Jeopolitik Risk ve Altın Getirileri Arasındaki İlişkinin Zaman-Frekans Analizi: Wavelet Yaklaşımı

Öz

Bu çalışmanın amacı, jeopolitik risk ile altın getirileri arasındaki ilişkinin zaman ve frekans boyutunda nasıl değiştiğini ortaya koymaktır. Bu doğrultuda, jeopolitik risk endeksi (GPR) ile altın getirilerine ait Ocak 2000-Mart 2026 dönemini kapsayan aylık veriler kullanılmıştır. Yöntem olarak, değişkenler arasındaki dinamik etkileşimi zaman ve frekans düzleminde incelemeye imkân tanıyan Wavelet Coherence (WTC) ve Cross Wavelet Transform (XWT) analizleri uygulanmıştır. Ayrıca, değişkenler arasındaki nedensellik ilişkisinin hem şokların yönüne hem de farklı zaman ufuklarına göre değerlendirilmesi amacıyla Hatemi-J asimetrik nedensellik testi ile Breitung-Candelon frekans nedensellik testinden yararlanılmıştır. Elde edilen bulgular, jeopolitik risk ile altın getirileri arasındaki ilişkinin sabit olmadığını ve zaman-frekans boyunca değişkenlik gösterdiğini ortaya koymaktadır. Özellikle kısa vadede güçlü ve anlamlı bir eş hareket tespit edilirken, orta vadede ilişkinin dönemsel olarak sürdüğü, uzun vadede ise zayıfladığı gözlemlenmiştir. Faz farkı bulguları, jeopolitik riskteki artışların çoğunlukla altın getirilerini önden etkilediğini göstermektedir. Buna karşılık, asimetrik ve frekans nedensellik testlerinden elde edilen sonuçlar, değişkenler arasında istatistiksel olarak anlamlı ve kalıcı bir nedensellik ilişkisinin bulunmadığına işaret etmektedir. Çalışmanın temel katkısı, söz konusu ilişkinin doğrusal olmayan, zamana bağlı ve çok boyutlu yapısını ortaya koyarak literatüre zaman-frekans perspektifinden yeni kanıtlar sunmasıdır. Sonuç olarak, altının jeopolitik belirsizlik dönemlerinde özellikle kısa vadede güvenli liman özelliği taşıdığı, ancak bu etkinin uzun vadede zayıfladığı belirlenmiştir.

Anahtar Kelimeler

• Jeopolitik risk
• Wavelet analizi
• zaman-frekans analizi
• Hatemi-J
• Breitung-Candelon
• finansal piyasalar

Time-Frequency Analysis of the Relationship Between Geopolitical Risk and Gold Returns: A Wavelet Approach

Öz

The aim of this study is to examine how the relationship between geopolitical risk and gold returns evolves across time and frequency dimensions. For this purpose, monthly data covering the period from January 2000 to March 2026 for the Geopolitical Risk Index (GPR) and gold returns are employed. Methodologically, Wavelet Coherence (WTC) and Cross Wavelet Transform (XWT) analyses are utilized to investigate the dynamic interaction between the variables in the time-frequency domain, while the Hatemi-J asymmetric causality approach together, with the Breitung–Candelon frequency-domain causality framework, is employed to examine causal interactions by considering both shock asymmetries and varying temporal horizons. The results indicate that the association between geopolitical risk and gold returns is dynamic rather than constant, exhibiting changes over time and across frequencies. In particular, a strong and statistically significant co-movement is identified in the short term, while the relationship persists periodically in the medium term and weakens in the long term. The phase difference results indicate that increases in geopolitical risk generally precede movements in gold returns. In contrast, the results obtained from the asymmetric and frequency-domain causality tests suggest the absence of a statistically significant and persistent causal relationship between the variables. The main contribution of the study is that it provides new evidence from a time–frequency perspective by revealing the nonlinear, time-varying, and multidimensional nature of the relationship. Overall, the findings indicate that gold serves as a safe haven during periods of geopolitical uncertainty, particularly in the short term, although this effect weakens over longer horizons.

Anahtar Kelimeler

• Geopolitical risk
• gold returns
• Wavelet analysis
• time-frequency analysis
• Hatemi-J
• Breitung-Candelon
• financial markets

