Endeks Modeli Kripto Para Birimlerinde Kullanılabilir mi? Geleneksel Yöntemlerden ve Sayısal Simülasyondan Elde Edilen Kanıtlar

Yıl 2025, Cilt: 10 Sayı: 4, 1399 - 1418, 31.12.2025

Muhammed Samancı , Emrah Noyan , Zeynep Öztürk Yaprak

https://doi.org/10.30784/epfad.1706657

Öz

Çalışma, kripto varlıklar için üç farklı endeks modelini karşılaştırarak yatırımcılara risk yönetiminde yeni yöntemler sunmayı ve literatüre bu alanda katkı sağlamayı amaçlamaktadır. Bu amaç doğrultusunda, endeks modellerinin piyasa volatilitesini tahmin etme doğruluğunu belirlemek için geleneksel ağırlıklandırma, standartlaştırma ve sayısal simülasyon yöntemleri kullanılarak üç farklı endeks hesaplanmıştır. Araştırmanın kapsamı, en yüksek işlem hacmine sahip yedi kripto varlık ile kontrol grubu olarak seçilen üç farklı kripto varlıkla sınırlıdır. Çalışmanın kapsamı yalnızca endeks ile volatilite arasındaki ilişkiyi incelemekle sınırlandırılmıştır. Bununla beraber endeksin tahmin edilmesi veya endeksi etkileyebilecek diğer değişkenlerin analizi çalışmanın gelişime açık alanlarıdır. Hesaplanan endeksler üzerinden elde edilen bulgular, geleneksel ağırlıklandırma modelinin piyasa volatilitesini %65 doğrulukla tahmin ettiğini, standartlaştırılmış endeksin ise bu oranı %78’e yükselttiğini göstermektedir. Buna karşın, sayısal simülasyonun yalnızca %42 doğruluk sağlaması, literatürdeki yerleşik varsayımların sorgulanmasına neden olmaktadır. Bu sonuçlar, özellikle kripto yatırımcılarının risk değerlendirme süreçlerinde önemli bir dönüşüm potansiyeli taşımaktadır. Endeks tabanlı bu yaklaşım, piyasa şeffaflığını artırırken düzenleyici kurumlar için yeni bir gözetim çerçevesi sunmaktadır. Çalışmanın politika yapıcılar açısından en önemli çıkarımı ise, kripto piyasalarında veriye dayalı düzenlemelerin teknik olarak mümkün olduğunu kanıtlamasıdır.

Anahtar Kelimeler

Kripto Paralar , Risk Düzeyi , Sayısal Simülasyon

Can the Index Model Be Used in Cryptocurrencies? Evidence from Traditional Methods and Numerical Simulation

Öz

The study aims to compare three different index models for crypto assets, offering investors new methods for risk management and contributing to the literature in this field. To this end, three different indices were calculated using traditional weighting, standardization, and numerical simulation methods to determine the accuracy of these models in predicting market volatility. The scope of the study is limited to seven crypto assets with the highest trading volumes and three additional crypto assets as a control group. The study is confined to examining the relationship between the index and volatility. Furthermore, analysis of index forecasting or the impact of other variables on the index remains an area for future development. The findings based on the calculated indices show that the traditional weighting model predicts market volatility with 65% accuracy, while the standardized index increases this rate to 78%. In contrast, the numerical simulation achieves only 42% accuracy, calling into question established assumptions in the literature. These results carry significant transformative potential for crypto investors’ risk assessment processes. The index-based approach enhances market transparency and provides regulatory bodies with a new supervisory framework. The vital implication for policymakers is that data-driven regulation in crypto markets is now technically feasible.

