Modeling the Dynamics of Gold Price Volatility Over Time

Articles in Press

Document Type : Research Paper

Authors

1 Department of Information Technology Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Department of Economics, Modeling and Optimization Research Center in Engineering Sciences, Tehran, Iran.

3 Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.

10.22103/jdc.2025.23835.1495

Abstract

Objective: Gold, as a strategic and volatile commodity, requires accurate price forecasting due to its economic importance. This study forecasts gold prices using a combination of approaches, including nonlinear Bayesian models, wavelet coherence, and multivariate models with time-varying parameters.
 
Method: This applied research uses monthly data from 2010 to 2022. Initially, 35 factors affecting gold price volatility were identified. Then, GARCH and stochastic volatility models were used to extract volatility, while TVPDMA, TVPDMS, and BMA models were employed to identify influential variables. The TVP-Quantile VAR approach was used to analyze causality between variables, and wavelet coherence was applied to examine the relationship between variables and gold price volatility across different time scales.
 
Results: This applied research uses monthly data from 2010 to 2022. Initially, 35 factors affecting gold price volatility were identified. Then, GARCH and stochastic volatility models were used to extract volatility, while TVPDMA, TVPDMS, and BMA models were employed to identify influential variables. The TVP-Quantile VAR approach was used to analyze causality between variables, and wavelet coherence was applied to examine the relationship between variables and gold price volatility across different time scales.
 
Conclusion: This applied research uses monthly data from 2010 to 2022. Initially, 35 factors affecting gold price volatility were identified. Then, GARCH and stochastic volatility models were used to extract volatility, while TVPDMA, TVPDMS, and BMA models were employed to identify influential variables. The TVP-Quantile VAR approach was used to analyze causality between variables, and wavelet coherence was applied to examine the relationship between variables and gold price volatility across different time scales.

Keywords

Main Subjects

References

اسکندری سبزی، سیما و حاجی آقاجانی؛ اعظم (1402). تأثیر همه‌گیری کووید-19 بر بازار سهام: مطالعه موردی کشورهای منتخب عضو اوپک. مجله توسعه و سرمایه، 8(1)، 151-168. .DOI: 10.22103/jdc.2022.19657.1259
بشارت‌نیا، فاطمه و طریقت، میلاد (1395). پیش‌بینی قیمت طلا با استفاده از شبکه‌های عصبی. دومین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات، آمل. https://civilica.com/doc/612499.
حدادی، محمدرضا؛ نادمی، یونس و فرهادی، حامد (1399). پیش‌بینی روند حرکت قیمت جهانی طلا با رویکرد مدل‌سازی توزیع‌های حاشیه‌ای: کاربردی از مدل‌های گارچ کاپولای گوسی و تی. مهندسی مالی و مدیریت اوراق بهادار، 11(42)، 88-67. https://sanad.iau.ir/Journal/fej/Article/1077882.
حسینی، حسن و نمکی، علی (1402). پیش‌بینی قیمت طلا با استفاده از شبکه حافظه بلند کوتاه‌مدت LSTM. نخستین همایش ملی اقتصاد هوشمند و توسعه مالی، تهران. https://civilica.com/doc/1816468.
حیدری اشترینانی، سروش؛ خوچیانی، رامین و خرسندزاک، محمد (1401). بررسی رابطه پویا بین بیت‌کوین با شاخص سهام، طلا و دلار در ایران: کاربردی از رویکرد همدوسی و تحلیل موجک. مجله توسعه و سرمایه، 7(2)، 91-109. DOI: 10.22103/jdc.2022.19251.1224.
حیدری، فریبا؛ ندری، کامران و حاجی، غلامعلی (1402). بررسی عوامل مؤثر بر قیمت مسکن با تأکید بر مطالبات غیرجاری بانک‌ها (رویکرد الگوهای VAR-TVPDMA). مجله توسعه و سرمایه، 8(2)، 91-112. DOI: 10.22103/jdc.2023.21279.1384.
خانی، فاطمه؛ جعفری صمیمی، احمد؛ طهرانچیان، امیرمنصور و احسانی، محمدعلی (1400). آثار بازار پول بر بازار طلا با رویکرد پویایی‌شناسی سیستمی. فصلنامه علمی مدل‌سازی اقتصادی، 15(54)، 1-19. https://www.sid.ir/paper/1061323.
خونساریان، فرانک؛ تیمورپور، بابک و رستگار، محمدعلی (1402). پیش‌بینی قیمت با شبکه عصبی مصنوعی LSTM و مدل انتخاب سبد سهام دارایی‌های مالی و ارز‌های دیجیتال. مهندسی مالی و مدیریت اوراق بهادار، 14(57)، 116-134. https://journals.iau.ir/article_703286.html.
زاهدی، یعقوب؛ رضایی، نادر و نجاری، ودود (1402). سنجش و آزمون انتقال متقابل حباب در بازار‌های بورس اوراق بهادار، ارز و طلا (مطالعه موردی: ایران با استفاده از توابع کاپولا). مهندسی مالی و مدیریت اوراق بهادار، 14(57)، 135-154. https://journals.iau.ir/article_700570.html.
کاظم‌زاده، عماد؛ ابراهیمی سالاری، تقی و بهنامه، مهدی (1398). پیش‌بینی نرخ رشد قیمت سکه طلا در ایران با استفاده ازالگوی رگرسیون داده‌ها با تواتر متفاوت (میداس). اقتصاد کاربردی، 9(28)، 53-43. https://www.sid.ir/paper/966993.
گرگ‌بندی، ساره و موسوی، سیدمرتضی (1402). پیش‌بینی روند کوتاه مدت قیمت طلا در بازار فارکس با استفاده از شبکه‌های عصبی عمیق. ششمین کنفرانس ملی فناوری‌های نوین در مهندسی برق و کامپیوتر، اصفهان. https://civilica.com/doc/1876625.
محمد نژادی پاشاگی، محمدباقر؛ صادقی شریف، سید جلال و اقبال نیا، محمد (1402). بررسی و تحلیل اثرات سرریز بازار سهام در تعامل با بازارهای ارز، سکه طلا، نفت و مسکن: مدلVARMA-BEKK-AGARCH. مهندسی مالی و مدیریت اوراق بهادار، 14(57)، 88-109. https://jfr.ut.ac.ir/article_92804.html.

