A Survey, “ Journal of Futures Market, 2(3): 261-94.
Kofi, T.A. (1973), “A Framework for Comparing the Efficiency of Futures Markets,” American Journal
of Agricultural Economics, 55(4): 584-94.
Larson D.F. (1991), “A World Bank View on the Price Prospects for Palm Oil,” paper presented at the
PORIM International Palm Oil Conference, PORIM, 9-14 September, 1991, Kuala Lumpur.
Leuthold, R.M. (1972), “Random Walk and Price Trends: Live Cattle Futures Market,” Journal of
Finance, 27: 879-89.
Leuthold, R.M. (1974), “Re Prices Performance on the Live Beef Cattle”, American Journal of
Agricultural Economics, 56(2): 313-324.
Lcuthold, R.M. and P.A. Hartmann (1979), “A Semi-Strong From Evaluation of the Efficiency of the
Hog Futures Market, “ American Journal of Agricultural Economics, 6(3): 482-489.
Mad Nasir Shamsudin, Zainalabidin Mohamed. and Fatimah Mohd. Arshad (1989), “Selected Factors
Affecting Crude Palm 0il Prices, “ Malaysian Journal of Agricultural Economics, Vol. 5, Dec:
20-29.
Mad Nasir Shamsudin and Fatimah Mohd. Arshad (1993): Malaysian Palm 0il Market Model in
Fatimah Mobd. Arshad (eds) Malaysian Agricultural Commodity Forecasting and Policy
Modelling. Centre for Agricultural Policy Studies, UPM.
Makridakis, S. (1983), Forecasting: Methods and Application, New York: John Wiley & Sons, Inc.
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
13
Peck, A.E. (1975), “Hedging and Income Stability: Concept, Implications and an Example,” as
reprinted in Peck A.E., Ed., Selected Writing on Futures Markets, Vol. 2, Chicago: Chicago
Board of Trade.
Roberts, H.V. (1967), “Statistical versus Clinical Prediction of Stock Market”, unpublished paper
presented to the Seminar on the Analysis of Security Prices, Chicago: University of Chicago
Press.
Rausser, C.G. and R. Just. (1979), “Agricultural Commodity Price Forecasting Accuracy: Futures
Market Versus Econometric Models,” In International Futures Trading Seminar, Vol. 6.
Chicago: Chicago Board of Trade.
Rausser, C.G. and C. Carter (1983), “Futures Market Efficiency in the Soybean Complex,” Review of
Economic and Statistics, 65 (August): 469-478.
Samuelson, P.A. (1969), “Proof Properly Anticipated Prices Fluctuate Randomly,” Industrial
Management Review, 6: 41-49.
APPENDIX
Table 1: Regression Results between Spot and Futures Prices for
Crude Palm Oil (February) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
66.60
0.91 (10.18)
0.96
90.83
1.91
84.65
2
-627.17
1.75 (6.66)
0.90
167.14
1.94
15.95
3
-446.91
1.66
(4.18)
0.66
234.38
1.98
17.48
4
-262.53
1.52
(2.87)
0.62
299.53
1.79
5.09
5
-568.77
1.75
(1.86)
0.62
279.66
2.03
5.80
Note: Figures in parantheses are t-values.
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 2: Regression Results between Spot and Futures Prices for Crude
Palm Oil (March) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
287.67
0.69
(1.66)
0.97
46.15
1.97
22.93
2
14.06
0.973
(13.08)
0.95
73.35
1.94
80.75
3
-9.71
0.96
(8.45)
0.90
110.11
1.83
33.88
4
-329.69
1.41
(5.00)
0.86
150.96
1.60
10.92
5
-516.33
1.51
(5.47)
0.81
156.52
2.15
15.00
Note: Figures in parantheses are t-values.
Table 3: Regression Results between Spot and Futures Prices for Crude
Palm Oil (April) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
-111.35
1.11
(15.08)
0.97
67.63
1.92
58.24
2
-600.02
1.66
(17.07)
0.95
86.76
1.79
34.74.
