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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Open Access Research Article

Gold market risk evaluations using GARCH incorporate with machine learning

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pp. 1381–1391Vol. 27Issue 7October 2024DOI: 10.47974/JSMS-1214XML
Received:
15 Feb 2023
Published Online:
30 Nov 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1214
Pages:
1381–1391

Abstract

This paper utilizes the Support Vector Regression (SVR) and Artificial Neural Network (ANN) integrated with a GARCH model in analyzing volatility within the gold market. We used the root of mean square error to compare the performance between the econometric model and various ML-GARCH models in forecasting the stock price of COMEX Gold Futures. SVR model with RBF kernel is found to be the most successful model in predicting the future stock prices of COMEX Gold Futures with an extremely low RMSE value among the machine learning models. For the market risk evaluations, we found that the gold future market become less volatile during the COVID-19 pandemic as compared to before pandemic.

Keywords

Subject Classifications

91B2591B8492B20

References

[1] T. Liu and Y. Shi, “Forecasting crude oil future volatilities with a threshold zero-drift GARCH model,” Mathematics, vol. 10, p. 2757 (2022).
[2] R. Maheshwari and V. Kapoor, “Estimating the volatility of stock price index for the Indian market using GARCH model,” J. Stat. Manag. Syst., vol. 25, no. 7, pp. 1523-1530 (2022).
[3] P. J. Venter and E. Maré, “Univariate and multivariate GARCH models applied to Bitcoin futures option pricing,” J. Risk Financial Manag., vol. 14, p. 261 (2021).
[4] K. D. Ashok and S. Murugan, “Performance analysis of Indian stock market index using neural network time series model,” in Proc. 2013 Int. Conf. Pattern Recognition, Informatics and Mobile Engineering, pp. 72–78 (2013).
[5] L. Chuong and D. Nikolai, “Forecasting of realised volatility with the random forests algorithm,” J. Risk Financial Manag., vol. 11, no. 4, p. 61 (2018).
[6] J. Huang and W. Pan, “Performing stock closing price prediction through the use of principle component regression in association with general regression neural network,” J. Discrete Math. Sci. Cryptogr., vol. 12, no. 6, pp. 717-728 (2009).
[7] M. Seo and G. Kim, “Hybrid forecasting models based on the neural networks for the volatility of Bitcoin,” Applied Sciences, vol. 10, p. 4768 (2020).
[8] W. K. Liu and M. K. P. So, “A GARCH model with artificial neural networks,” Information, vol. 11, p. 489 (2020).
[9] A. Petrozziello, L. Troiano, A. Serra, I. Jordanov, G. Storti, R. Tagliaferri, and M. L. Rocca, “Deep learning for volatility forecasting in asset management,” Soft Comput., vol. 26, pp. 8553–8574 (2022).
[10] L. Tze and P. S. Samuel, “Stochastic neural networks with applications to nonlinear time series,” J. Amer. Stat. Assoc., vol. 96, no. 455, pp. 968–981 (2001).
[11] H. S. Vatsal, “Machine learning techniques for stock prediction,” Foundations of Machine Learning, vol. 1, no. 1, pp. 6–12 (2007).
[12] W. J. Chen, J. J. Yao, and Y. H. Shao, “Volatility forecasting using deep neural network with time-series feature embedding,” Economic Research Ekonomska Istraživanja, vol. 36, no. 1, pp. 1377-1401 (2023).
[13] S. H. Yang, “Stock price direction prediction by directly using prices data: an empirical study on the Kospi and HSI,” Int. J. Bus. Intell. Data Mining, vol. 9, no. 2, pp. 145–160 (2014).
[14] H. Q. Yang, L. W. Chan, and I. King, “Support vector machine regression for volatile stock market prediction,” in Proc. Int. Conf. Intell. Data Eng. Autom. Learn., pp. 391–396 (2002).
[15] V. Jyothi and M. M. Tripathi, “K-means clustering based photovoltaic power forecasting using artificial neural network, particle swarm optimization and support vector regression,” J. Inf. Optim. Sci., vol. 40, no. 2, pp. 309-328 (2019). doi: 10.1080/02522667.2019.1578091.
[16] B. Parveen and S. Bhardwaj, “Estimation of solar radiation using support vector regression,” J. Inf. Optim. Sci., vol. 40, no. 2, pp. 339-350 (2019). doi: 10.1080/02522667.2019.1578093.
[17] K. Abdullah, Machine Learning for Financial Risk Management with Python, O’Reilly Media, Inc. (2021).
[18] T. Cheng, J. Liu, W. Yao, and A. B. Zhao, “The impact of COVID-19 pandemic on the volatility connectedness network of global stock market,” Pacific-Basin Finance Journal., vol. 71, p. 101678 (2022).
[19] H. Hong, Z. Bian, and C. C. Lee, “COVID-19 and instability of stock market performance: evidence from the U.S.,” Financial Innovation, vol. 7, p. 12 (2021).
[20] S. S. Sharma, “A note on the Asian market volatility during the COVID-19 pandemic,” Asian Economics Letters, vol. 1, no. 2 (2020).

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