Advances and Applications in Statistics

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THE EFFECT OF INCORPORATING MEMORY AND STOCHASTIC VOLATILITY INTO GEOMETRIC BROWNIAN MOTION IN FORECASTING THE PERFORMANCE OF TADAWUL ALL SHARE INDEX (TASI)

Authors

  • Anas Abbas
  • Mohammed Alhagyan

Keywords:

TASI, geometric Brownian motion, long memory, stochastic volatility.

DOI:

https://doi.org/10.17654/0972361722017

Abstract

It is known that Tadawul All Share Index (TASI) is the market indices of Saudi Arabia. TASI reflects the performance of financial situation in Saudi Arabia. Therefore, the forecasting of the performance is a crucial issue. This empirical study forecasted the daily index prices of TASI for year 2018 depending on the historical data of year 2017. To act this, three models of geometric Brownian motion (GBM) were depended. These models were first; GBM with no memory and constant volatility. Second, geometric fractional Brownian motion GFBM with memory and under constant volatility assumption. Finally, GFBM with memory and under stochastic volatility assumption. Meanwhile, the evaluation of the performance calculated using mean absolute percentage error (MAPE). All results revealed the positive effect of incorporating both of long memory property and stochastic volatility assumption into GBM model to forecast index prices of TASI and thus can be used in real financial environment.

Received: December 28, 2021
Accepted: February 4, 2022

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Published

24-09-2025

Issue

Vol. 74 (2022)

Section

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How to Cite

THE EFFECT OF INCORPORATING MEMORY AND STOCHASTIC VOLATILITY INTO GEOMETRIC BROWNIAN MOTION IN FORECASTING THE PERFORMANCE OF TADAWUL ALL SHARE INDEX (TASI). (2025). Advances and Applications in Statistics , 74, 47-62. https://doi.org/10.17654/0972361722017

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