Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Cocoa Bean Prices Forecasting Using GARCH Model for Long
Term Strategic Production and Hedging Strategies
Adrianus Agassi1 and Ana Noveria2
As the international cocoa bean price is liable to change rapidly and
unpredictably, it’s represented a price risk for the producer. Therefore,
understand pattern movement of international cocoa bean prices in
foreseeable future is necessary for them. This paper aims to find out the
approximate pattern of the international cocoa bean prices for the period
2014 to 2024 using historical data in form of monthly average of International
Cocoa Organization (ICCO) daily prices from June 1994 to June 2014.
Furthermore, by discovering the price movement in the next ten years, this
paper also aims to give recommendation on the production level and
hedging strategy for Indonesian producer in dealing with the cocoa bean
prices movement. This paper used GARCH (1,2) model for forecasting. The
forecast pattern of international cocoa bean prices for the next ten years
shows low volatility in the beginning and tends to rise gradually as the
forecast-horizon increases. Based on this forecast result, cocoa bean
producer will face a low price risk in the short term but a high risk in the long
term periods. They should maximize their production and sell their cocoa
when the prices are estimated to increase. When the prices are estimated to
decline, cocoa bean producer should have a forward contract to lock the
prices in order to mitigate the reduction of their profit.
Keywords: cocoa bean prices, GARCH forecasting, hedging, production strategy
Field of Research: Finance
1. Introduction
Over several years, cocoa bean is one of many commodities which are consumed
worldwide. Cocoa bean has a wide variety of uses and become raw ingredients for many
food and beverage products. One of the most popular products made from cocoa bean is
chocolate. As chocolate become popular and people around the world enjoy chocolate in
different form, the global cocoa market also expanding. Most of the global cocoa bean
production comes from Africa continent. Along the cocoa bean history, most of cocoa
beans have been exported to Europe and USA for grinding processing.
Indonesia is the world‟s third largest cocoa producer after Ivory Coast and Ghana,
representing around 15% of the global production of cocoa beans. Therefore, cocoa bean
is considered as one of the most important agricultural export products of Indonesia.
Additionally, Indonesia‟s Ministry of Trade has included cocoa bean as a one of ten main
commodities export products together with textile, electronic, rubber, palm oil, forest
products, footwear, automotive, shrimps, and coffee.
_________________________________________________________________
1
Adrianus Agassi, School of Business and Management, Institut Teknologi Bandung, Indonesia
Email: adrianus.agassi@sbm-itb.ac.id
2
Ana Noveria, SE, MBA, School of Business and Management, Institut Teknologi Bandung, Indonesia
Email: ana.noveria@sbm-itb.ac.id
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Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
As developing country that become world‟s third largest cocoa producer and depend on
cocoa as the one of the most important agricultural export product, having a better
understanding about cocoa bean prices characteristics is extremely important. Indonesian
cocoa stakeholder should pay attention to international cocoa price, especially the
producer. The average of international cocoa bean price usually becomes a reference for
Indonesian government to determine the cocoa bean standard price. As the international
cocoa price liable to change rapidly and unpredictably, it is represented a price risk for the
cocoa stakeholder. Therefore, the good estimation of future cocoa price becomes
something important for them. Thus, this research will use time series data and GARCH
model to make a forecast of international cocoa bean price in the next ten years.
Furthermore, this research also aims to give recommendation on production level and
hedging strategies for Indonesian producer in dealing with the price risk.
2. Literature Review
2.1 Cocoa Bean
The cocoa bean is derived from Theobroma cacao which is dried and fully fermented
(Mehta, 2013). Cocoa bean is the basis of chocolate which are many people consume
nowadays. A cocoa fruit has a rough rind about 3 cm thick. It is varies with the origin and
variant of the fruit. Cocoa fruits are attached to the trunk and branches varying in length
from 15-25 cm attaching the 40-50 beans in a sticky pulp which the color is white. The
beans which are usually white become violet or reddish brown during the drying process
(Mehta, 2013).
2.1.1 Cocoa Bean Trading
According to ICCO (2012), cocoa bean is trading on two different types of market, which
are actual or physical market and the futures or terminal market. Nearly all cocoa coming
from producer is sold through physical market. The physical market implicates the type of
business that most people normally think of when talking about trading in commodities. In
the futures or terminal market, cocoa bean trades on two world exchanges: London (LIFFE
– Pound) and New York (ICE – USD) (World Cocoa Foundation, 2012). These two
exchanges provide the facility and trading platform that bring buyers and sellers together.
2.1.3 Factor Affecting Cocoa Bean Prices
Cocoa price are volatile and influenced by many factors. Sweeney (1971) defines that the
crop estimate of the world production cocoa is paramount in the price movement of cocoa.
