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The Role of the Relationship between the Price Dynamics of Precious Metals (Gold and Silver) in Managing Operational Risks in the Forex Market through the Support of the Hawks Foundation for the Period (2014-2024)
This research aims to study the role of the relationship between the dynamics of precious metal price changes in the Forex market, supported by the Hawks process, in economic growth over the period (2014-2024). The study utilizes the Hawks model, which is used to evaluate events related to price patterns and spikes. It also aims to provide managers and decision-makers with sufficient knowledge and the ability to understand market shifts and imbalances through an operations research model linked to the Hawks process. In this way, liquidity and economic growth can be identified. Furthermore, the analysis showed that gold and silver interact differently with each other. Additional analysis revealed that silver has a higher level of self-stimulation compared to gold, meaning that silver price spikes last longer than gold price spikes. Moreover, it was found that price shocks in silver affect the price of gold 1.19 times, while the impact of gold price spikes on silver was minimal. This will help capital decision-makers understand the nature of these two metals. It has been proven that during periods of financial crisis, including the COVID-19 pandemic, the prices of both metals rose, confirming their status as safe-haven assets. The study concluded that the price of silver precedes the price of gold, and that any change in the price of silver leads to changes in the price of.
Gold
Silver
Hawkes Process
Forex
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Adekoya, O., Abakah, E., Oliyide, J., & Gil‐Alana, L. (2023). Factors behind the performance of green bond markets. International Review of Economics & Finance, 88, 92–106. https://doi.org/10.1016/j.iref.2023.03.006
-
Akhilesh Prasad, Priti Bakhshi, A. Seetharaman (2022). The Impact of the U.S. Macroeconomic Variables on the CBOE VIX Index. Journal of risk and financial management, 15, 126-126. https://doi.org/10.3390/jrfm15030126
-
Bacry, E., Mastromatteo, I., & Muzy, J. (2015). Hawkes processes in finance. Market Microstructure and Liquidity, 1(01), 1550005. https://doi.org/10.1142/S2382626615500057
-
Bonin, H. (2007). French banks in Hong Kong (1860s–1950s): Challengers to British banks?. Business History, 49(3), 337–360. https://doi.org/10.1080/00076790701248351
-
Boxiao Wang. (2024). The relationship between silver prices and consumer price index: Evidence on China market. Finance & Economics,2(9),1-5. https://doi.org/10.61173/0h79ca52
-
Cocco, L., Marchesi, M., & Tonelli, R. (2022). Bitcoin as safe haven during COVID-19 disease. Finance Research Letters, 46, 102455. https://doi.org/10.1016/j.frl.2021.102455
-
Cohen, G. (2022). Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies. Mathematics, 10(18), 3302. https://doi.org/10.3390/math1018330
-
Corbet, S., Larkin, C., Lucey, B. M., Meegan, A., & Yarovaya, L. (2020). The impact of macroeconomic news on Bitcoin returns. European Journal of Finance, 26(15), 1396–1416. https://doi.org/10.1080/1351847X.2019.1704011
-
Drożdż, S., Kwapień, J., Oświęcimka, P., & Stanisz, T. (2020). Complexity in economic and social systems: Cryptocurrency market at around COVID-19. Entropy, 22(9), 1043. https://doi.org/10.3390/e22091043
-
Drożdż, S., Kwapień, J., Oświęcimka, P., Stanisz, T., & Wątorek, M. (2020). Complexity in economic and social systems: Cryptocurrency market at around COVID-19. Entropy, 22(9), 1043. https://doi.org/10.3390/e22091043
-
Ghosh, A. (2012). Operational risk management framework. In Editor Name (Ed.), Book title (pp. 387–408). Wiley. https://doi.org/10.1002/9781119199250.ch22
-
Jain, D., & Mandot, N. (2012). Impact of demographic factors on investment decision of investors in Rajasthan. Researchers World, 3(2), 81–92.
-
Hawkes, A. (1971). Spectra of some self exciting and mutually exciting point processes. Biometrika, 58(1), 83–90. https://doi.org/10.1093/biomet/58.1.83
-
Khamis, M. S.,& Aassouli, D. (2023). The eligibility of green bonds as safe haven assets: A systematic review. Sustainability, 15(8), 6841. https://doi.org/10.3390/su15086841
-
Li, X. (2022). Dynamic changes in prices of gold and silver during Russia Ukraine conflict. BCP Business & Management, 31, 318–326. https://doi.org/10.54691/bcpbm.v31i.2646
-
Matha, R., & Raghavendra, S. (2022). Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India. Investment Management and Financial Innovations, 19(4), 1–15. https://doi.org/10.21511/imfi.19(4).2022.01
-
Patil, S., Poddar, P., & Vasava, P. (2016). Gold market analyzer using selection based algorithm. International Journal of Advanced Engineering Research and Science (IJAERS), 3(4),99-102.
-
Rakesh, H. (2014). A study on retail investors behavior. EXCEL International Journal of Multidisciplinary Management Studies, 4(4), 67–75. https://www.semanticscholar.org/paper/f742c1c0cc59ccde5a799778e79f9bffc763e91a
-
Sahadudheen, I., & Kumar, P. S. (2023). Time-varying relationship and volatility spillovers among oil, gold, forex and stock markets in Indian context: The juxtaposition of global economic crisis and COVID-19 pandemic. The Indian Economic Journal, 71(4), 748–767.
-
Sharma, P. (2014). Analysis of retail investors’ behaviour in mutual funds market in Rajasthan. Emerging Markets: Finance eJournal, https://www.semanticscholar.org/paper/ff6f8b4916db77425ae928ab7924d8dbf5a85fbb
-
Sornmayura, S. (2019). Robust FOREX trading with Deep Q Network (DQN). ABAC Journal, 39(1), 15–33.
-
Togba, E., & Ural, M. (2024). Volatility spillovers between financial asset and commodity prices: Evidence from Türkiye. Uluslararası Sosyal ve Ekonomik Çalışmalar Dergisi, 5(2),219- 234.
-
Xu, Y., Zhu, L., & Pinedo, M. (2020). Operational risk management: A stochastic control framework with preventive and corrective controls. Operations Research, 68(6), 1804–1825. https://doi.org/10.1287/opre.2020.1982
-
Yadav, M. P., Abedin, M. Z., Sinha, N., & Arya, V. (2023). Uncovering dynamic connectedness of artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine invasion. Research in International Business and Finance, 67, 102146. https://doi.org/10.1016/j.ribaf.2023.102146
