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 1 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 2 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. 3 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 4 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 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. 5 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. 6 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 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 7 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 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. 8 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 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 9 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 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. References Assis, K, Amran, A, & Remali, Y 2010, „Forecasting Cocoa Bean Prices Using Univariate Time Series Model‟, Journal of Art Science & Commerce, vol.1, no.1, pp.71-80. Bollerslev, T 1986, „Generalized Autoregressive Conditional Heteroscedasticity‟, Journal of Econometrics, vol.31, pp.307-327. 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World Cocoa Foundation 2012, Cocoa Market Update, viewed 23 July 2014 from http://worldcocoafoundation.org/wp-content/uploads/Cocoa-Market-Update-as-of3.20.2012.pdf 11 Proceedings of 7th Asia-Pacific Business Research Conference 25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0 12