In other hands, Shamsudin et al. (1992) defines that the world cocoa price is determined
by the consumption and stock levels. World Cocoa Foundation (2012) also defines various
factors that affect the cocoa bean prices such as including stock/grind ratios, expectations
for future production/demand, global food prices, and consolidation/fragmentation in cocoa
trade and processing industries.
2.1.4 Factor Affecting Cocoa Bean Production
According to Dand (1993), there are some factors that affect the production of cocoa, such
as: return on investment; government schemes; alternative crops; pest, diseases, drought,
and floods; yield; tree-stock characteristic; environmental influences; and costs.
2.2 Risk
Risk can be defined in a variety of ways depend on the context. Jones (2005) defined risk
as “the probable frequency and probable magnitude of future loss”. Hanson (2011) defined
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Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
risk in two ways: non-technical and technical context. In non-technical contexts, risk refers
to situations in which it is possible undesirable event will happen. While in technical
context, risk has various meanings because widely used across discipline.
2.2.1 Risk in Cocoa Industry
According to module from The World Bank's Cocoa Price Risk Management Program
(2014), there are some key risks that cocoa industry faces, such as: climate risk;
counterparty risk; quality risk; pest and disease risk; currency risk, price risk; physical risk;
and regulatory risk.
2.2.1.2 Price Risk in Cocoa Industry
Price risk is defined as the risk that incomes decrease as a result of change in the price of
cocoa or as an outcome of volatility of cocoa market price. According to module from The
World Bank‟s Cocoa Price Risk Management Program, price risk in cocoa trading is
influenced by three factors such as traders fixing sales or purchase prices; time; and
volume. Producer‟s profit margin can be reduced because of unforeseen changes in cocoa
prices. However, producers can protect themselves from fluctuations in cocoa prices by
applying some financial strategies which will ensure a cocoa price or lock in a worst case
scenario price. Two financial instruments mostly used to hedge against commodity prices
risk are futures and options (Investopedia.com, 2014).
2.3 Time Series Data
Gujarati (2004) defines time series data as a set of observations on the values that a
variable takes at different times. Time series data can be collected at regular time intervals
such as daily, weekly, monthly, quarterly, and annually. A key feature of time series data is
most economic and other time series are related, often strongly related, to their recent
histories (Wooldridge, 2009).
2.4 ARCH and GARCH Model
There are some models that can be used to measure volatility in time series, such as
autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive
conditional heteroscedasticity (GARCH) models, which are the most common use.
ARCH model were introduced by Engle (1982). ARCH model is useful because does not
assume the variance is constant and describes how the variance of the error evolves
(Brooks, 2008). Under the ARCH model the autocorrelation in volatility is modelled by
allowing the conditional variance of the error term,
, to depend on the immediately
previous value of the squared error. One example of a full model would be
(1)
(2)
The ARCH model could be extended to the general case where the error variance
depends on q lags of squared errors which known as ARCH (q) model.
As ARCH model have some problem which are the high ARCH order models were needed
to really fit with dynamic process of the conditional variance and it were pretty hard to
quantify, Bollerslev (1986) succeed to answer that problem by proposed GARCH model.
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Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
GARCH model defines that the conditional variance of u at time t depends not only on the
squared error term in the previous time period but also on its conditional variance in the
previous time period (Gujarati, 2004). The simplest GARCH model can be written as:
(3)
Above model is a GARCH (1,1) model.
is the conditional variance since it is one period
ahead estimate for the variance calculated based on past data. This model can be
extended to a GARCH (p, q) model in which there are p lagged terms of the squared error
term and q terms of the lagged conditional variances. However, in general a GARCH (1,1)
model will be enough to capture the volatility clustering in the data.
2.5 Forecasting
GARCH models are useful to forecast volatility. GARCH is a model to describe movement
in the conditional variance of an error term, , which not appear particularly useful.
However, it is possible to show that the conditional variance of y, given its previous value,
is the same as the conditional variance of u, given its previous value.
(
|
,
As described in the equation above, modelling
variance of as well (Brooks, 2008).
(
|
,…)
(4)
will give models and forecasts for the
2.6 Previous Study
There are some previous studies that related to this research. Sumaryanto (2009) analyze
the retail price volatility in some food commodities using ARCH/GARCH model in the last
two years. From the overall estimation results, it appeared that the most appropriate model
for rice, red chili, and shallot was ARCH (1); while for sugar and wheat flour was GARCH
(1,1). Assis, Amran, and Remali (2010) use univariate time series model to forecast cocoa
bean prices. They compared four different types of univariate time series methods, namely
exponential smoothing, ARIMA, GARCH, and the mixed ARCH/GARCH models in
forecasting Bagan Datoh cocoa bean prices. They found that GARCH (1,1) model
outperformed the exponential smoothing, ARIMA, and the mixed ARCH/GARCH models
for forecasting Bagan Datoh cocoa bean prices.
