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Dynamics of market changing as a result of market liberalization have an impact on agricultural commodities price fluctuation. High volatility on cocoa price movement reflect its price and market risk. Because of price and market uncertainty, the market players face some difficulties to make a decision in determining business development. This research was conducted to 1) understand the characteristics of cocoa price movement in cocoa futures trading, and 2)analyze cocoa price volatility using ARCH and GARCH type model. Research was carried out by direct observation on the pattern of cocoa price movement in the futures trading and volatility analysis based on secondary data. The data was derived from Intercontinental Exchange ( ICE) Futures U.S. Reports. The analysis result showed that GARCH is the best model to predict the value of average cocoa price return volatility, because it meets criteria of three diagnostic checking, which are ARCH-LM test, residual autocorrelation test and residual normality test. Based on the ARCH-LM test, GARCH (1,1)did not have heteroscedasticity, because p-value 2 (0.640139)and F-statistic (0.640449) were greater than 0.05. Results of residual autocorrelation test indicated that residual value of GARCH (1,1) was random, because the statistic value of Ljung-Box (LB)on the 36 th lag is smaller than the statistic value of 2. Whereas, residual normality test concluded the residual of GARCH (1,1) were normally distributed, because AR (29), MA (29), RESID (-1)^2, and GARCH (-1) were significant at 5% significance level. Increasing volatility value indicate high potential risk. Price risk can be reduced by managing financial instrument in futures trading such as forward and futures contract, and hedging. The research result also give an insight to the market player for decision making and determining time of hedging. Key words: Volatility, price, cocoa, GARCH, risk, futures trading
How to Cite
Aklimawati, L., & Wahyudi, T. (2013). Estimating the Volatility of Cocoa Price Return with ARCH and GARCH Models. Pelita Perkebunan (a Coffee and Cocoa Research Journal), 29(2). https://doi.org/10.22302/iccri.jur.pelitaperkebunan.v29i2.61
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