UUM Electronic Theses and Dissertation
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SWGARCH : an enhanced GARCH model for time series forecasting

Shbier, Mohammed Z. D (2017) SWGARCH : an enhanced GARCH model for time series forecasting. PhD. thesis, Universiti Utara Malaysia.

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Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is one of most
popular models for time series forecasting. The GARCH model uses the long run variance as one of the weights. Historical data is used to calculate the long run variance because it is assumed that the variance of a long period is similar to the variance of a short period. However, this does not reflect the influence of the daily variance. Thus, the long run variance needs to be enhanced to reflect the influence of
each day. This study proposed the Sliding Window GARCH (SWGARCH) model to improve the calculation of the variance in the GARCH model. SWGARCH consists of four (4) main steps. The first step is to estimate the model parameters and the
second step is to compute the window variance based on the sliding window technique. The third step is to compute the period return and the final step is to embed the recent variance computed from historical data in the proposed model. The performance of SWGARCH is evaluated on seven (7) time series datasets of different
domains and compared with four (4) time series models in terms of mean square error and mean absolute percentage error. Performance of SWGARCH is better than the GARCH, EGARCH, GJR, and ARIMA-GARCH for four (4) datasets in terms of mean squared error and for five (5) datasets in terms of maximum absolute percentage error. The window size estimation has improved the calculation of the long run variance. Findings confirm that SWGARCH can be used for time series forecasting in different domains.

Item Type: Thesis (PhD.)
Supervisor : Ku Mahamud, Ku Ruhana and Othman, Mahmud
Item ID: 6808
Uncontrolled Keywords: GARCH, Time series forecasting, Sliding window, Long run variance.
Subjects: Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Awang Had Salleh Graduate School of Arts & Sciences
Date Deposited: 19 Sep 2018 04:02
Last Modified: 18 Aug 2021 07:15
Department: Awang Had Salleh Graduate School of Arts and Sciences
Name: Ku Mahamud, Ku Ruhana and Othman, Mahmud
URI: https://etd.uum.edu.my/id/eprint/6808

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