TY - JOUR T1 - Kahverengi kripto ve yeşil kripto paraların yatırımcı risk toleransı ile ilişkilisi: Yerli yatırımcılar bazında A-ARDL ve NARDL yöntemleri ile bir değerlendirme TT - Relationship of risk tolerance with brown and green cryptocurrencies: An examination with A-ARDL and NARDL methods for domestic investors AU - Çifçi, Gönül PY - 2025 DA - June Y2 - 2025 DO - 10.33707/akuiibfd.1551277 JF - Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi JO - KOCATEPEİİBFD PB - Afyon Kocatepe University WT - DergiPark SN - 1302-1966 SP - 130 EP - 153 VL - 27 IS - 1 LA - tr AB - Yapılan bu çalışmanın amacı kahverengi kripto ve yeşil kripto para fiyatlarının yerel yatırımcıların risk toleransları ile ilişkisini ortaya koymaktır. Bu amaç doğrultusunda yerel gerçek kişi ve yerel tüzel kişi risk toleransları çalışmanın bağımsız değişkenlerini oluşturmaktadır. Araştırmada değişkenler arasında simetrik ve asimetrik uzun dönem ilişkini sınamak her bir test yöntemi için dört farklı araştırma modeli oluşturulmuştur. Test sonuçlarna göre yerel yatırımcıların risk toleransları kripto para fiyatları arasında simetrik ilişki yoktur. Ancak, yerel gerçek kişi risk toleransı ile kahverengi kripto ve yeşil kripto fiyatları arasında ve yerel tüzel kişi risk toleransı ile kahverengi kripto ve yeşil kripto fiyatları arasında asimetrik eşbütünleşme ilişkileri bulunmaktadır. Bu sonuçlara göre kahverengi ve yeşil kripto para fiyatlarındaki değişimler yerel yatırımcıların risk toleransını etkilemektedir. Özellikle, yeşil kripto para olan Stellar ve Tron fiyatları belirtilen risk toleransları üzerinde asimetrik etkilere sahiptirler. KW - Kahverengi kripto para KW - Yeşil kripto para KW - Yerel yatırımcı risk toleransı KW - Augmented-ARDL eşbütünleşme testi N2 - The aim of this study is to reveal the relationship between brown and green cryptocurrency prices and domestic investors' risk tolerances. For that purpose, the risk tolerances of domestic individual investors and institutional investors were sorted as independent variables of the study. To examine long-run symmetric and asymmetric relationships of the dependent and independent variables, four different research models were created. The test results showed that the domestic investors’ risk tolerances don’t have a symmetric cointegration relationship with brown and green crypto prices. However, there are asymmetric cointegrations between domestic individual investor risk tolerance both with brown and green cryptocurrency prices and between domestic institutional investor risk tolerance both with brown and green cryptocurrency prices. According to those results, the changes in the brown and green cryptocurrency prices impact domestic investors’ risk tolerances. Stellar and Tron, which are green cryptocurrencies, especially have asymmetric effects on risk tolerance. CR - Aydoğan, B., Cayirli, O., & Vardar, G. (2024). Impact of Macroeconomics Factors on Cryptocurrency Pricing: Evidence from Bitcoin and Ethereum Markets. Computational Economics, 1-36. https://doi.org/10.1007/s10614-024-10804-0 CR - Bajwa, I. A. (2025). Reinvestment intentions in cryptocurrency: Examining the dynamics of risks and investor risk tolerance. Digital Business, 5(1), 100104. https://doi.org/10.1016/j.digbus.2024.100104 CR - Benzekri, M.K.& Özütler, H.Ş. (2021). Bitcoin fiyat hareketleri üzerine: ARIMA ile kısa vadeli bir fiyat tahmini. İktisat Politikası Araştırmaları Dergisi, 8(2), 293-309. http://dx.doi.org/10.26650/JEPR.946081 CR - Bouri, E., Gupta, R., Lau, M., & Roubaud, D. (2019). Risk aversion and Bitcoin returns in normal, bull, and bear markets (No. 201927). CR - Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17, 334-55. http://dx.doi.org/10.1111/j.1467-8454.1978.tb00635.x CR - Dilek, Ş., & Furuncu, Y. (2019). Bitcoin mining and its environmental effects. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 33(1), 91-106. CR - 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 CR - Esparcia, C., Fakhfakh, T., & Jareño, F. (2024). The green, the dirty and the stable: Diversifying equity portfolios by adding tokens of different nature. The North American Journal of Economics and Finance, 69, 102020. CR - Foley, S., Frijns, B., Garel, A., & Roh, T. Y. (2022). Who buys Bitcoin? The cultural determinants of Bitcoin activity. International Review of Financial Analysis, 84, 102385. https://doi.org/10.1016/j.irfa.2022.102385 CR - Gemici, E., Gök, R., & Bouri, E. (2023). Predictability of risk appetite in Turkey: Local versus global factors. Emerging Markets Review, 55, 101018. https://doi.org/10.1016/j.ememar.2023.101018 CR - Gerrans, P., Abisekaraj, S. B., & Liu, Z. F. (2023). The fear of missing out on cryptocurrency and stock investments: Direct and indirect effects of financial literacy and risk