Page 208 - Special Topic Session (STS) - Volume 1

STS425 Arifah B. et al.
3.6 CPO Forecast Price
14 Days Forecast with MAPE = 1.05
Date Forecast
6/1/2018 652.9451
6/4/2018 652.766
6/5/2018 656.5104
6/6/2018 650.6179
6/7/2018 651.445
6/8/2018 638.5913
6/11/2018 630.5371
6/12/2018 632.9981
13/6/2018 632.7982
14/6/2018 641.9716
15/6/2018 647.309
18/6/2018 633.0494
19/6/2018 634.6982
20/6/2018 649.5477

4. Conclusion
The framework for handling LMSV had been applied for detection of long
memory process of the CPO time series. This study presents the results of the
estimated volatility process based on the proxies of volatilities, where the
parameters of the LMSV model had been correspondingly estimated.
Procedures have been established to construct the LMSV model and
estimation methods suitable to explain the CPO market tendency in Malaysia
with small errors.

References
1. Ahmad, M. H., Ping, P. Y. & Mahamed, n. 2014. Volatility modelling and
forecasting of Malaysian crude palm oil prices. Applied Mathematical
Sciences, 8, 6159-6169.
2. Arshad, F. & Zainalabidin, M. Price discovery through crude palm oil
futures market: An economic evaluation. Proceedings of the 3rd Annual
World Business Congress on Capitalising the Potentials of Globalisation-
Strategies and Dynamics of Business, 1994. 73-92.
3. Arshad, F. M. & Ghaffar, R. A. 1986. Crude Palm Oil Price Forecasting
Box-Jenkins Approach, Universiti Pertanian Malaysia.
4. Bardet, J. & Kammoun, I. 2008. Asymptotic Properties of the Detrended
Fluctuation Analysis of Long-Range-Dependent Processes. IEEE
Transactions on Information Theory, 54, 2041-2052.

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