Anahtar Kelimeler

Cryptocurrencies , Risk Level , Numerical Simulation

Kaynakça

• Ağaçkesen, E. (2022). Examining the mutual relationship between Bitcoin and stock markets: Turkey and selected countries. Journal of Social, Humanities and Administrative Sciences, 5(4), 419-431. doi:10.26677/TR1010.2022.969
• Akçalı, B.Y. and Şişmanoğlu, E. (2019). Analysis of the relationship between cryptocurrencies with Toda-Yamamoto causality test. EKEV Academy Journal, 78, 99-122. Retrieved from https://dergipark.org.tr/en/pub/sosekev
• Akkuş, H.T. (2023). Examining the relationships between gold and cryptocurrency markets with non-linear models: The case of Bitcoin and Ethereum. Balikesir University The Journal of Social Sciences Intitute, 26(50), 617-636. doi: 10.31795/baunsobed.1350805
• Aksoy, E., Teker, T., Mazak, M. and Kocabıyık, T. (2020). An analysis on cryptocurrencies and price relations: A review by Todayamamoto causality test. Journal of Süleyman Demirel University Institute of Social Sciences, 37, 110-129. Retrieved from https://dergipark.org.tr/en/pub/sbe
• Amsyar, I., Christopher, E., Dithi, A., Khan, A.N. and Maulana, S. (2020). The challenge of cryptocurrency in the era of the digital revolution: A review of systematic literature. Aptisi Transactions on Technopreneurship (ATT), 2(2), 153-159. https://doi.org/10.34306/att.v2i2.96
• Atabey, A.Ö. and Karakuş, M. (2022). Analysis of the relationship between cryptocurrency market and Borsa Istanbul: Toda-Yamamoto causality test. Five Zero, 2(2), 97-110. doi:10.54486/fivezero.2022.18
• Ay, M. and Adıyaman, G. (2022). Examining the relationship between Bitcoin and altcoins. The Journal of Selcuk University Social Sciences Institute, 47, 31-46. https://doi.org/10.52642/susbed.1010149
• Bariviera, A.F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4. https://doi.org/10.1016/j.econlet.2017.09.013
• Bozdoğanoğlu, B. (2023). Blockchain technology and its usability in public entities: Country examples and assessment of Türkiye. Journal of Turkish Court of Accounts, 130, 355-385. Retrieved from https://research.ebsco.com/
• Büberkökü, Ö. (2021). Examining the return and volatility spillovers among cryptocurrencies. Çağ Üniversity Journal of Social Sciences, 18(2), 1-16. https://doi.org/10.3390/jrfm14100493
• Canh, N.P., Wongchoti, U., Thanh, S.D. and Thong, N.T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH Model. Finance Research Letters, 29, 90-100. https://doi.org/10.1016/j.frl.2019.03.011
• Çetinkaya, Ş. (2018). Development of crypto coins and investigation of the place in money markets by SWOT analysis. Journal of International Economics and Politics Sciences Academic Research, 2(5), 11-21. doi:10.17261/Pressacademia.2021.1491
• Ceylan, M.E. (2019). Bitcoin economy: The impact of bitcoin on the finance sector (Master thesis). Batman University, Batman, Türkiye.
• Ceylan, O. and Isık, A.H. (2023). Blockchain technology and application areas. Uluborlu Journal of Vocational Sciences, 6(1), 129-154. Retrieved from https://dergipark.org.tr/tr/pub/umbd/
• Chen, B.X. and Sun, Y.L. (2024). Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework. The North American Journal of Economics and Finance, 69, 1-19, 102036. https://doi.org/10.1016/j.najef.2023.102036
• Chowdhury, R., Rahman, M.A., Rahman, M.S. and Mahdy, M.R.C. (2020). An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Physica A: Statistical Mechanics and its Applications, 551, 1-17, 124569. https://doi.org/10.1016/j.physa.2020.124569
• Chu, J., Chan, S., Nadarajah, S. and Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17. https://doi.org/10.3390/jrfm10040017
• CoinGecko. (2024). Q2 crypto industry report. Retrieved from https://www.coingecko.com/research/publications/2024-q2-crypto-report
• Coinmarket. (2024). Top 100 crypto coins by market capitalization, 2024. Retrieved from https://coinmarketcap.com
• Dai, W. (1998). B-money. Retrieved from https://bitcoinstan.io/prehistory
• Doğan, Ş. (2020). Transformation of money in digital age: Cryptocurrency and blockchain technology: A research for university students. Journal of Social Sciences of Mus Alparslan University, 8(3), 859-870. https://doi.org/10.18506/anemon.647019
• Dyhrberg, A.H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139-144. https://doi.org/10.1016/j.frl.2015.10.025
• Eğilmez, M. (2013). Bitcoin. Retrieved from https://www.mahfiegilmez.com
• European Central Bank. (2012). Virtual currency schemes. Retrieved from https://www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemes201210en.pdf
