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Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH

Siti Roslindar, Yaziz and Roslinazairimah, Zakaria (2018) Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH. In: Simposium Kebangsaan Sains Matematik Ke 26 (SKSM26) 2018, 28 - 29 November 2018 , Universiti Malaysia Sabah, Kota Kinabalu Sabah. p. 1.. (Unpublished)

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Abstract

The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study is proposing a new algorithm of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance of the BJ-G model for highly volatile time series data. The promising results from one-step ahead out-of-sample forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the algorithm of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed algorithm is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed algorithm of multistep ahead forecast to the algorithm of BJ-G provides a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The algorithm adds the value of BJ-G model since it allows the model to explain more about the characteristics of the volatile series up to n-step ahead forecast.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Box-Jenkins; GARCH; Highly volatile data; Multistep forecast; Gold price
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 13 Feb 2019 07:55
Last Modified: 13 Feb 2019 07:55
URI: http://umpir.ump.edu.my/id/eprint/24110
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