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

Yıl 2023, Sayı: 39, 37 - 48, 27.12.2023

Abdülsamet Aça Kemal Dinçer Dingeç

https://doi.org/10.26650/ekoist.2023.39.1334288

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

Proje Numarası

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Kaynakça

  • Azzalini, A., & Capitanio, A. (2014). The Skew-Normal and Related Families (First). Cambridge University Press. google scholar
  • Baur, D. G., Hong, K. H., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions andMoney, 54, 177-189. https://doi.Org/10.1016/j.intfin.2017.12.004 google scholar
  • Becker, R. A. , Chambers, J. M., & Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole. google scholar
  • Bouri, E., Gupta, R., Lahiani, A., & Shahbaz, M. (2018). Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices. Resources Policy, 57, 224-235. https://doi.org/10.1016/j.resourpol.2018.03.008 google scholar
  • Canoz, I., & Dirican, C. (2017). The Cointegration Relationship Between Bitcoin Prices and Major World Stock Indices: An Analysis with ARDL Model Approach. Press academia, 4(4), 377-392. https://doi.org/10.17261/pressacademia.2017.748 google scholar
  • Çaşkurlu, E.,& Arslan, C. B. (2021). Blockchain Technology, Cryptocurrency and Financial Depensement: An Analysis on Turkey. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 28, 97-124. google scholar
  • Ciaian, P., Rajcaniova, M., & Kancs, d’Artis. (2018). Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173-195. https://doi.org/10.1016/j.intfin.2017.11.001 google scholar
  • Coinmarketcap. (2022, August 29). https://coinmarketcap.com/tr/currencies/bitcoin/ google scholar
  • Contuk, F. Y. (2021). Covid-19 Sürecinde Altın ve Petrol Fiyatlarının Bitcoin Üzerindeki Asimetrik Etkisi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(3), 911-926. google scholar
  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199. google scholar
  • De la O Gonzalez, M., Jareno, F., & Skinner, F. S. (2020). Nonlinear autoregressive distributed lag approach: An application on the connectedness between bitcoin returns and the other ten most relevant cryptocurrency returns. Mathematics, 8(5). google scholar
  • Demir, E., Simonyan, S., Garma-Gomez, C. D., & Lau, C. K. M. (2020). The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model. Finance Research Letters. https://doi.org/10.1016/j.frl.2020.101754 google scholar
  • 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 google scholar
  • Gaies, B., Nakhli, M. S., Sahut, J. M., & Guesmi, K. (2021). Is Bitcoin rooted in confidence? - Unraveling the determinants of globalized digital currencies. Technological Forecasting and Social Change, 172. https://doi.org/10.1016/j.techfore.2021.121038 google scholar
  • Ghorbel, A., Frikha, W., & Manzli, Y. S. (2022). Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets. Eurasian Economic Review. https://doi.org/10.1007/s40822-022-00206-8 google scholar
  • Jareno, F., de La O Gonzalez, M., & Belmonte, P. (2022). Asymmetric interdependencies between cryptocurrency and commodity markets: the COVID-19 pandemic impact. Quantitative Finance and Economics, 6(1), 83-112. https://doi.org/10.3934/qfe.2022004 google scholar
  • Jareno, F., Gonzalez, M. de la O., Lopez, R., &Ramos, A. R. (2021). Cryptocurrencies andoilprice shocks: A NARDL analysis in theCOVID-19 pandemic. Resources Policy, 74. https://doi.org/10.1016/j.resourpol.2021.102281 google scholar
  • Jareno, F., Gonzalez, M. de la O., Tolentino, M., & Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67. google scholar
  • Jeribi, A., Jena, S. K., & Lahiani, A. (2021). Are cryptocurrencies a backstop for the stock market in a covid-19-led financial crisis? Evidence from the nardl approach. International Journal of Financial Studies, 9(3). https://doi.org/10.3390/yfs9030033 google scholar
  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous Univariate Distributions. In Continuous Univariate Distributions (Vol. 2). Wiley. google scholar
  • Leirvik, T. (2022). Cryptocurrency returns and the volatility of liquidity. Finance Research Letters, 44. https://doi.org/10.1016/j.frl.2021.102031 google scholar
  • Li, X.,& Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49-60. google scholar
  • Lin, M. Y., & An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74. google scholar
  • Lin, M. Y., & An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74. google scholar
  • Moussa, W., Mgadmi, N., Bejaoui, A., & Regaieg, R. (2021). Exploring the dynamic relationship between Bitcoin and commodities: New insights through STECM model. Resources Policy, 74. https://doi.org/10.1016/j.resourpol.2021.102416 google scholar
  • Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. www.bitcoin.org google scholar
  • 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. google scholar
  • R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. google scholar
  • Shanaev, S., & Ghimire, B. (2021). A fitting return to fitting returns: Cryptocurrency distributions revisited. https://ssrn.com/abstract=3847351 google scholar
  • Shin, Y., Yu, B., & Greenwood-Nimmo, Ma. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of PEter Schmidt; Econometric Methods and Applications. (pp. 281-314). Spinger. google scholar
  • Sunder, M. (n.d.). STATA NARDL Package. google scholar
  • Sayed, A. A., Ahmed, F., Kamal, M. A., Ullah, A., & Ramos-Requena, J. P. (2022). Is There an Asymmetric Relationship between Eco-nomic Policy Uncertainty, Cryptocurrencies, and Global Green Bonds? Evidence from the United States of America. Mathematics, 10(5). https://doi.org/10.3390/math10050720 google scholar