3. Methodology
3.1 Data Collection
Data used in this research are secondary quantitative data. The data of this research are
obtained from World Bank, especially Global Economic Monitor (GEM) Commodities. The
cocoa bean prices data are in form of monthly average of ICCO daily prices from June
1994 to June 2014.
3.2 Forecasting Procedures
To achieve the objective of this research, there are several steps to forecast international
cocoa bean prices such as testing for ARCH effects, estimating GARCH (p,q) models,
selecting the best model, checking the model, getting a regression result, and forecast the
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Proceedings of 7th Asia-Pacific Business Research Conference
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cocoa bean prices for the next ten years. Figure 3.2 show the forecasting procedures.
Figure 3.2 Forecasting Procedures
Testing for
ARCH Effect
Estimating
GARCH (p,q)
Models
Model
Selection
Model
Checking
Regression
Result:
Conditional
Variance
Forecasting:
next ten years
Before estimating a GARCH model, it is sensible first to do the Engle (1982) test for ARCH
effects to make sure that there is heteroscedasticity in the residuals. The probability value
of F-statistic should lower than 0.05 to conclude that the residuals containing ARCH effect.
After testing the heteroscedasticity in the residuals, the estimating of GARCH (p,q) models
begins. EViews software is used to perform trial and error to determine best fitting model.
The best appropriate GARCH model is selected based on some selection criteria. First
criteria is the all coefficient in the GARCH (p,q) model must be significant, except the
constant term. All the probability value of each coefficient should higher than 0.05. Second
criteria are Akaike Information Criterion (AIC) and Schwarz Criterion. The lower the values
of these criteria, the better model fit the data. However, Burnham and Anderson (2002,
2004) define convincing arguments in favor of AIC over SIC. The selection of the model
also can be seen from the standard error of the regression. The lower the values of these
criteria, the better model fit the data.
Then, the best fitting model is examined on its standardized residual. The residuals must
follow a white noise process which it is independently and identically distributed as a
normal distribution with zero mean and constant variance (Gujarati, 2004). Additionally, it
should be serially uncorrelated too. The conditional variance which is the main output of
GARCH model, will transform to standard deviation as a basis to forecast international
cocoa bean prices movement in the next ten years. Forecasting will use the historical
international cocoa bean price ± standard deviation as the upper and lower forecast
international cocoa bean prices for the next ten years.
4. The Findings
4.1 Data Description
The figure 4.1 below shows the monthly average ICCO cocoa bean prices from June 1994
to June 2014. The data used comprise about 241 observations of monthly average ICCO
cocoa bean prices. The maximum value of this data is 3552.1 USD per metric ton which
happened in January 2010. The minimum value of this data is 860.74 USD per metric ton
which happened in February 2000. Hence, it‟s giving a data range of 2661.36. The
average value of this data is 1904.51 USD per metric ton. As we can see from figure 4.1,
the cocoa bean has common financial characteristics which are has volatility. The data has
the standard deviation of 678.12 which indicates the data has quite high level of volatility.
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Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Figure 4.1 International Cocoa Bean Prices during June 1994 to June 2014
International Cocoa Bean Prices
$4,000.00
$3,500.00
$3,000.00
$2,500.00
$2,000.00
$1,500.00
$1,000.00
$500.00
Jun-94
Feb-95
Oct-95
Jun-96
Feb-97
Oct-97
Jun-98
Feb-99
Oct-99
Jun-00
Feb-01
Oct-01
Jun-02
Feb-03
Oct-03
Jun-04
Feb-05
Oct-05
Jun-06
Feb-07
Oct-07
Jun-08
Feb-09
Oct-09
Jun-10
Feb-11
Oct-11
Jun-12
Feb-13
Oct-13
Jun-14
$0.00
Source: World Bank, especially GEM Commodities
4.2 Forecasting Procedures
4.2.1 ARCH Effect Test
Before estimating a GARCH model, it is sensible first to do the Engle (1982) test for ARCH
effects to test the presence of heteroscedasticity in the data. Table 4.1 below shows the
result of the ARCH effect test.
Table 4.1 ARCH Test
F-statistic
5.033097
Prob. F(1,237)
0.0258
Obs*R-squared
4.970024
Prob. Chi-Square(1)
0.0258
According to table 4.1, the probability of F-statistic is 0.0258, which are below 5% level of
significance. This result is significant which indicates there is a presence of ARCH effects
or heteroscedasticity in the data.