References

Adekoya, O.B., Asl, M.G., Oliyide, J.A., & Izadi, P. (2023). Multifractality and cross-correlation between the crude oil and the European and non-European stock markets during the Russia-Ukraine war. Resources Policy, 80(C), 103134. https://ideas.repec.org/a/eee/jrpoli/v80y2023ics0301420722005773.html.
Amini, A., & Kalantari, R. (2024). Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning. PLoS One, 19(3), e0298426. https://doi.org/10.1371/journal.pone.0298426.
Apergis, N., Cooray, A., Khraief, N., & Apergis, I. (2019). Do gold prices respond to real interest rates? Evidence from the Bayesian Markov Switching VECM model. Journal of International Financial Markets, Institutions and Money, 60, 134–148. https://doi.org/10.1016/j.intfin.2018.12.014.
Basharatnia, F., & Taqiqat, M. (2016). Forecasting gold price using neural networks. In Second National Conference on Computer Science and Information Technology, Amol. https://civilica.com/doc/612499 [In Persian].
Baur, D.G., & McDermott, T.K. (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34(8), 1886-1898. https://doi.org/10.1016/j.jbankfin.2009.12.008.
Carlson, J.A., & Lothian, J.R. (2006). The dynamic adjustment of expectations to a change in regime: The case of inflation. American Economic Review, 96(2), 103-116. https://ideas.repec.org/a/ijc/ijcjou/y2022q1a5.html.
Chiang, T. (2022). Geopolitical risks and gold price volatility. Journal of Risk and Financial Management, 15(4), 189–201.
Chong, T.T., & Lai, M.M. (1999). Efficiency of the Hong Kong stock market. Applied Financial Economics, 9(3), 293-304.
Choudhary, P. (2021). An influence of analysis on the Indian gold price with Sensex and Nifty in India. Journal of Advances and Scholarly Research in Allied Education, 16(5), 1414-1419. https://ignited.in/index.php/-jasrae/article/view/11122/22048.
Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.
Cohen, G., & Aiche, A. (2023). Forecasting gold price using machine learning methodologies. Chaos, Solitons & Fractals, 175, 2. https://doi.org/10.1016/j.chaos.2023.114079.
Daga, V., & James, L. (2020). Effect of macroeconomic factors on the price of gold in India. 8, 1-13. https://ijcrt.org/papers/IJCRT_192457.pdf.
Dahir, A.M., Mahat, F., Hisyam, A.B., & Razal, N. (2017). Revisiting the dynamic relationship between exchange rates and stock prices in BRICS countries: A wavelet analysis. Borsa Istanbul Review, 1(13), 258-271. DOI: 10.1016/j.bir.2017.10.001.
Dalam, A.M., Kulub, N., Rashid, A., & Padli, J. (2019). Factors determining gold prices in Malaysia. Universiti Malaysia Terengganu Journal of Undergraduate Research, 1(2), 75–82.
Deniz, P., & Stengos, T. (2023). Revisiting the determinants of consumption: A Bayesian model averaging approach. JRFM, 16(3), 1-13. https://ideas.repec.org/a/gam/jjrfmx/v16y2023i3p190-d1093692.html.
Eshaq, A. (2019). An analysis of technical indicators on gold price forecasting using RSI, SMA, Bollinger Bands, MACD, ATR and Ichimoku Cloud. Journal of Economics and Management, 15(2), 78–92.
Eskandari Sabzeh, S., & Haji Aghajani, A. (2023). The impact of the COVID-19 pandemic on the stock market: A case study of selected OPEC member countries. Journal of Development and Capital, 8(2), 151-168. DOI: 10.22103/jdc.2022.19657.1259 [In Persian].
Fang, L., Chen, B., Yu, H., & Qian, Y. (2017). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach. Journal of Futures Markets, 38, 413–422. https://doi.org/10.1002/fut.21897.
Ferrer, R., Bolos, V.J., & Benitez, R. (2016). Interest rate changes and stock returns: A European multi-country study with wavelets. International Review of Economics and Finance, 44, 1–12. DOI: 10.1016/j.iref.2016.03.001.
