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Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Indexed in ABDC Australia

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Statistics and Management Systems
Open Access Research Article

Analyzing forecasting capabilities of GARCH models for stock prices in stochastic volatility contexts

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pp. 1065–1084Vol. 27Issue 5July 2024DOI: 10.47974/JSMS-1349XML
Received:
08 May 2024
Published Online:
05 Aug 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1349
Pages:
1065–1084

Abstract

Gold, a highly valued and sought-after asset, has long captured the interest of investors, financial analysts, and policymakers due to its historical significance. To predict future gold prices, this study uses the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model. The GARCH model is implemented in R Studio, and this study’s findings provide valuable insights for investors, equipping them with informed decision-making capabilities and robust risk management strategies. The precise gold price forecasting achieved through the GARCH method highlights its reliability in financial prediction. This research also adds to the body of knowledge already available on gold price forecasting and provides a basis for investigating alternative models and incorporating macroeconomic aspects to improve prediction accuracy. Three versions of GARCH model are compared namely: GJR-GARCH, EGARCH, and standard GARCH. The implications shows that EGARCH model showed promising results, there is scope for further research and validation. Continual refinement and testing of the model’s assumptions and parameters are essential to ensure its reliability and applicability in different market conditions.

Keywords

Gold priceGARCHEHARCH and GJR GARCH

Subject Classifications

68Q10

References

[1] Zhang, Pinyi, and Bicong Ci. “Deep belief network for gold price forecasting.” Resources Policy 69 : 101806 (2020).
[2] Liang, Yayun, et al. “Gold source and ore-forming process of the Linglong gold deposit, Jiaodong gold province, China: Evidence from textures, mineral chemical compositions and sulfur isotopes of pyrite.” Ore Geology Reviews 159 : 105523 (2023).
[3] Liu, Peng, and Ke Tang. “The stochastic behavior of commodity prices with heteroskedasticity in the convenience yield.” Journal of Empirical Finance 18.2 : 211-224 (2011).
[4] Zhao, Shuai, et al. “Comprehensive investigations into low temperature oxidation of heavy crude oil.” Journal of Petroleum Science and Engineering 171 : 835-842 (2018).
[5] Sari, Ramazan, Shawkat Hammoudeh, and Ugur Soytas. “Dynamics of oil price, precious metal prices, and exchange rate.” Energy Economics 32.2 : 351-362 (2010).
[6] Engle, Robert F., Eric Ghysels, and Bumjean Sohn. “Stock market volatility and macroeconomic fundamentals.” Review of Economics and Statistics 95.3 : 776-797 (2013).
[7] https://webscope.sandbox.yahoo.com/
[8] Bollerslev, Tim. “Glossary to arch (garch).” CREATES Research paper 49 (2008).
[9] Su, Chang. “Application of EGARCH model to estimate financial volatility of daily returns: The empirical case of China.” (2010).
[10] Ali, Ghulam. “EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH models for pathogens at marine recreational sites.” Journal of Statistical and Econometric Methods 2.3 : 57-73 (2013).
[11] Wang, I-Ming, and Chich-Jen Shieh. “The relationship between service quality and customer satisfaction: the example of CJCU library.” Journal of Information and optimization Sciences 27.1 : 193-209 (2006).
[12] Abdulghani, Farah A., and Nada AZ Abdullah. “Hybrid deep learning model for Arabic text classification based on mutual information.” Journal of Information and Optimization Sciences 43.8 : 1901-1908 (2022).

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