4.2.2 Estimating GARCH (p,q) Models and Model Selection
Four GARCH (p,q) models were selected and compared, namely GARCH(1,1),
GARCH(1,2), GARCH(2,1), and GARCH(2,2). Using the model selection criteria the
GARCH(1,2) model has been selected as the best model among the other three GARCH
models. Table 4.2 below shows the estimation output of the four GARCH models, while
table 4.3 shows the comparison of two best fit models which are GARCH (1,1) and
GARCH (1,2) model.
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Table 4.2 GARCH (p,q) Models
α0
α1
GARCH (1,1)
392.305
(0.1345)
0.188974
(0.0093)*
0.794507
(0.0000)*
GARCH (1,2)
271.6421
(0.0102)*
0.100755
(0.0025)*
1.535673
(0.0000)*
GARCH (2,1)
7342.991
(0.0000)*
0.163180
(0.1290)
0.398882
(0.0000)*
-0.102369
(0.2897)
GARCH (2,2)
756.2847
(0.1249)
0.071810
(0.3392)
0.218057
(0.0023)*
-0.054283
(0.5305)
GARCH (p,q) Models
α2
β1
β2
-0.648954
(0.0000)*
0.712345
(0.0000)*
Note: * = significant at 5%; value in parenthesis is p-value
Table 4.3 Comparison of GARCH (1,1) and GARCH (1,2) Model
Model
Akaike Info Criterion (AIC)
Schwarz Criterion
S.E. of regression
Log Likelihood
GARCH (1,1)
12.10102
12.17354
114.0303
-1447.123
GARCH (1,2)
12.09863
12.18565
113.9445
-1445.836
According to table 4.2, the GARCH (1,1) and GARCH (1,2) are the two best fit models
because all the coefficients is significant, except the constant term. By comparing some
selection criteria in table 4.3, GARCH (1,2) are chosen to be the best model for forecasting
because have lower AIC, S.E of regression, and higher log likelihood.
4.2.3 Model Checking
As was explained in section 3.2, the sign of selected GARCH model fit the data well is the
residuals are follow white noise process. The histogram residuals and the probability value
of Jarque-Bera test is used to perform the normality check. The histogram shows a bellshaped distribution which is indicates the residuals following a normal distribution.
According to table 4.4, the probability value of Jarque-Bera test also have value of
0.390560 which is higher than 0.05. It means that the residuals are normally distributed.
Table 4.4 Model Checking
The Best GARCH
Model
GARCH (1,2) Model
ARCH Effect Test p-value
(Prob.F)
Jarque-Bera pvalue
Q-statistic p-value
0.9012
0.3905
All value higher than
0.05
The Engle (1982) test for ARCH effects is used to test whether the residuals have constant
variances or not. Constant variances means there is no ARCH effect or heteroscedasticity
in the residuals. According to table 4.4, the probability of F-statistic is 0.9012, which are
higher than 5% level of significance. This result is not significant which indicates the
residuals are free from presence of ARCH effects. It can be concluded that the residuals
have constant variances.
The correlogram of the standardized residuals are used for checking the autocorrelation in
the residuals. All the probability values of Q-statistic are not significant or higher than 0.05
significant values. It can be concluded that there are no autocorrelation left in the
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Proceedings of 7th Asia-Pacific Business Research Conference
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residuals. Since the model follow the white noise process, therefore the GARCH (1,2)
model can be used to forecast international cocoa bean prices.
4.2.4 Forecasting with GARCH (1,2) Model
In time series, forecasting is a mathematical way of estimating future values using present
and historical values of the series. Forecasting will use twenty years historical international
cocoa bean prices which are in the form of monthly average of daily ICCO cocoa bean
prices. The estimated pattern of international cocoa bean prices will be conducted by the
upper and lower price limit. The prices limit formed from historical data of international
cocoa bean prices ± standard deviation from the conditional variance. Figure 4.3 shows
the forecast pattern of international cocoa bean prices for the next ten years.