Frankel, J., & Rose, A.K. (2021). Determinants of agricultural and mineral commodity prices. Cambridge: Harvard University, John F. Kennedy School of Government. https://www.researchgate.net/publication/46396548.
Gorgbandi, S., & Mousavi, S.M. (2023). Short-term forecasting of gold prices in the forex market using deep neural networks. In Sixth National Conference on Novel Technologies in Electrical Engineering and Computer Science, Isfahan. https://civilica.com/doc/1876625 [In Persian].
Haddadi, M.R., Nadimi, Y., & Farahani, H. (2020). Forecasting the trend of global gold prices using marginal distribution modeling: An application of GARCH copula models. Financial Engineering and Securities Management, 11(42), 67-88. https://sanad.iau.ir/Journal/fej/Article/1077882 [In Persian].
Hansun, S., & Suryadibrata, A. (2021). Gold price prediction in COVID-19 era. International Journal of Computational Intelligence and Control, 13, 29–34. https://www.researchgate.net/publication/35668367.
Hashim, S.L. (2022). Analyses of factors influencing the price of gold in Malaysia. Advanced International Journal of Business, Entrepreneurship and SMEs, 4(11), 16–22. https://doi.org/10.35631/aijbes.411002.
Hatamlou, A., & Deljavan, M. (2019). Forecasting gold price using data mining techniques by considering new factors. Journal of AI and Data Mining, 7(3), 411-420. https://jad.shahroodut.ac.ir/article_1262.html.
Heidari Eshtirnani, S., Kouchiani, R., & Khorsandzak, M. (2022). Examining the dynamic relationship between Bitcoin and stock indices, gold, and the U.S. dollar in Iran: An application of the co-movement approach and wavelet analysis. Journal of Development and Capital, 7(2), 91-109. DOI: 10.22103/jdc.2022.19251.1224 [In Persian].
Heidari, F., Nadri, K., & Haji, G.A. (2023). Investigating factors affecting housing prices with an emphasis on non-performing loans from banks (VAR-TVPDMA approach). Journal of Development and Capital, 8(2), 91-112. DOI: 10.22103/jdc.2023.21279.1384. [In Persian].
Hoeting, J.A., Madigan, D., Raftery, A.E., & Volinsky, C.T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. https://www.stat.colostate.edu/~jah/papers/statsci.pdf.
Hosseini, H., & Namaki, A. (2023). Forecasting gold prices using long short-term memory (LSTM) networks. In First National Conference on Intelligent Economy and Financial Development, Tehran. https://civilica.com/doc/1816468 [In Persian].
Hu, X. (2021). A comprehensive analysis of technical indicators in gold price forecasting: Focusing on RSI, SMA, Bollinger Bands, MACD, ATR, and Ichimoku Cloud. Journal of Financial Studies, 18(4), 112–130.
Huseynli, N. (2023). Analyzing the relationship between oil prices and gold prices before and after COVID-19. International Journal of Energy Economics and Policy, 13(2), 373-378. DOI: 10.32479/ijeep.13820.
Kazemzadeh, E., Ebrahimi Salari, T., & Bahnameh, M. (2019). Forecasting the growth rate of gold coin prices in Iran using regression models with different frequencies (MIDAS). Applied Economics, 9(28), 43-53. https://www.sid.ir/paper/966993 [In Persian].
Khani, F., Jafari Samimi, A., Taheranchian, A.M., & Ehsani, M.A. (2021). The effects of the money market on the gold market using system dynamics approach. Scientific Economic Modeling Quarterly, 15(54), 1-19. https://www.sid.ir/paper/1061323 [In Persian].
Khonsarian, F., Timourpour, B., & Rastgar, M.A. (2023). Forecasting prices using LSTM artificial neural networks and portfolio selection of financial assets and cryptocurrencies. Financial Engineering and Securities Management, 14(57), 116-134. https://journals.iau.ir/article_703286.html [In Persian].
Koop, G., McIntyre, S., Mitchell, J., Poon, A., & Wu, P. (2024). Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 477–495. https://doi.org/10.1093/jrsssa/qnad130.