95
3
459.56
0.44
(1.50)
0.90
74.50
2.11
4.82
4
-220.64
1.28
(8.20)
0.90
108.66
1.90
32.44
5
-212.37
1.21
(6.34)
0.83
141.75
1.97
17.61
Note: Figures in parantheses are t-values.
14
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 4: Regression Results between Spot and Futures Prices for Crude
Palm Oil (June) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
112.78
0.88
(6.50)
0.89
107.23
2.10
24.85
2
-575.06
1.72
(6.12)
0.92
110.23
2.03
15.83
3
-371.85
1.40
(10.36)
0.96
68.81
2.14
63.59
4
-105.28
1.18
(4.20)
0.81
141.23
1.93
13.05
5
181.49
0.78
(3.75)
0.93
74.06
2.00
108.83
Note: Figures in parantheses are t-values.
Table 5: Regression Results between Spot and Futures Prices for Crude
Palm Oil (July) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
456.17
0.41
(2.76)
0.55
150.25
2.20
3.71
2
227.61
0.72
(2.19)
0.54
151.58
2.19
3.60
3
336.89
0.57
(2.58)
0.56
148.43
2.08
3.38
4
-559.47
1.66
(1.64)
0.89
152.90
1.90
2.06
5
290.38
0.60
(5.00)
0.80
100.62
1.93
11.98
Note: Figures in parantheses are t-values.
15
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 6: Regression Results between Spot and Futures Prices for Crude
Palm Oil (August) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
60.64
0.90
(4.79)
0.74
102.29
1.79
22.94
2
274.23
0.56
(1.83)
0.73
126.46
1.69
3.72
3
504.64
0.30
(0.92)
0.60
197.38
2.17
0.76
4
457.57
0.391
(1.41)
0.20
179.84
2.06
2.01
5
337.02
0.50
(1.18)
0.36
174.94
2.17
1.46
Note: Figures in parantheses are t-values.
Table 7: Regression Results between Spot and Futures Prices for Crude
Palm Oil (September) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
-77.09
1.12
(8.50)
0.90
174.13
1.92
72.28
2
227.10
0.60
(2.88)
0.96
48.39
2.10
15.00
3
424.64
0.43
(1.50)
0.22
207.20
1.98
2.27
4
496.29
0.29
(0.87)
0.31
170.32.
90
1.89
1.14
5
459.97
0.39
(1.38)
0.19
210.84
2.03
1.92
Note: Figures in parantheses are t-values.
16
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 8: Regression Results between Spot and Futures Prices for Crude
Palm Oil (October) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
259.82
0.70
(2.39)
0.96
45.18
1.90
13.06
2
388.21
0.53
(2.03)
0.95
47.68
1.73
11.67
3
517.96
0.42
(0.49)
0.49
149.18
2.01
2.44
4
245.91
0.73
(1.86)
0.30
210.47
1.92
3.47
5
397.80
0.46
(2.02)
0.95
50.57
2.00
26.25
Note: Figures in parantheses are t-values.
Table 9: Regression Results between Spot and Futures Prices for Crude
Palm Oil (November) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
48.22
0.98
(13.69)
0.96
52.54
1.97
73.41
2
368.46
0.60
(1.10)
0.86
75.84
1.64
3.16
3
-27.10
1.14
(3.82)
0.61
152.37
1.53
2.14
4
367.35
0.63
(1.54)
0.23
210.98
2.04
2.39
5
519.11
0.43
(1.23)
0.17
222.42
1.21
1.53
Note: Figures in parantheses are t-values.
17
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
Table 10: Regression Results between Spot and Futures Prices for Crude
Palm Oil (December) Futures Contract
Months before
delivery
Constant
Slope
R2
S.E.
D.W.
F Stat
1
-36.99
1.06
(5.34)
0.86
90.68
1.94
8.50
2
11.61
1.05
(4.79)
0.82
121.99
2.06
14.04
3
116.28
(5.17)
0.77
123.02
1.98
26.72
4
86.90
1.03
(3.54)
0.61
159.86
1.98
12.56
5
538.15
0.38
(0.67)
0.52
135.66
2.11
1.12
Note: Figures in parantheses are t-values.
18
Fatimah and Zainal: Price Discovery through Crude Palm Oil Futures…
19