SWGARCH : an enhanced GARCH model for time series forecasting
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...
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Ku Mahamud, Ku Ruhana Othman, Mahmud |
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QA273-280 Probabilities Mathematical statistics Shbier, Mohammed Z. D SWGARCH : an enhanced GARCH model for time series forecasting |
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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. |
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Shbier, Mohammed Z. D |
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Shbier, Mohammed Z. D |
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Shbier, Mohammed Z. D |
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SWGARCH : an enhanced GARCH model for time series forecasting |
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SWGARCH : an enhanced GARCH model for time series forecasting |
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SWGARCH : an enhanced GARCH model for time series forecasting |
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SWGARCH : an enhanced GARCH model for time series forecasting |
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SWGARCH : an enhanced GARCH model for time series forecasting |
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swgarch : an enhanced garch model for time series forecasting |
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Universiti Utara Malaysia |
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Awang Had Salleh Graduate School of Arts & Sciences |
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2017 |
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https://etd.uum.edu.my/6808/1/s91141_01.pdf https://etd.uum.edu.my/6808/2/s91141_02.pdf |
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my-uum-etd.68082021-08-18T07:15:49Z SWGARCH : an enhanced GARCH model for time series forecasting 2017 Shbier, Mohammed Z. D Ku Mahamud, Ku Ruhana Othman, Mahmud Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA273-280 Probabilities. Mathematical statistics 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. 2017 Thesis https://etd.uum.edu.my/6808/ https://etd.uum.edu.my/6808/1/s91141_01.pdf text eng public https://etd.uum.edu.my/6808/2/s91141_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abarbanel, H. (1997). Analysis of Observed Chaotic Data. Cambridge University Press. Akpinar, M., & Yumusak, N. (2013). Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle. In 2013 7th International Conference on Application of Information and Communication Technologies (pp. 1–6). IEEE. doi:10.1109/ICAICT.2013.6722753 Areekul, P., Senjyu, T., Toyama, H., & Yona, A. (2009). Combination of artificial neural network and ARIMA time series models for short term price forecasting in deregulated market. In 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific (pp. 1–4). IEEE. doi:10.1109/TDASIA.2009.5356936 Awartani, B. M., & Corradi, V. 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