tolerance. Journal of Financial Literacy and Wellbeing, 1(1), 103-137. CR - Goodkind, A. L., Jones, B. A., & Berrens, R. P. (2020). Cryptodamages: Monetary value estimates of the air pollution and human health impacts of cryptocurrency mining. Energy Research & Social Science, 59, 101281. https://doi.org/10.1016/j.erss.2019.101281 CR - Göksu, S., & Balkı, A. (2023). ARDL ve NARDL eşbütünleşme analizleri: Adım adım E-views uygulaması. Serüven Yayınevi, Ankara-Türkiye CR - Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance, 25, 100271. https://doi.org/10.1016/j.jbef.2020.100271 CR - Haq, I. U. (2023). Time‐frequency comovement among green financial assets and cryptocurrency uncertainties. Economic Notes, 52(1), e12216. CR - Hayashi, F., & Routh, A. (2024). Financial Literacy, Risk Tolerance, and Cryptocurrency Ownership in the United States. Federal Reserve Bank of Kansas City Working Paper, (24-03). CR - Kılıç, M., & Altan, İ. M. (2023). Are green cryptocurrencies safe? Investigation of the green and non-green cryptocurrencies. Akademik Yaklaşımlar Dergisi, 14(2), 651-663. CR - Lashkaripour, M. (2023). How carbon is priced in cryptocurrencies. Available at SSRN 4560309. CR - Malladi, R.K., Dheeriya, P.L. (2021) Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45, 75–94. https://doi.org/10.1007/s12197-020-09526-4 CR - Meyer, J. H., Friederich, F., Matute, J., & Schwarz, M. (2024). My money—My problem: How fear‐of‐missing‐out appeals can hinder sustainable investment decisions. PsychoLy & Marketing. CR - Naeem, M. A., Nguyen, T. T. H., Karim, S., & Lucey, B. M. (2023). Extreme downside risk transmission between green cryptocurrencies and energy markets: the diversification benefits. Finance Research Letters, 58, 104263. https://doi.org/10.1016/j.frl.2023.104263 CR - Ögel, S., & Ögel, İ. Y. (2021). The interaction between perceived risk, attitude, and intention to use: An empirical study on bitcoin as a cryptocurrency. In new challenges for future sustainability and wellbeing (pp. 211-241). Emerald Publishing Limited. CR - Önk.H, & Saygın, O. (2022). Bitcoin, risk iştahı, BIST100 endeksi ilişkisi: Türkiye örneği. International Journal of Disciplines in Economics & Administrative Sciences Studies, 8(42), 419-427. https://doi.org/10.29228/ideas.62987 CR - Patel, R., Gubareva, M., & Chishti, M. Z. (2024). Assessing the connectedness between cryptocurrency environment attention index and green cryptos, energy cryptos, and green financial assets. Research in International Business and Finance, 70, 102339. https://doi.org/10.1016/j.ribaf.2024.102339 CR - Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. CR - Robba, M., Sorgente, A., & Iannello, P. (2024). Disentangling the “crypto fever”: An exploratory study of the psychoLical characteristics of cryptocurrency owners. Current Research in Behavioral Sciences, 6, 100151. CR - Sam, C. Y., McNown, R., & Goh, S. K. (2019). An augmented autoregressive distributed lag bounds test for cointegration. Economic Modelling, 80, 130-141. CR - Sridharan, U., Mansour, F., Ray, L. and Huning, T. (2023). Effect of risk attitude on cryptocurrency adoption for compensation and spending. Journal of Financial Economic Policy, 15(4/5), 337-350. https://doi.org/10.1108/JFEP-04-2023-0099 Syed, A. A., Ahmed, F., Kamal, M. A., Ullah, A., & Ramos-Requena, CR - J. P. (2022). Is there an asymmetric relationship between economic policy uncertainty, cryptocurrencies, and global green bonds? Evidence from the United States of America. Mathematics, 10(5), 720. CR - Tabachnick, B. G., & Fidell, L. S. (2015). Using multivariate statistics. (6.basım). Allyn & Bacon / Pearson Education. 6. Basımdan Çeviri: Mustafa Baloğlu, Nobel Akademik Yayıncılık, Ankara CR - Uçkun, N., & Dal, L. (2021). Kripto para yatırımcılarında finansal risk toleransı. Muhasebe ve Finansman Dergisi, (89), 155-170. https://doi.org/10.25095/mufad.852118 CR - Umar, Z., Usman, M., Umar, M., & Ktaish, F. (2024). Interdependencies and risk management strategies between green cryptocurrencies and traditional energy sources. Energy Economics, 136, 107742. https://doi.org/10.1016/j.eneco.2024.107742 CR - Ye, W., Wong, W. K., Arnone, G., Nassani, A. A., Haffar, M., & Faiz, M. F. (2023). Crypto currency and green investment impact on global environment: A time series analysis. International Review of Economics & Finance, 86, 155-169. https://doi.org/10.1016/j.iref.2023.01.030 CR - Yousaf, I., Abrar, A., Yousaf, U. B., & Goodell, J. W. (2024). Environmental attention and uncertainties of cryptocurrency market: Examining linkages with crypto-mining stocks. Finance Research Letters, 59, 104672. https://doi.org/10.1016/j.frl.2023.10467 UR - https://doi.org/10.33707/akuiibfd.1551277 L1 - https://dergipark.org.tr/en/download/article-file/4219542 ER -