Lee, S.W., & Kim, H.Y. (2020). Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert Systems with Applications, 161, 113704. https://doi.org/10.1016/j.eswa.2020.113704.
Li, Y., Li, L., Zhao, X., et al. (2020). An attention-based LSTM model for stock price trend prediction using limit order books. Journal of Physics: Conference Series, 1575. https://doi.org/10.1088/1742-6596/1575/1/012124.
Liang, Y., Lin, Y., & Lu, Q. (2022). Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM. Expert Systems with Applications, 206, 117847. https://doi.org/10.1016/j.eswa.2022.117847.
Liu, T., Ma, X., Li, S., Li, X., & Zhang, C. (2022). A stock price prediction method based on meta-learning and variational mode decomposition. Knowledge-Based Systems, 252, 109324. https://doi.org/10.1016/j.knosys.-2022.109324.
Liya, A., Qin, Q., Kamran, H.W., Sawangchai, A., Wisetsri, W., & Raza, M. (2021). How macroeconomic indicators influence gold price management. Business Process Management Journal, 27(7), 2075-2087. https://doi.org/10.1108/BPMJ-12-2020-0579.
Long, P.D., Hien, B.Q., & Ngoc, P.T.B. (2022). Impacts of inflation on the gold price and exchange rate in Vietnam: Time-varying vs. fixed coefficient cointegrations. Asian Journal of Economics and Banking, 6(1), 88–96. https://doi.org/10.1108/ajeb-07-2021-0083.
Lu, W., Qiu, T., Shi, W., & Sun, X. (2022). International gold price forecast based on CEEMDAN and support vector regression with grey wolf algorithm. Complexity, 2022. https://doi.org/10.1155/2022/1511479.
Mainal, S.A., Mohd Selamat, A.H., Abd Majid, N.D.S., & Noorzee, K.N.I. (2023). Factors influencing the price of gold in Malaysia. Information Management and Business Review, 15(3), 195-205. https://doi.org/-10.22610/imbr.v15i3(I).3529.
Md Isa, M.A., A Latif, R., Nasrul, F., Zaharum, Z., & Noh, M.K.A. (2020). Relational study between macroeconomic variables and gold price: Latest Malaysian evidence. Advanced International Journal of Business, Entrepreneurship and SMEs, 2(6), 01–09. https://doi.org/10.35631/aijbes.2600.
Mohammadinejad Pashaki, M.B., Sadeqi Sharif, S.J., & Iqbalia, M. (2023). Examination and analysis of spillover effects between stock markets and gold, currency, oil, and housing markets: A VARMA-BEKK-AGARCH model. Financial Engineering and Securities Management, 14(57), 88-109. https://jfr.ut.ac.ir/article_92804.html [In Persian].
Nisarga, M. & Marisetty, N. (2023). A study on various factors impacting the gold price in India. Asian Journal of Economics, Business and Accounting, 23(20), 254-265. https://doi.org/10.2139/ssrn.4587897.
Plakandaras, V., Gogas, P., & Papadimitriou, T. (2021). Gold against the machine. Computational Economics, 57, 5–28. https://doi.org/10.1007/s10614-020-10019-z.
Pradeep, K.V., & Karunakaran, N. (2022). Gold price dynamics in India: A pre-post-liberalization comparison. Journal of Management Research and Analysis, 9(2), 102-107. https://doi.org/10.18231/j.jmra.2022.020.
Pukthuanthong, K., & Roll, R. (2011). Gold and the Dollar (and the Euro, Pound, and Yen). Journal of Banking & Finance, 35(8), 2070-2083. https://doi.org/10.1016/j.jbankfin.2011.01.014.
Qian, Y., Ding, J., Huang, W., & Zhang, H. (2022). Does political risk matter for gold market fluctuations? A structural VAR analysis. Research in International Business and Finance, 60, 101618. https://doi.org/-10.1016/j.ribaf.2022.101618.
Qian, Y., Ralescu, D., & Zhang, B. (2019). The analysis of factors affecting global gold price. Resources Policy, 64, 101478. https://doi.org/10.1016/j.resourpol.2019.101478.
