CAUSAL RELATIONSHIP BETWEEN CRYPTOCURRENCY RETURNS AND COMMODITY PRICES: A TIME SERIES ANALYSIS BASED ON INDIVIDUAL ASSETS

Yıl 2025, Cilt: 26 Sayı: 2, 371 - 386, 31.12.2025

Özgün Şanlı

https://doi.org/10.24889/ifede.1699328

Öz

In the recent period when digitalization has gained momentum and investors have been in search of alternative investment instruments, crypto assets have become an investment option that attracts interest in financial markets. With the increase in the trading volume of crypto assets, examining the factors affecting these assets and their returns has become important. The aim of this study is to examine the relationship between crypto assets and commodity markets. For this purpose, the relationship between the returns of eleven different crypto assets with high market capitalisation and the changes in the commodity index was examined using weekly data covering the period from July 2020 to April 2025. The relationship between the variables included in the research was analyzed using the Fourier Granger causality test. According to the analysis results, a unidirectional causality relationship was found between XRP, LEO, HBAR, LTC, and XMR and the DJCI. On the other hand, no causality relationship was found between BTC, ETH, BNB, SOL, TRX, and XLM and the DJCI. Based on the research findings, it can be stated that commodity markets have an effect on crypto assets; however, this effect varies on an asset basis.

Anahtar Kelimeler

Crypto Assets , Commodity Prices , Financial Markets , Fourier Granger Causality Test

KRİPTO VARLIK GETİRİLERİ İLE EMTİA FİYATLARI ARASINDAKİ NEDENSELLİK İLİŞKİSİ: VARLIK BAZLI BİR ZAMAN SERİSİ ANALİZİ

Öz

Dijitalleşmenin hız kazandığı ve yatırımcıların alternatif yatırım araçları arayışında olduğu son dönemde kripto varlıklar, finansal piyasalarda ilgi gören bir yatırım seçeneği haline gelmiştir. Kripto varlıkların işlem hacminin artması ile birlikte, söz konusu varlıklar ve getirilerini etkileyen faktörlerin incelenmesi önemli bir hal almıştır. Bu çalışmanın amacı, kripto varlıklar ve emtia piyasaları arasındaki ilişkiyi incelemektir. Bu amaçla, yüksek piyasa değerine sahip olan on bir farklı kripto varlığın getirileri ile emtia endeksindeki değişim arasındaki ilişki, Temmuz 2020 – Nisan 2025 dönemine ait haftalık veriler kullanılarak incelenmiştir. Araştırma kapsamına alınan değişkenler arasındaki ilişki Fourier Granger nedensellik testi ile incelenmiştir. Analiz sonuçlarında; XRP, LEO, HBAR, LTC ve XMR ile DJCI arasında tek yönlü nedensellik ilişkisi bulunmuştur. Diğer taraftan BTC, ETH BNB, SOL, TRX ve XLM değişkenleri ile DJCI arasında herhangi bir nedensellik ilişkisine rastlanmamıştır. Araştırma sonuçlarına dayanarak, emtia piyasalarının kripto varlıklar üzerinde etkili olduğu, ancak söz konusu etkinin varlık bazında farklılık gösterdiği söylenebilir.

Anahtar Kelimeler

Kripto Varlıklar , Emtia Fiyatları , Finansal Piyasalar , Fourier Granger Nedensellik Testi

