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J. Risk Financial Manag. 2018, 11(4), 84; https://doi.org/10.3390/jrfm11040084

Forecasting Volatility: Evidence from the Saudi Stock Market

1
Department of Finance, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Department of Accounting, Economics, and Finance, School of Business, Park University, Parkville, MO 64152, USA
*
Author to whom correspondence should be addressed.
Received: 24 October 2018 / Revised: 19 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
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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. View Full-Text
Keywords: Tadawul All Share Index; TIPISI index for petrochemical industries; volatility forecasting; GARCH; out-of-sample forecast Tadawul All Share Index; TIPISI index for petrochemical industries; volatility forecasting; GARCH; out-of-sample forecast
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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).
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Al Rahahleh, N.; Kao, R. Forecasting Volatility: Evidence from the Saudi Stock Market. J. Risk Financial Manag. 2018, 11, 84.

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