• Gökalp, B.T. (2022). The effects of cryptocurrency market on Borsa Istanbul indices. Journal of Research in Economics Politics and Finance, 7(2), 481-499. doi:10.30784/epfad.1081705
• Gubadlı, M. and Sarıkovanlık, V. (2023). Test of volatility behaviors on the crypto currency market with the asymmetric stochastic volatility model. International Journal of Management Economics and Business, 19(1), 61-82. https://doi.org/10.17130/ijmeb.1175863
• Gümüş, A. and Erkuş, H. (2019). An assessment on the use of Blockchain and cryptocurrency. Journal of Social Sciences of Mus Alparslan University, 7(2), 41-49. https://doi.org/10.18506/anemon.427976
• Gürgüç, Z. and Konttenbelt, W. (2018). Cryptocurrencies: Overcoming barriers to trust and adoption. Retrieved from https://www.doc.ic.ac.uk/~wjk/publications/gurguc-knottenbelt-etoro-2018.pdf
• Häusler, K. and Xia, H. (2022). Indices on cryptocurrencies: an evaluation. Digital Finance, 4(2), 149-167. https://doi.org/10.1007/s42521-022-00048-8
• Henriques, I. and Sadorsky, P. (2018). Can Bitcoin replace gold in an ınvestment portfolio? Journal of Risk and Financial Management, 11(3), 48. https://doi.org/10.3390/jrfm11030048
• Hossain, M.S. (2021). What do we know about cryptocurrency? Past, present, future. China Finance Review International, 11(4), 552-572. https://doi.org/10.1108/CFRI-03-2020-0026
• İçellioğlu, C.Ş. and Öztürk, M.B.E. (2018). In search of the relationship between Bitcoin and selected exchange rates: Johansen test and Granger causality test for the period 2013-2017. Journal of Finance Letters, 1(109), 51-70. Retrieved from https://ideas.repec.org/
• Jareño, F., González, de la O.M., Tolentino, M. and Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 1-14, 101666. https://doi.org/10.1016/j.resourpol.2020.101666
• Kahraman, M.E. (2022). Widespread virtual life with blockchain, Deepfake, avatar, cryptocurrency, NFT and metaverse. International Journal of Cultural and Social Research, 8(1), 149-162. https://doi.org/10.46442/intjcss.1106228
• Kaplanhan, F. (2018). The analysis of crypto-currency with respect to Turkish tax legislation: Bitcoin example. Journal of Tax Issues, 353, 105-123. Retrieved from https://openaccess.izu.edu.tr/
• Kara, A. and Demireli, E. (2023). Cointegration and causality analysis between bitcoin and selected altcoins in the cryptocurrency market. Journal of Business Economics and Management Research, 6(1), 52-71. https://doi.org/10.33416/baybem.1190625
• Karyağdı, N.G. and Yolci, M. (2023). The concept and control of crypto money. Turkish Business Journal, 4(7), 1-13. https://doi.org/10.51727/tbj.1221264
• Kaya, M. (2025). Analysis of volatility spillover between cryptocurrencies: Evidence from high-cap cryptocurrencies. Hitit Journal of Social Sciences, 18(1), 18-38. https://doi.org/10.17218/hititsbd.1555090
• Kazova, F. and Ercan, A.B. (2021). Comparative analysis of the volatility structure of cryptocurrencies. EKOIST Journal of Econometrics and Statistics, 35, 33-57. https://doi.org/10.26650/ekoist.2021.36.984568
• Kesebir, M. and Günceler, B. (2019). The sparking future of crypto currencies. Iğdır University Journal of Social Sciences, 17, 605-626. Retrieved from https://dergipark.org.tr/en/pub/igdirsosbilder
• Khuntia, S. and Pattanayak, J.K. (2018). Adaptive market hypothesis and evolving predictability of Bitcoin. Economics Letters, 167, 26-28. https://doi.org/10.1016/j.econlet.2018.03.005
• Kim, M. J., Canh, N.P. and Park, S.Y. (2021). Causal relationship among cryptocurrencies: A conditional quantile approach. Finance Research Letters, 42, 1-8, 101879. https://doi.org/10.1016/j.frl.2020.101879
• Klein, T., Thu, H.P. and Walther, T. (2018). Bitcoin is not the new gold - A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105-116. https://doi.org/10.1016/j.irfa.2018.07.010
• Koç, S. and Çaykara, U. (2021). An empirical application on the causality relationship between selected crypto coins and exchange rates (2015-2019). International Journal of Economic and Administrative Academic Research, 1(2), 113-134. Retrieved from https://dergipark.org.tr/tr/pub/ijerdergisi/
• Konakçi, A.R. (2018). Bitcoin pioneer Jeff Garzik discusses Bitcoin’s evolution and Satoshi’s true identity. Retrieved from https://koinbülteni.com
• Kurihara, Y. AND Fukushima, A. (2017). The market efficiency of Bitcoin: A weekly anomaly perspective. Journal of Applied Finance and Banking, 7(3), 57. Retrieved from https://www.scienpress.com/
• Macit, D. (2022). The economic effects and policy recommendations for the future of cryptocurrencies. Journal of Applied and Theoretical Social Sciences, 4(1), 19-38. Retrieved from https://research.ebsco.com/c
