Home > Published Issues > 2023 > Volume 14, No. 2, 2023 >
JAIT 2023 Vol.14(2): 311-318
doi: 10.12720/jait.14.2.311-318

Forecasting Volatility in Generalized Autoregressive Conditional Heteroscedastic (GARCH) Model with Outliers

Shahid Akbar 1, Tanzila Saba 2, Saeed Ali Bahaj 3, Muhammad Inshal 4, and Amjad Rehman Khan 2,*
1. Department of Economics, University of Swabi, Swabi, 23430, Pakistan; Email: shahidakbar@uoswabi.edu.pk (S.A.)
2. Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh 11586, Saudi Arabia; Email: drstanzila@gmail.com (T.S.)
3. MIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia; Email: s.bahaj@psau.edu.sa (S.A.B.)
4. Taiko Foods - Snowfox Group London W3 7XR United Kingdom; Email: Muhammad.inshal@taikofoods.co.uk (M.I.)
*Correspondence: arkhan@psu.edu.sa (A.R.K.)

Manuscript received August 16, 2022; revised September 14, 2022; accepted September 29, 2022; published April 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

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.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.