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ESSAYS ON FORECASTING STOCK MARKET VOLATILITY / ABDULKARIM ALHEJAILI

Swansea University Author: ABDULKARIM ALHEJAILI

  • E-Thesis under embargo until: 17th December 2026

DOI (Published version): 10.23889/SUThesis.71310

Abstract

This thesis aims to enhance the predictive accuracy of volatility models by examining the predictive power of key uncertainty indicators and introducing a global uncertainty measure designed to forecast future volatility across international stock markets. It comprises three self-contained empirical...

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Published: Swansea University 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Shabi-Ul-Hassan, S., Tsakou, K., and Shabi, S.
URI: https://cronfa.swan.ac.uk/Record/cronfa71310
first_indexed 2026-01-27T11:28:40Z
last_indexed 2026-01-28T05:36:34Z
id cronfa71310
recordtype RisThesis
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spelling 2026-01-27T11:48:06.4275933 v2 71310 2026-01-27 ESSAYS ON FORECASTING STOCK MARKET VOLATILITY 8643405de54add3ceaef91f5c5954981 ABDULKARIM ALHEJAILI ABDULKARIM ALHEJAILI true false 2026-01-27 This thesis aims to enhance the predictive accuracy of volatility models by examining the predictive power of key uncertainty indicators and introducing a global uncertainty measure designed to forecast future volatility across international stock markets. It comprises three self-contained empirical chapters, each addressing a distinct dimension of volatility forecasting.Chapter Two examines the predictive power of local implied volatility (IV) and economic policy uncertainty (EPU) indicators on forecasting aggregate volatility across international market. A central contribution lies in presenting the first comprehensive cross-country of these measures. The chapter also examines whether U.S. uncertainty measures provide superior forecasting performance compared to local uncertainty indicators. The findings reveal that local IV is consistently the most effective predictor of future volatility, particularly during periods of heightened uncertainty, while the predictive strength of U.S. indicators weakens considerably once local measures are accounted.Chapter Three introduces a new Global Implied Volatility (GIV) index, constructed using principal component analysis to combine the IV information from 13 international equity markets and two commodity markets gold and oil. This chapter evaluates the performance of the GIV within the Heterogeneous Autoregressive (HAR) framework and shows that it significantly outperforms the VIX in forecasting implied volatility. These results challenge the prevailing reliance on the VIX as a global uncertainty benchmark and underscore the benefits of incorporating cross-national information.Chapter Four extends the analysis by assessing the ability of the GIV to forecast RV across 28 international stock markets. The results demonstrate that the GIV improves out-of-sample forecast accuracy in most markets. Its predictive performance is further strengthened when combined with the local IV index. The study also introduces a new Global Variance Risk Premium (GVRP), which shows superior predictive power for international equity returns, particularly at medium and longer horizons compared to the U.S VRP. E-Thesis Swansea University Volatility forecasting, Implied Volatility (IV), Global Implied Volatility (GIV), Realized volatility (RV), Economic policy uncertainty (EPU), Variance Risk Premium (VRP), Heterogeneous Autoregressive (HAR) model. 17 12 2025 2025-12-17 10.23889/SUThesis.71310 COLLEGE NANME COLLEGE CODE Swansea University Shabi-Ul-Hassan, S., Tsakou, K., and Shabi, S. Doctoral Ph.D Jouf University Jouf University 2026-01-27T11:48:06.4275933 2026-01-27T11:13:34.9533074 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance ABDULKARIM ALHEJAILI 1 Under embargo Under embargo 2026-01-27T11:25:52.9842809 Output 4932918 application/pdf E-Thesis true 2026-12-17T00:00:00.0000000 Copyright: the author, Abdulkarim Alhejaili, 2025 true eng
title ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
spellingShingle ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
ABDULKARIM ALHEJAILI
title_short ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
title_full ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
title_fullStr ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
title_full_unstemmed ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
title_sort ESSAYS ON FORECASTING STOCK MARKET VOLATILITY
author_id_str_mv 8643405de54add3ceaef91f5c5954981
author_id_fullname_str_mv 8643405de54add3ceaef91f5c5954981_***_ABDULKARIM ALHEJAILI
author ABDULKARIM ALHEJAILI
author2 ABDULKARIM ALHEJAILI
format E-Thesis
publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUThesis.71310
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
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description This thesis aims to enhance the predictive accuracy of volatility models by examining the predictive power of key uncertainty indicators and introducing a global uncertainty measure designed to forecast future volatility across international stock markets. It comprises three self-contained empirical chapters, each addressing a distinct dimension of volatility forecasting.Chapter Two examines the predictive power of local implied volatility (IV) and economic policy uncertainty (EPU) indicators on forecasting aggregate volatility across international market. A central contribution lies in presenting the first comprehensive cross-country of these measures. The chapter also examines whether U.S. uncertainty measures provide superior forecasting performance compared to local uncertainty indicators. The findings reveal that local IV is consistently the most effective predictor of future volatility, particularly during periods of heightened uncertainty, while the predictive strength of U.S. indicators weakens considerably once local measures are accounted.Chapter Three introduces a new Global Implied Volatility (GIV) index, constructed using principal component analysis to combine the IV information from 13 international equity markets and two commodity markets gold and oil. This chapter evaluates the performance of the GIV within the Heterogeneous Autoregressive (HAR) framework and shows that it significantly outperforms the VIX in forecasting implied volatility. These results challenge the prevailing reliance on the VIX as a global uncertainty benchmark and underscore the benefits of incorporating cross-national information.Chapter Four extends the analysis by assessing the ability of the GIV to forecast RV across 28 international stock markets. The results demonstrate that the GIV improves out-of-sample forecast accuracy in most markets. Its predictive performance is further strengthened when combined with the local IV index. The study also introduces a new Global Variance Risk Premium (GVRP), which shows superior predictive power for international equity returns, particularly at medium and longer horizons compared to the U.S VRP.
published_date 2025-12-17T05:33:48Z
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score 11.096047

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