Kaynakça

1. Adekoya, O. B., Oliyide, J. A., Yaya, O. S., & Al-Faryan, M. A. S. (2022). Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga. Resources Policy, 77, 102728. https://doi.org/10.1016/j.resourpol.2022.102728
2. Alola, A. A., & Uzuner, G. (2021). Testing the asymmetric causal nexus of housing-oil prices and pandemic uncertainty in four major economies. Environmental Science and Pollution Research, 28(43), 60550-60556. https://doi.org/10.1007/s11356-021-14992-2
3. Al-Yahyaee, K. H., Mensi, W., & Yoon, S.-M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234. https://doi.org/10.1016/j.frl.2018.03.017
4. Antonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest. International Review of Financial Analysis, 50, 1-26. https://doi.org/10.1016/j.irfa.2017.01.004
5. Bahmani-Oskooee, M., Chang, T., & Ranjbar, O. (2016). Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches. Economic Modelling, 56, 66-78. https://doi.org/10.1016/j.econmod.2016.03.002
6. Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398-1410. https://doi.org/10.1016/j.eneco.2010.05.015
7. Baur, D. G., & Lucey, B. M. (2010). Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold. Financial Review, 45(2), 217-229. https://doi.org/10.1111/j.1540-6288.2010.00244.x
8. Baur, D. G., & McDermott, T. K. J. (2016). Why is gold a safe haven? Journal of Behavioral and Experimental Finance, 10, 63-71. https://doi.org/10.1016/j.jbef.2016.03.002

9. Bouri, E., Demirer, R., Gupta, R., & Marfatia, H. A. (2019). Geopolitical Risks and Movements in Islamic Bond and Equity Markets: A Note. Defence and Peace Economics, 30(3), 367-379. https://doi.org/10.1080/10242694.2018.1424613
10. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198. https://doi.org/10.1016/j.frl.2016.09.025
11. Breitung, J., & Candelon, B. (2006). Testing for short- and long-run causality: A frequency-domain approach. Journal of Econometrics, 132(2), 363-378. https://doi.org/10.1016/j.jeconom.2005.02.004
12. Caldara, D., & Iacoviello, M. (2022a). Measuring Geopolitical Risk. American Economic Review, 112(4), 1194-1225. https://doi.org/10.1257/aer.20191823
13. Caldara, D., & Iacoviello, M. (2022b). Measuring Geopolitical Risk. American Economic Review, 112(4), 1194-1225. https://doi.org/10.1257/aer.20191823
14. Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). THE ECONOMETRICS OF FINANCIAL MARKETS. Macroeconomic Dynamics, 2(4), 559-562. https://doi.org/10.1017/S1365100598009092
15. Frikha, W., Brahim, M., Jeribi, A., & Lahiani, A. (2023). COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: A wavelet-based analysis. Journal of Business Analytics, 6(4), 255-275. https://doi.org/10.1080/2573234X.2023.2193224
16. Full article: Geopolitical Risks and Movements in Islamic Bond and Equity Markets: A Note. (t.y.). Geliş tarihi 21 Nisan 2026, gönderen https://www.tandfonline.com/doi/full/10.1080/10242694.2018.1424613
17. Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. https://doi.org/10.2307/1912791
18. Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447-456. https://doi.org/10.1007/s00181-011-0484-x
19. Mensi, W., Alomari, M., Vo, X. V., & Kang, S. H. (2023). Extreme quantile spillovers and connectedness between oil and Chinese sector markets: A portfolio hedging analysis. The Journal of Economic Asymmetries, 28, e00327. https://doi.org/10.1016/j.jeca.2023.e00327
20. Old Geopolitical Risk (GPR) Index. (t.y.). Geliş tarihi 21 Nisan 2026, gönderen https://www.matteoiacoviello.com/gpr2019.htm
21. Percival, D. B., & Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press.
22. R Gencay, F Selcuk, & B Whitcher. (2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Waves in Random Media, 12(3), 399. https://doi.org/10.1088/0959-7174/12/3/701
23. Reboredo, J. C., & Ugolini, A. (2020). Price spillovers between rare earth stocks and financial markets. Resources Policy, 66, 101647. https://doi.org/10.1016/j.resourpol.2020.101647
24. Tianjin University of Commerce, Lu, Z., Gozgor, G., Istanbul Medeniyet University, Huang, M., Chongqing Technolog y and Business University, Keung Lau, M. C., & The University of Huddersfield. (2020). The Impact of Geopolitical Risks on Financial Development: Evidence from Emerging Markets. Journal of Competitiveness, 12(1), 93-107. https://doi.org/10.7441/joc.2020.01.06
25. Torrence, C., & Compo, G. P. (1998). A Practical Guide to Wavelet Analysis. https://journals.ametsoc.org/view/journals/bams/79/1/1520-0477_1998_079_0061_apgtwa_2_0_co_2.xml
26. Zhang, Z., Bouri, E., Klein, T., & Jalkh, N. (2022). Geopolitical risk and the returns and volatility of global defense companies: A new race to arms? International Review of Financial Analysis, 83, 102327. https://doi.org/10.1016/j.irfa.2022.102327
27. Zhou, H., & Liang, C. (2025). Geopolitical risk and gold price bubbles. Review of Accounting and Finance, 24(3), 353-374. https://doi.org/10.1108/RAF-09-2024-0369