TY - JOUR T1 - KRİPTO VARLIKLAR ARASINDAKİ DOĞRUSAL OLMAYAN NEDENSELLİK İLİŞKİSİ TT - NONE-LINEAR CAUSALITY RELATIONSHIPS AMONG CRYPTO ASSETS AU - Sünbül, Ersin PY - 2025 DA - March Y2 - 2025 DO - 10.48070/erciyesakademi.1586676 JF - Erciyes Akademi PB - Erciyes University WT - DergiPark SN - 2757-7031 SP - 152 EP - 179 VL - 39 IS - 1 LA - tr AB - Bu çalışma, kripto para birimlerinin nedensellik ilişkilerini doğrusal olmayan yöntemlerle inceleyerek, bu varlıkların birbirleriyle olan etkileşimlerini daha kapsamlı bir şekilde anlamayı amaçlamaktadır. Çalışmada, 2020'nin ilk haftasından 2022'nin otuz birinci haftasına kadar olan sekiz önemli kripto varlığının (Bitcoin, Ethereum, Tether, USD Coin, Binance Coin, Ripple ve Cardano) haftalık dolar cinsinden döviz kuru verileri kullanılmıştır. Veri seti 135 gözlemi içermektedir. Çalışma, özellikle durağanlık analizi ve doğrusal olmayan nedensellik analizi olmak üzere ekonometrik zaman serisi ve yapay sinir ağı (YSA) analiz yöntemlerini kullanmaktadır. Değişkenlerin durağanlık kararları, üç birim kök testine dayanmaktadır. Bunlar; ADF Testi, PP Testi ve KPSS Testleridir. Değişkenler arasındaki ilişki, Doğrusal olmayan Granger Nedensellik Analizi kullanılarak keşfedilmiştir. Tüm analizler R-Studio programında gerçekleştirilmiştir. Durağanlık analizinde, USDT ve USDC'nin düzeyde (I (0)) durağan olduğu, diğer değişkenlerin ise birinci farkta (I (1)) durağan olduğu belirlenmiştir. Çalışma sonucunda, hiçbir değişken arasında doğrusal olmayan nedensellik ilişkisine rastlanmamıştır. KW - Kripto varlıklar KW - Doğrusal Olmayan Nedensellik Analizi KW - Zaman Serisi Analizi N2 - This study aims to comprehensively understand the interactions among cryptocurrency assets by examining their causality relationships using nonlinear methods. The study utilizes weekly USD exchange rate data for eight major cryptocurrencies (Bitcoin, Ethereum, Tether, USD Coin, Binance Coin, Ripple, and Cardano) from the first week of 2020 to the thirty-first week of 2022. The dataset contains 135 observations. The study employs econometric time series methods and artificial neural network (ANN) analysis, focusing particularly on stationarity analysis and nonlinear causality analysis. The stationarity of the variables is determined based on three unit root tests: ADF Test, PP Test, and KPSS Test. The relationship among variables is explored using Nonlinear Granger Causality Analysis. All analyses are conducted using R-Studio. The stationarity analysis indicates that USDT and USDC are stationary at level (I (0)), while other variables are stationary at first difference (I (1)). The study finds no evidence of nonlinear causality relationships among the variables. CR - Agyei, S. K., Adam, A. 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