Volatility and Value at Risk of Gold Return
Pages 317 - 322
Abstract
This study aims to incorporate the Value at Risk (VaR) with ARCH family models, which are widely used for measuring risk, to express values that are easier to comprehend. The gold price data used for estimating the model and calculating VaR, covering the time period between February 15th, 2018, and February 14th, 2023, has been obtained from the Nasdaq website. The model evaluation based on the significance of parameters, AIC value, and model properties leads to the conclusion that EGARCH (1,1) is an appropriate model. The model estimation indicates that the gold return is sensitive to good news. After obtaining conditional variance, VaR was calculated at a 95 percent confidence interval, and the result showed that most of the time, gold returns fall within this interval.
References
[1]
N. Sopipan, P. Sattayatham, and B. Premanode, “Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy,” JMF, vol. 02, no. 01, pp. 121–131, 2012.
[2]
P. K. Mishra, “Gold Price Volatility and Stock Market Returns in India,” American Journal of Scientific Research, vol. 9, pp. 47–55, 2010.
[3]
C. Pierdzioch, M. Risse, and S. Rohloff, “A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss,” Resources Policy, vol. 47, pp. 95–107, Mar. 2016.
[4]
F. Y. Contuk, H. Burucu, and B. Güngör, “Effect of Gold Price Volatility on Stock Returns: Example of Turkey,” vol. 5, no. 1, pp. 119–140, 2013.
[5]
W. Kristjanpoller and M. C. Minutolo, “Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model,” Expert Systems with Applications, vol. 42, no. 20, pp. 7245–7251, Nov. 2015.
[6]
M. Balcilar, R. Gupta, and C. Pierdzioch, “Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test,” Resources Policy, vol. 49, pp. 74–80, Sep. 2016.
[7]
S. L. Hashim, H. Ramlan, N. H. A. Razali, and N. Z. M. Nordin, “Macroeconomic Variables Affecting the Volatility of Gold Price,” Journal of Global Business and Social Entrepreneurship, vol. 3, no. 5, pp. 97–106, 2017.
[8]
R. Grynberg, T. Kaulihowa, and F. Singogo, “Gold Jewellery Demand and Gold Price Volatility: A Global Perspective,” JEMT, vol. 21, no. 10, pp. 1–13, Oct. 2018.
[9]
I. Yousef and E. Shehadeh, “The Impact of COVID-19 on Gold Price Volatility,” IJEBA, vol. VIII, no. Issue 4, pp. 353–364, Oct. 2020.
[10]
M. M. Marwanti and R. Robiyanto, “Oil and Gold Price Volatility on Indonesian Stock Market in the Period of Covid-19 Pandemic,” jmk, vol. 23, no. 2, pp. 129–137, Sep. 2021.
[11]
N. Raza, S. Jawad Hussain Shahzad, A. K. Tiwari, and M. Shahbaz, “Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets,” Resources Policy, vol. 49, pp. 290–301, Sep. 2016.
[12]
S. Singhal, S. Choudhary, and P. C. Biswal, “Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico,” Resources Policy, vol. 60, pp. 255–261, Mar. 2019.
[13]
B. Shakeri, A. Beytari, M. Ghorbanian, and R. Javadi, “Evaluation of the association between cryptocurrencies with oil and gold prices using the BEKK multivariate GARCH model,” International Journal of Nonlinear Analysis and Applications, vol. 14, no. 1, pp. 2061–2078, 2022.
[14]
R. A. Lamouchi and R. O. Badkook, “Gold Prices Volatility among Major Events and During the Current COVID-19 Outbreak,” Journal of Statistical and Econometric Methods, vol. 9, no. 4, pp. 39–52, 2020.
[15]
S. Setyowibowo, M. As'ad, E. F. Sujito, and E. Farida, “Forecasting of Daily Gold Price using ARIMA-GARCH Hybrid Model,” Jurnal Ekonomi Pembangunan, vol. 19, no. 2, pp. 257–270, 2021.
[16]
A. Khaskheli, H. Zhang, S. A. Raza, and K. A. Khan, “Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period,” Resources Policy, vol. 79, pp. 1–11, 2022.
[17]
I. N. N. Ejap, N. N. Azmi, N. H. M. Ariff, and N. H. Athirah, “Modelling and Forecasting Gold Price Return and Its Volatility,” Applied Mathematics and Computational Intelligence, vol. 11, no. 1, pp. 399–411, 2022.
[18]
S. Manganelli and R. F. Engle, “Value at Risk Models in Finance,” European Central Bank, Working Paper No. 75, 2001. Accessed: Feb. 15, 2023. [Online]. Available: https://www.ssrn.com/abstract=356220
[19]
R. Fu ̈ ss, D. G. Kaiser, and Z. Adams, “Value at risk, GARCH modelling and the forecasting of hedge fund return volatility,” Journal of Derivatives & Hedge Funds, vol. 13, no. 1, pp. 1–24, 2007.
[20]
M. Isabel Restrepo E., “Estimating Portfolio Value at Risk with GARCH and MGARCH models,” Perfil de Coyuntura Económica, vol. 19, pp. 77–92, 2012.
[21]
P. Ani, “Variance – Covariance Risk Value Model for Currency Market,” ENGINEERING ECONOMICS, vol. 1, no. 61, pp. 18–27, 2009.
[22]
L. Maluf and J. Asano, “Comparison of VaR Models to the Brazilian Stock Market Under the Hypothesis of Serial Independence in Higher Orders: Are Garch Models Really Indispensable?,” BBR, vol. 16, no. 6, pp. 626–645, Nov. 2019.
Index Terms
- Volatility and Value at Risk of Gold Return
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Published In
July 2023
507 pages
ISBN:9798400708022
DOI:10.1145/3616712
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 December 2023
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ICEME 2023
ICEME 2023: 2023 the 14th International Conference on E-business, Management and Economics
July 21 - 23, 2023
Beijing, China
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