Sadeghi, M. (2019). An investigation of technical indicators on gold price volatility: A case study of RSI, SMA, Bollinger Bands, MACD, ATR and Ichimoku Cloud. Quarterly Journal of Financial Research, 12(3), 45–60.
Sailaja, V.N., Kumar, A.K., & Kota, P.V.S. (2022). A study on macro-economic variables and their impact on gold price in India. International Journal of Development Research, 26(5), 1-16. https://www.abacademies.org/articles/-a-study-on-macro-economic-variables-and-their-impact-on-gold-price-in-india-15156.html.
Sarvaiya, D., & Ramchandani, D. (2022). Time series analysis and forecasting of gold price using ARIMA and LSTM model. International Journal for Research in Applied Science & Engineering Technology, 10(9), 168-173. DOI: 10.22214/ijraset.2022.46555.
Selvanathan, S., & Selvanathan, E. A. (2022). The nexus between oil and gold prices during the COVID-19 pandemic. In Financial Transformations Beyond the COVID-19 Health Crisis (pp. 447-474). Singapore: World Scientific Publishing Co. https://ideas.repec.org/h/wsi/wschap/9781800610781_0015.html.
Sims, C.A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48. https://www.jstor.org/stable/1912017.
Tanin, T.I., Sarker, A., Brooks, R., & Do, H.X. (2022). Does oil impact gold during COVID-19 and three other recent crises? Energy Economics, 108, 105938. DOI: 10.1016/j.eneco.2022.105938.
Torrence, C., & Compo, G.P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61-78. https://www.scirp.org/reference/referencespapers?referenceid=2214163.
Wen, J., Khalid, S., Mahmood, H., & Yang, X. (2022). Economic policy uncertainty and growth nexus in Pakistan: New evidence using NARDL model. Economic Change and Restructuring, 55(3), 1701–1715. https://doi.org/10.1007/s10644-021-09364-2.
Yan, W., & Elbushra, M. (2024). Forecasting Sudan gold prices with a hybrid deep learning approach. In 2024 International Conference on Cloud and Network Computing (ICCNC), Jinhua, China (pp. 71-78). https://doi.org/10.1109/ICCNC63989.2024.00020.
Yang, M. (2017). Effects of idiosyncratic shocks on macroeconomic time series. Empirical Economics, 53(4), 1441-1461. https://ideas.repec.org/a/spr/empeco/v53y2017i4d10.1007_s00181-016-1184-3.html.
Yuan, F., Lee, C., & Chiu, C. (2020). Using market sentiment analysis and genetic algorithm-based least squares support vector regression to predict gold prices. International Journal of Computational Intelligence Systems, 13(1), 234–246. DOI: 10.2991/ijcis.d.200214.002.
Zahedi, Y., Rezaei, N., & Najari, V. (2023). Measurement and testing for cross-bubble transmission in stock, currency, and gold markets: A case study of Iran using copula functions. Financial Engineering and Securities Management, 14(57). https://journals.iau.ir/article_700570.html [In Persian].
Zhang, G., Jiang, L., Tian, L., & Fu, M. (2021). Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks. The North American Journal of Economics and Finance, 57(3), 101437. DOI:10.1016/j.najef.2021.101437.
Zhang, P., & Ci, H. (2020). Deep belief network for gold price forecasting. Revue de la Politique Economique, 69(3), 1806-1830. DOI: 10.1016/j.resourpol.2020.101806.
Zhang, W., & Wei, Y. (2011). The dynamic relationship between crude oil and gold futures markets: Evidence from spillover effects and time‐varying correlations. Resources Policy, 35(3), 168-177. DOI: 10.1016/-j.resourpol.2010.05.003.
Zhao, Y. (2017). Evaluating the effectiveness of technical indicators in gold market analysis: Emphasis on RSI, SMA, Bollinger bands, MACD, ATR, and ichimoku cloud. International Journal of Economics and Finance, 9(1), 55–70.
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  • Receive Date: 04 August 2024
  • Revise Date: 22 January 2025
  • Accept Date: 03 May 2025

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