NARDL Yönteminin Kripto Para Birimlerine Yönelik Bir Monte Carlo Simülasyon Analizi

Öz

Doğrusal olmayan ARDL (NARDL) yöntemi ekonomik ve finansal değişkenlerin incelenmesinde kullanılan doğrusal olmayan ekonometrik yöntemlerden biridir. Kripto paralar ile ekonomik ve finansal değişkenler arasındaki asimetrik ilişkileri inceleme imkânı sunan NARDL yöntemi bu çalışmanın odak noktasını oluşturmaktadır. NARDL metodunun ilk geliştirilme aşamasında, tahmin edicilerin sonlu örnek özelliklerini araştırmak için basit veri üretme süreci kullanılarak ve normal dağılım varsayımı altında Monte Carlo deneyleri yapılmıştır. Bu çalışmada ise NARDL yönteminin güvenilirliği normal olmayan dağılımlar altında incelenmektedir. Kripto para birimlerinin getiri dağılımları normal dağılımından farklı ve de ağır kuyruklu olduğu için bu önemli bir araştırma problemidir. Bu çalışmada, NARDL modeli normal dağılım ve farklı ağır kuyruklu dağılımlar (Student t-dağılımı ve Skew-t dağılımı) altında simüle edilmiştir. Literatürde, bildiğimiz kadarıyla, normal olmayan zaman serileri için NARDL yönteminin sonlu örnek özellikleri üzerine bir çalışma bulunmamaktadır. Bu açıdan çalışmamızın sonuçları, kripto para birimlerinin ekonomi ve finans üzerindeki etkileri hakkında yapılan değerlendirmelerin doğruluğu üzerine çıkarımlar yapılmasında yol gösterici olacaktır.

Anahtar Kelimeler

• Monte Carlo simülasyonu
• doğrusal olmayan ARDL
• kripto paralar

A Monte Carlo Simulation Analysis of the NARDL Method with Regard to Cryptocurrencies

Öz

One of the nonlinear techniques utilized in the analysis of economic and financial variables is the nonlinear autoregressive distributed lag (NARDL) method. This study primarily focuses on the NARDL approach, which offers the chance to assess the asymmetric relationships between cryptocurrencies and economic and financial variables. Monte Carlo experiments were carried out while developing the NARDL method for the purpose of investigating the finite sample properties of estimators under the premise of normal distribution for a simple data generation procedure. This study examines the NARDL method’s dependability for non-normal distributions. The return distributions of cryptocurrencies are obviously non-normal and heavy-tailed, making this a significant research challenge. This study simulates the NARDL model using both several heavy-tailed distributions as well as a normal distribution. To the best of our knowledge, no research has yet occurred on the NARDL method’s finite sample qualities for time series with non-normality. The findings from this study could have a significant impact on how accurately predictions are made regarding the impact cryptocurrencies have on the economy and finance.

Anahtar Kelimeler

• Monte Carlo simulation
• nonlinear ARDL
• cryptocurrencies

Kaynakça

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