Kaynakça

• Alahmad, M., Alshaıkhlı, I., Alkandarı, A., Alshehab, A., Islam, M. R., & Alnasheet, M. (2022). Influence of Hedera Hashgraph over Blockchain. Journal of Engineering Science and Technology, 17(5), 3475–3488.
• Altuntaş, S. T., & Çolak, F. D. (2015). BİST-100 endeksinde volatilitenin modellenmesi ve öngörülmesinde ARCH modelleri. İstanbul Üniversitesi İşletme Fakültesi İşletme İktisadı Enstitüsü Yönetim Dergisi, 26(79), 208–223.
• Aydoğdu, A. (2024). Hatemi-J yaklaşımı ile Bitcoin, emtia, dolar ve borsa endeksleri arasındaki nedensellik analizi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 63, 1–17. https://doi.org/10.30794/pausbed.1494042
• Aydoğdu, A. (2025). Risk şokları ve Türkiye’deki finansal varlıklar arasındaki yayılım etkisinin TVP-VAR dayalı wavelet uyum analizi ile incelenmesi. Ekonomi, Politika ve Finans Araştırmaları Dergisi, 10(1), 303–331. https://doi.org/10.30784/epfad.1595233
• Batırlık, S. N., Zeren, F., & Gençer, Y. G. (2023). Faiz oranı, döviz kuru ve ekonomik büyümenin otomobil satışları üzerindeki etkisinin incelenmesi: Türkiye örneği. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 862–869. https://doi.org/10.25287/ohuiibf.1270906
• Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409. https://doi.org/10.1111/j.1467-9892.2006.00478.x
• Bouazizi, T., Galariotis, E., Guesmi, K., & Makrychoriti, P. (2023). Investigating the nature of interaction between cryptocurrency and commodity markets. International Review of Financial Analysis, 88, 1-17. https://doi.org/10.1016/j.irfa.2023.102690
• Bouoiyour, J., & Selmi, R. (2015). What does Bitcoin look like? Annals of Economics and Finance, 16(2), 449–492. https://doi.org/10.13140/2.1.2839.8089
• Catalini, C., & Gans, J. S. (2020). Some simple economics of the blockchain. Communications of the ACM, 63(7), 80–90. https://doi.org/10.1145/3359552
• Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of Bitcoin price formation. Applied Economics, 48(19), 1799–1815.https://doi.org/10.1080/00036846.2015.1109038
• Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004
• Çelik, İ., Özdemir, A., Gürsoy, S., & Ünlü, H. U. (2018). Gelişmekte olan hisse senedi piyasaları ile kıymetli madenler arasındaki getiri ve volatilite yayılımı. Ege Akademik Bakış, 18(2), 217–230. https://doi.org/10.21121/eab.2018237351
• D’Amico, G., & Petroni, F. (2021). A micro‐to‐macro approach to returns, volumes and waiting times. Applied Stochastic Models in Business and Industry, 37(4), 767–789. https://doi.org/10.1002/asmb.2622
• Dias, A. (2013). Market capitalization and Value-at-Risk. Journal of Banking & Finance, 37(12), 5248–5260. https://doi.org/10.1016/j.jbankfin.2013.04.015
• Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, 85–92. https://doi.org/10.1016/j.frl.2015.10.008
• Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. https://doi.org/10.1111/j.1468-0084.2011.00662.x
• Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics & Econometrics, 20(4), 399–419. https://doi.org/10.1515/snde-2014-0101
• Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey–Fuller type unit root tests. Economics Letters, 117(1), 196–199. https://doi.org/10.1016/j.econlet.2012.04.081
• Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin—Asset or currency? Revealing users’ hidden intentions. Proceedings of the European Conference on Information Systems (ECIS 2014).
• Gujarati, D. N., & Porter, D. C. (2008). Basic Econometrics (5th ed.) [PDF]. McGraw Hill Education.
• Harvey, D. I., Leybourne, S. J., & Xiao, B. (2008). A powerful test for linearity when the order of integration is unknown. Studies in Nonlinear Dynamics & Econometrics, 12(3). https://doi.org/10.2202/1558-3708.1582
• Hasan, M., Naeem, M. A., Arif, M., Shahzad, S. J. H., & Vo, X. V. (2022). Liquidity connectedness in cryptocurrency market. Financial Innovation, 8(1), 1-25. https://doi.org/10.1186/s40854-021-00308-3
• Hassan, M. K., Hasan, M. B., & Rashid, M. M. (2021). Using precious metals to hedge cryptocurrency policy and price uncertainty. Economics Letters, 206, 1-5. https://doi.org/10.1016/j.econlet.2021.109977
• Krištoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLOS ONE, 10(4), 1-15. https://doi.org/10.1371/journal.pone.0123923
• Lin, M. Y., & An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74, 1-7. https://doi.org/10.1016/j.resourpol.2021.102383
• Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and cryptocurrency technologies: A comprehensive introduction. Princeton University Press.
• Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610
• Okorie, D. I., & Lin, B. (2020). Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy. Energy Economics, 87, 1-10. https://doi.org/10.1016/j.eneco.2020.104703
• Padmavathi, M., & Suresh, R. M. (2019). Secure P2P intelligent network transaction using Litecoin. Mobile Networks and Applications, 24(2), 318–326. https://doi.org/10.1007/s11036-018-1044-9
• Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, 235–240. https://doi.org/10.1016/j.frl.2018.03.016
• Pessa, A. A. B., Perc, M., & Ribeiro, H. V. (2023). Age and market capitalization drive large price variations of cryptocurrencies. Scientific Reports, 13(1), 1-12. https://doi.org/10.1038/s41598-023-30431-3
• Samırkaş, M. C., & Komşu, M. S. (2022). Emtia piyasalarında fiyat balonları: Covid-19 dönemi için bir inceleme. International Journal of Disciplines in Economics & Administrative Sciences Studies, 8(44), 575–586. https://doi.org/10.29228/ideas.63952
• Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. Review of the Federal Reserve Bank of St. Louis, 103(2), 153–174. https://doi.org/10.20955/r.103.153-74
• Shaik, M., Rabbani, M. R., Atif, M., Aysan, A. F., Alam, M. N., & Kayani, U. N. (2024). The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian–Ukraine war. PLOS ONE, 19(2), 1-16. https://doi.org/10.1371/journal.pone.0286963
• Sohan, S. V. P. (2024). Unveiling the veil: An in-depth review of Monero’s anonymous ecosystem. Journal of Emerging Technologies and Innovative Research (JETIR), 11(2), 351–354.
• Srinivasan, P., Maity, B., & Krishna Kumar, K. (2022). Macro-financial parameters influencing bitcoin prices: Evidence from symmetric and asymmetric ARDL models. Review of Economic Analysis, 14(1), 143–175. https://doi.org/10.15353/rea.v13i3.3585
• Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54–74. https://doi.org/10.2469/faj.v68.n6.5
• White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934
• Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning, s. 349–352.
• Yılancı, V., & Tıraşoğlu, M. (2016). Türkiye’nin makroekonomik zaman serilerinin doğrusallığının testi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 1–16. https://doi.org/10.18074/cnuiibf.315
• Zhu, Y., Dickinson, D., & Li, J. (2017). Analysis on the influence factors of Bitcoin’s price based on VEC model. Financial Innovation, 3(1), 1-13. https://doi.org/10.1186/s40854-017-0054-0