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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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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Open Access Research Article

Gold price volatility and forecasting evaluations with the impact of COVID-19 pandemic

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* Corresponding author · click or hover a name for details

pp. 1867–1882Vol. 26Issue 8November 2023DOI: 10.47974/JSMS-985XML
Received:
09 Mar 2022
Accepted:
05 Jul 2022
Published Online:
23 Dec 2023
Article type:
Research Article
Language:
EN
Article no.:
JSMS-985
Pages:
1867–1882

Abstract

Gold is a precious metal that has always been recognized as a safe-haven investment for many defensive investors. As compared to the stock market, gold is considered less volatile. In early 2020, the Covid-19 pandemic has caused turbulence in the financial market. It is believed that the pandemic has affected the volatility of gold market. This study aims to investigate the volatility of gold market before the Covid-19 pandemic and during the pandemic using several different models including GARCH, EGARCH and GJR-GARCH. The use of EGARCH and GJR-GARCH is meant to capture the leverage effect of the market in order to have a better volatility forecast. With the results from the analysis, other financial applications such as determining value-at-risk and forecasting can be done. The results of this study act as a good reference to investors who are interested in gold investment.

Keywords

Subject Classifications

91B2591B3037M10

References

[1] Box G.E.P. , Gwilym Jenkins, Gregory C.R., Ljung G.M. 2016. Time Series Analysis: Forecasting and Control, 5th edition. Wiley.
[2] Brock T. 2021. Does it still pay to invest in gold? 
[3] https://www. investopedia.com/articles/basics/08/invest-in-gold. asp, 2011. 
[4] Cowpertwait P.S.P., Metcalfe A.V. 2009. Introductory Time Series with R. Use R! Springer New York.
[5] Curto J.D., Serrasqueiro P. (In press). The impact of COVID-19 on S&P500 sector indices and FATANG stocks volatility: An expanded APARCH model, Finance Research Letters, https://doi.org/10.1016/j.frl.2021.102247
[6] Daltorio T. 2021. 8    Good reasons to own gold. https: //www.investopedia.com/articles/basics/08/ reasons-to-own-gold.asp.
[7] Engle R.F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50(4):987–1007.
[8] Glosten, L. R., Jagannathan R., Runkle D. E.. 1993. On The Relation between The Expected Value and The Volatility of Nominal Excess Return on stocks. Journal of Finance 48: 1779-1801. 
[9] Lioudis N. 2021. What is the gold standard?     https://www. investopedia.com/ask/answers/09/gold-standard.asp
[10] Oxford Gold Group. 2021. How does gold perform during a recession? https://www.oxfordgoldgroup.com/articles/how-does-gold-perform-during-a-recession/
[11] The Local, 2021. The long history of gold trading. https://www.thelocal.no/ 20111114/the-long-history-of-gold-trading/
[12] Yuan YaLi (2017) Forecasting method for import and export trade on the basis of GMDH network model, Journal of Discrete Mathematical Sciences and Cryptography, 20:4, 755-766 DOI: 10.1080/09720529.2017.1358859.
[13] Jia LuoGe, Xiang ZhuHui. (2022). Artificial Intelligent based day-ahead stock market profit forecasting. Computers and Electrical Engineering, Vol 99, 107837.
[14] Zhang Chun-Xia, Li Jun, huang Xing-Fang, Zhang Jiang-She and Huang Hua-Chuan. 2022. Forecasting stock volatility and value-at-risk based on temporal convolutional networks. Expert Systems with Applications, Vol. 207, 117951.
[15] Kumburea Mahinda-Mailagaha, Lohrmanna Christoph, Luukka Pasi and Porras Jari. 2022. Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, Vol. 197, 116659.
[16] Timmermann Allen, Chapter 4 forecast combinations, Handbook of Economic Forecasting 1(2006), pp. 135-196.
[17] Chin WenCheong and Lee MinCherng. (2021). Nonlinear high-frequency stock market time series: Modeling and combine forecast evaluations, Communications in Statistics – Simulation and Computation, Vol. 50(7), 2126-2144.

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