The Predictability of GARCH-Type Models on the Returns Volatility of Primary Indonesian Exported Agricultural Commodities

Abstract

Agricultural sector plays an important role in Indonesia"s economy; especially for the plantation sub-sector contributing high revenues to Indonesia"s exporting sectors. The primary agricultural commodities in Indonesian export discussed in this study would be Crude Palm Oil (CPO), Natural Rubber TSR20, Arabica Coffee, Robusta Coffee, Cocoa, White Pepper and Black Pepper. Meanwhile, the returns volatility nature of agricultural commodity is famous. The volatility refers to heteroscedasticity nature of the returns which can be modeled by GARCH-type models. The returns volatility can be describe by the residual of the mean equation and volatility of error variances in the previous periods. The aims of this study are to examine the predictability of GARCH-type models on the returns volatility of those seven agricultural commodities and to determine the best GARCH-type models for each commodity based on the traditional symmetric evaluation statistics. The results find that the predictability of ARCH, GARCH, GARCH-M, EGACRH and TGARCH, as type of GARCH models used in this study, are different for each commodity.

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

“…The GARCH (1,1)-type models was applied by [20] to Indonesian commodity market, [28] to Indonesian foreign exchange market, [5] to Indonesian stock market, and [15] to Indonesian capital market. [20] examined the predictability of five GARCH-type models, namely ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, for seven primary agricultural commodities in Indonesian export and found that the predictability of the considered models is different for each commodity.…”

“…The GARCH (1,1)-type models was applied by [20] to Indonesian commodity market, [28] to Indonesian foreign exchange market, [5] to Indonesian stock market, and [15] to Indonesian capital market. [20] examined the predictability of five GARCH-type models, namely ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, for seven primary agricultural commodities in Indonesian export and found that the predictability of the considered models is different for each commodity. [28] applied the GARCH (1,1) model and some of its variations, such as ARCH(1), TARCH(1,1), TS-GARCH (1,1), GJR-GARCH (1,1), NARCH(1), and APARCH(1,1), for the daily selling exchange rates of the EUR (Euro), JPY (Japanese Yen), and USD (US Dollar) against the IDR (Indonesian Rupiah) covering period from January 2010 to December 2015 and found that the GARCH (1,1) model provided the best fit for the selling rates EUR data.…”

Recommendation system for horticultural commodities

“…All data are expected to contain information of the location, and to be time series data in the period of five to ten years. 4 Developing a price prediction system which can predict the prices of horticultural products with acceptable accuracy.…”

“…Another research further explored the performance of variants of GARCH models in assessing volatility of agricultural product prices in Indonesian spot market, it was mentioned that there are five models of GARCH, which are ARCH, GARCH, GARCH-M, TGARCH and EGARCH [4]. As a conclusion, EGARCH is the best model to assess volatility in almost all the commodities except CPO, which is best assessed using TGARCH.…”