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Predicting the Volatility of Natural Rubber Price Using Time Series Models<br />

Nur Adilla Binti Othman<br />

Supervisor: Dr. Hanafi Bin A. Rahim<br />

Bachelor of Science in Financial Mathematics<br />

School of Informatics and Applied Mathematics<br />

The natural rubber industry is one of the most important economies in country. This study<br />

discusses about the volatility of natural rubber price using the time series models. The<br />

study used the daily data for April 2013 to November 2017. The existence of<br />

autocorrelation and ARCH effect was tested using Durbin Watson test and Lagrange<br />

Multiplier test respectively. Further, ARCH (1) and GARCH (1,1) models are used. In<br />

addition, the QGARCH (1,1) model has also been used to study the volatility of this natural<br />

rubber price. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC)<br />

are computed in order to evaluate the performance of the identified model. The results<br />

obtained from the comparison that have been done on all three models show that GARCH<br />

(1,1) model is the best model for natural rubber data.<br />

850 | UMT UNDERGRADUATE RESEARCH DAY 2018

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