The Effect of Incorporating Memory and Stochastic Volatility Into Geometric Brownian Motion in Forecasting the Performance of Tadawul All Share Index (Tasi)

Abbas, Anas; Alhagyan, Mohammed

doi:10.17654/0972361722017

Cited by 2 publications

(3 citation statements)

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“…In general, the empirical results of this study agree with earlier empirical works such as Abbas & Alhagyan (2022;), Mansour & Alhagyan (2022, Alhagyan & Yassen (2023), Alhagyan (2022), Willinger et al (1999), Painter (1998), Rejichi & Aloui (2012) and Alhagyan & Alduais (2020) to name just a few. Subsequently, we are strongly recommend investors and trader to invest in the Energy Sector in Saudi Arabia because of its predictability and stability.…”

Section: Discussionsupporting

confidence: 92%

“…Moreover, one can observe that the values of MSE of SVGFBM, GFBM, and SVGBM are close together while GBM is relatively far. These outcomes run in the same direction as many experimental researches for instance Willinger et al (1999), Rejichi and Aloui (2012), Alhagyan (2022), Painter (1998), Alhagyan and Yassen (2023), Alhagyan and Alduais (2020), and Abbas and Alhagyan (2022;. Figure 3 illustrates the comparison between the actual close prices versus forecasted close prices.…”

Section: Forecasting and Evaluationsupporting

confidence: 54%

“…The GFBM model is considered a developed version of GBM, thus they can be employed in the same financial applications. For example, option pricing (Misiran et al 2010;, index prices (Alhagyan & Al-Duais 2020, Abbas & Alhagyan 2022, Xiao et al 2015, value at risk (Alhagyan et al 2021, Wang et al 2017, exchange rate (Gözgör 2013, Mansour and Alhagyan 2022, Alhagyan 2022, Abbas and Alhagyan 2023), and mortgage insurance (Bardhan et al 2006, Alhagyan et al 2021, Chen et al 2013.…”

Section: Geometric Brownian Motion Modelsmentioning

confidence: 99%

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Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Alhagyan

2024

Preprint

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The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

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Section: Discussionsupporting

confidence: 92%

Section: Forecasting and Evaluationsupporting

confidence: 54%

Section: Geometric Brownian Motion Modelsmentioning

confidence: 99%

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Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Alhagyan

2024

Preprint

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Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM Models

Alhagyan

2024

JRFM

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Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector.

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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)

Cited by 2 publications

References 0 publications

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM Models

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