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Forecasting Volatility: Evidence from the Saudi Stock Market

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  • Naseem Al Rahahleh

    (Department of Finance, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Robert Kao

    (Department of Accounting, Economics, and Finance, School of Business, Park University, Parkville, MO 64152, USA)

Abstract

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations.

Suggested Citation

  • Naseem Al Rahahleh & Robert Kao, 2018. "Forecasting Volatility: Evidence from the Saudi Stock Market," JRFM, MDPI, vol. 11(4), pages 1-18, November.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:84-:d:186076
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    3. Elyas Abdulahi Mohamued & Masood Ahmed & Paula Pypłacz & Katarzyna Liczmańska-Kopcewicz & Muhammad Asif Khan, 2021. "Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions," Energies, MDPI, vol. 14(6), pages 1-18, March.

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