• Mendi, A.F. (2021). The improvement needs in blockchain technology. European Journal of Science and Technology, 29, 6-10. https://doi.org/10.31590/ejosat.1009560
• Milutinović, M. (2018). Cryptocurrency. Ekonomika, Journal for Economic Theory and Practice and Social Issues, 64(1), 105-122. doi:10.22004/ag.econ.290219
• Münyas, T. and Aydın, G.K. (2023). Etkin piyasa hipotezi ve kripto para piyasaları üzerine bir uygulama. Alanya Akademik Bakış, 7(3), 1203-1216. https://doi.org/10.29023/alanyaakademik.1240173
• Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://assets.pubpub.org/d8wct41f/31611263538139.pdf
• Nikolic, I., Kolluri, A., Sergey, I., Saxena, P. and Hobor, A. (2018). Finding ther greedy, prodigal, and suicidal contracte at acale. Papers presented at the ACSAC '18: 34th Annual Computer Security Applications Conference, Puerto Rico, USA. Retrieved from https://arxiv.org/pdf/1802.06038
• Öget, E. and Kanat, E. (2018). An analysis of long and short term relationships between Bitcoin and Turkey and G7 country stock exchanges. Research of Financial Economic and Social Studies (RFES), 3(3), 601-614. doi: 10.29106/fesa.422113
• Onay, A. (2018). Accounting of crypto-currencies and an evaluation on the impact of crypto-currency on the internal control of enterprises. In Academic studies in economic sciences (pp. 469-492). Ankara: Gece Kitaplığı.
• Özdemir, L. (2025). The comparison of cryptocurrency volatility levels with the asymmetric GARCH model. Academic Review of Economics and Administrative Science, 18(1), 493-509. https://doi.org/10.25287/ohuiibf.1600186
• Özkan, O. and Şahin, E.E. (2020). Etkin piyasalar hipotezi kapsamında kripto paraların zayıf form bilgisel etkinliklerinin karşılaştırılması. Turkish Studies-Economics, Finance, Politics, 15(4), 2393-2406. Retrieved from https://turkishstudies.net/economics-finance-politics
• Özpençe, Ö. and Noyan, E. (2022). Blockchain technology and applications, “digitalisation of economic activities and metaverse”. Ankara: Nobel Publishing.
• Polat, M. and Karakaya, O. (2023). Kripto paralar arasında getiri ve risk açısından nedensellik ilişkisi. Bartın Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(27), 1-17. https://doi.org/10.47129/bartiniibf.1169216
• Şahin, M. (2024). Cryptocurrency and surveillance: Constructing the cashless society. Bitlis Eren Journal of Social Research, 2(1), 65-79. Retrieved from https://dergipark.org.tr/en/pub/bersad/
• Serbest, D. (2023). Factors affecting consumers choice of crypto currency exchanges (Master thesis). Marmara University, İstanbul, Türkiye.
• Shah, A., Chauhan, Y. and Chaudhury, B. (2021). Principal component analysis based construction and evaluation of cryptocurrency ındex. Expert Systems with Applications, 163, 1-11, 113796. https://doi.org/10.1016/j.eswa.2020.113796
• Swartz, L. (2018). What was Bitcoin, what will it be? The techno-economic imaginaries of a new money technology. Cultural Studies, 32(4), 623-650. https://doi.org/10.1080/09502386.2017.1416420
• Szabo, N. (2005). Bit gold. Retrieved from https://nakamotoinstitute.org/library/bit-gold/
• Teichmann, F.M.J. and Falker, M.C. (2020). Money laundering via cryptocurrencies–potential solutions from Liechtenstein. Journal of Money Control, 24(1), 91-101. doi:10.1108/JMLC-04-2020-0041
• Tevetoğlu, M. (2021). Ethereum and the smart conracts. Inonu university Law Review, 12(1), 193-208. https://doi.org/10.21492/inuhfd.852860.
• Trimborn, S. and Härdle, W.K. (2018). CRIX an index for cryptocurrencies. Journal of Empirical Finance, 49, 107-122. https://doi.org/10.1016/j.jempfin.2018.08.004
• Turan, Z. (2018). Crypto money, Bitcoin, blockchain, petro gold, digital money and their usage areas. Academic Review of Economics and Administrative Sciences, 11(3), 1-5. Retrieved from https://dergipark.org.tr/tr/pub/ohuiibf
• Wang, X., Chen, X. and Zhao, P. (2020). The relationship between bitcoin and stock market. International Journal of Operations Research and Information Systems (IJORIS), 11(2), 22-35. doi:10.4018/IJORIS.2020040102
• Xu, L. and Kinkyo, T. (2023). Hedging effectiveness of Bitcoin and gold: Evidence from G7 stock markets. Journal of International Financial Markets, Institutions and Money, 85, 101764. https://doi.org/10.1016/j.intfin.2023.101764
• Yıldırım, M. (2019). Blockchain technology, cryptocurrencies and countries' approaches to cryptocurrencies. Bartın University Journal of Faculty of Economics and Administrative Sciences, 10(20), 295-277. Retrieved from https://dergipark.org.tr/tr/pub/bartiniibf
• Zohar, A. (2015). Bitcoin: under the hood. Communications of the ACM, 58(9), 104-113. https://doi.org/10.1145/2701411