2023
doi: 10.12720/jait.14.2.311-318
Forecasting Volatility in Generalized Autoregressive Conditional Heteroscedastic (GARCH) Model with Outliers
Shahid Akbar1
,
Tanzila Saba2 ,
Saeed Ali Bahaj3 ,
Muhammad Inshal4 ,
Amjad Rehman Khan2,*
- 1Department of Economics, University of Swabi, Swabi, 23430, Pakistan
- 2Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh 11586, Saudi Arabia
- 3MIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia
- 4Taiko Foods - Snowfox Group London W3 7XR United Kingdom
- Manuscript receivedAugust 16, 2022
- revisedSeptember 14, 2022
- acceptedSeptember 29, 2022
- publishedApril 13, 2023
Abstract
This study aims to detect outliers and identify the best outlier detection technique and forecasting model for the financial time series data. Six outlier detection techniques and three forecasting models are compared to find the best technique and model using the daily returns data of the Pakistan Stock Exchange (PSX) 100 index from January 1996 to July 2020. The data is divided into two sections: the first estimate the model from January 1996 to December 2020, while the second produces one-day forecasts from January 2021 to July 2021. According to the research findings, the Mean Absolute Deviation (MADe) method of outlier identification outperforms the other outlier detection techniques in all three forecasting models with distinct loss functions. Furthermore, when comparing Generalized Autoregressive Conditional Heteroscedastic (GARCH) type models, Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH (1,1)) outperforms the other two forecasting models corresponding to the reported Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Therefore, the findings recommend that researchers use the MADe method to detect outliers and the EGARCH model as a forecasting model for financial time series data.
Keywords
- predictive analysis
- outliers detection
- Generalized Autoregressive Conditional Heteroscedastic (GARCH) models
- time series
- loss functions
- economics growth
How to Cite
Shahid Akbar, Tanzila Saba, Saeed Ali Bahaj, Muhammad Inshal, and Amjad Rehman Khan, "Forecasting Volatility in Generalized Autoregressive Conditional Heteroscedastic (GARCH) Model with Outliers," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 311-318, 2023. https://doi.org/10.12720/jait.14.2.311-318
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