Figure 4.3 Forecast Pattern of International Cocoa Bean Prices for the Next Ten Years
3000
Forecast Pattern of International Cocoa
2500
2000
Lower
Price
Limit
1500
Upper
Price
Limit
1000
500
Differe
nce
Jul-24
Dec-23
May-23
Oct-22
Mar-22
Aug-21
Jan-21
Jun-20
Nov-19
Apr-19
Sep-18
Feb-18
Jul-17
Dec-16
May-16
Oct-15
Mar-15
Aug-14
0
From the period of August 2014 to March 2017 the movements of the estimated prices are
not too volatile. In the April 2017 until October 2018, the forecast prices increase and
make a new pivot point, leaving the previous average price. However, the volatility of the
prices is still low. Then, in period of October 2018 to August 2024 the forecast cocoa bean
prices have a high volatility. The estimated prices increases and decrease rapidly in the
certain time. In October 2018, the forecasts prices start to decrease. The forecast prices
continue to have a great fall until September 2020 which is the lowest price for the next ten
years. After have a great declining, the cocoa bean prices estimated to raises again in
January 2021. The prices continue to increases rapidly until March 2023 which is the
maximum price for the next ten years. However, after have the rapidly increases for about
two years, the forecast cocoa bean prices tend to decline again, starting from April 2023
until July 2024. The forecast price in June 2024 reaches about same pivot points again
with the prices in August 2014.
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4.3 Recommendation on the Production Level and Hedging Strategy for Indonesian
Producer
To deal with the price movement of international cocoa bean in the next ten years,
producer should develop some strategies planning to maximize the profit. Figure 5.1
shows the production level and hedging strategy for Indonesian producer in the next ten
years.
Figure 4.4 Production Level and Hedging Strategy for Indonesian Producer in the Next
Ten Years
Forecast Pattern of International Cocoa Bean Prices
3000
2500
Lower
Price
Limit
2000
1500
Upper
Price
Limit
1000
Maximize
production
Forward
Contract
Differe
nce
500
Aug-24
Feb-24
Aug-23
Feb-23
Aug-22
Feb-22
Aug-21
Feb-21
Aug-20
Feb-20
Aug-19
Feb-19
Aug-18
Feb-18
Aug-17
Feb-17
Aug-16
Feb-16
Aug-15
Feb-15
Aug-14
0
According to Figure 4.6, the international cocoa bean prices tend to have low volatility from
the period of August 2014 to February 2017. In this period, the international cocoa bean
prices usually decline around December and start to rise again around March. In order to
capture maximized profits in this period, producer should prepare their production output to
sell it when the prices are high (March 2015 and July 2016). Producer can also have a
forward contract using March 2015 as agreed price until the end of 2016 to get a high
profit.
On March 2017 until October 2018, the international cocoa bean prices are forecasted to
move higher than the previous average price. Producer should maximize their production
output around March 2017 to July 2017 in other to catch more profit. There some high
price points in these periods which are July 2017, October 2017, January 2018, and June
2018. The producer can choose between these months to get the maximized profit.
The declining in the international cocoa bean prices start in October 2018 and continue to
decline rapidly until its lowest value in September 2020. These huge declining in prices
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makes a new lower pivot point compared to previous prices. In order to deal with these
declining prices, producer should make forward contract to the buyer to lock the sales
price so they can still maintain their profit. Producer should have forward contract with
agreed prices around January 2019 or February 2019 to minimize the reduction in the
profit. The length of this contract is suggested to be two years, start from February 2019 to
February 2021. It can be better to have three years of forward contract in the fixed prices
until February 2022 when the prices will rise again.
On the April 2021, there is a signal that the prices will move to higher pivot point. Producer
should already prepare their production to capture the increased of the prices. The
forecast price will increase rapidly start from July 2021 to its highest value on March 2023.
Producer should make efforts to maximize their production so they can get maximized
profit from this moment.
However, generally in time series data, after the rapid increase of the prices will follow by
the decrease of the prices. After the international cocoa bean prices reach the highest
point on March 2023, it started to decline again from April 2023 to July 2024. The producer
can maintain the high profit by making a forward contract again from around April 2023 or
May 2023 with the one year length of contract which will end in 2024.
5. Summary and Conclusions
This paper has presented some time series analysis in nonlinear model and its application
to forecast the international cocoa bean prices for the next ten years. As the result of data
analysis, the best fitting model to forecast is GARCH (1,2) model. The forecast pattern of
international cocoa bean prices for the next ten years shows a low volatility in the short
term and tends to rise in the long term. Therefore, cocoa bean producer will face a low
price risk in the short term but a high risk in the long term periods.
Cocoa bean producer should use this information to develop production strategy for
maximizing profit and mitigate the risk. They should maximize their production and sell
their cocoa when the prices are estimated to increase (March 2015, July 2016, October
2017, October 2022, March 2023). Another way to maximize profit is having a forward
contract with the buyer and locking the prices when the prices are quite high. When the
prices are estimated to decline (end of 2018 until the end of 2021), they should have
forward contract to lock the prices in order to mitigate the reduction of their profit because
of the declining in the international cocoa bean prices.
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