NARDL Yönteminin Kripto Para Birimlerine Yönelik Bir Monte Carlo Simülasyon Analizi

Yıl 2023, Sayı: 39, 37 - 48, 27.12.2023

Abdülsamet Aça Kemal Dinçer Dingeç

https://doi.org/10.26650/ekoist.2023.39.1334288

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

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

  • Azzalini, A., & Capitanio, A. (2014). The Skew-Normal and Related Families (First). Cambridge University Press. google scholar
  • Baur, D. G., Hong, K. H., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions andMoney, 54, 177-189. https://doi.Org/10.1016/j.intfin.2017.12.004 google scholar
  • Becker, R. A. , Chambers, J. M., & Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole. google scholar
  • Bouri, E., Gupta, R., Lahiani, A., & Shahbaz, M. (2018). Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices. Resources Policy, 57, 224-235. https://doi.org/10.1016/j.resourpol.2018.03.008 google scholar
  • Canoz, I., & Dirican, C. (2017). The Cointegration Relationship Between Bitcoin Prices and Major World Stock Indices: An Analysis with ARDL Model Approach. Press academia, 4(4), 377-392. https://doi.org/10.17261/pressacademia.2017.748 google scholar
  • Çaşkurlu, E.,& Arslan, C. B. (2021). Blockchain Technology, Cryptocurrency and Financial Depensement: An Analysis on Turkey. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 28, 97-124. google scholar
  • Ciaian, P., Rajcaniova, M., & Kancs, d’Artis. (2018). Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173-195. https://doi.org/10.1016/j.intfin.2017.11.001 google scholar
  • Coinmarketcap. (2022, August 29). https://coinmarketcap.com/tr/currencies/bitcoin/ google scholar
  • Contuk, F. Y. (2021). Covid-19 Sürecinde Altın ve Petrol Fiyatlarının Bitcoin Üzerindeki Asimetrik Etkisi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(3), 911-926. google scholar
  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199. google scholar
  • De la O Gonzalez, M., Jareno, F., & Skinner, F. S. (2020). Nonlinear autoregressive distributed lag approach: An application on the connectedness between bitcoin returns and the other ten most relevant cryptocurrency returns. Mathematics, 8(5). google scholar
  • Demir, E., Simonyan, S., Garma-Gomez, C. D., & Lau, C. K. M. (2020). The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model. Finance Research Letters. https://doi.org/10.1016/j.frl.2020.101754 google scholar
  • 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 google scholar
  • Gaies, B., Nakhli, M. S., Sahut, J. M., & Guesmi, K. (2021). Is Bitcoin rooted in confidence? - Unraveling the determinants of globalized digital currencies. Technological Forecasting and Social Change, 172. https://doi.org/10.1016/j.techfore.2021.121038 google scholar
  • Ghorbel, A., Frikha, W., & Manzli, Y. S. (2022). Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets. Eurasian Economic Review. https://doi.org/10.1007/s40822-022-00206-8 google scholar
  • Jareno, F., de La O Gonzalez, M., & Belmonte, P. (2022). Asymmetric interdependencies between cryptocurrency and commodity markets: the COVID-19 pandemic impact. Quantitative Finance and Economics, 6(1), 83-112. https://doi.org/10.3934/qfe.2022004 google scholar
  • Jareno, F., Gonzalez, M. de la O., Lopez, R., &Ramos, A. R. (2021). Cryptocurrencies andoilprice shocks: A NARDL analysis in theCOVID-19 pandemic. Resources Policy, 74. https://doi.org/10.1016/j.resourpol.2021.102281 google scholar
  • Jareno, F., Gonzalez, M. de la O., Tolentino, M., & Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67. google scholar
  • Jeribi, A., Jena, S. K., & Lahiani, A. (2021). Are cryptocurrencies a backstop for the stock market in a covid-19-led financial crisis? Evidence from the nardl approach. International Journal of Financial Studies, 9(3). https://doi.org/10.3390/yfs9030033 google scholar
  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous Univariate Distributions. In Continuous Univariate Distributions (Vol. 2). Wiley. google scholar
  • Leirvik, T. (2022). Cryptocurrency returns and the volatility of liquidity. Finance Research Letters, 44. https://doi.org/10.1016/j.frl.2021.102031 google scholar
  • Li, X.,& Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49-60. google scholar
  • Lin, M. Y., & An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74. google scholar
  • Lin, M. Y., & An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74. google scholar
  • Moussa, W., Mgadmi, N., Bejaoui, A., & Regaieg, R. (2021). Exploring the dynamic relationship between Bitcoin and commodities: New insights through STECM model. Resources Policy, 74. https://doi.org/10.1016/j.resourpol.2021.102416 google scholar
  • Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. www.bitcoin.org google scholar
  • 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. google scholar
  • R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. google scholar
  • Shanaev, S., & Ghimire, B. (2021). A fitting return to fitting returns: Cryptocurrency distributions revisited. https://ssrn.com/abstract=3847351 google scholar
  • Shin, Y., Yu, B., & Greenwood-Nimmo, Ma. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of PEter Schmidt; Econometric Methods and Applications. (pp. 281-314). Spinger. google scholar
  • Sunder, M. (n.d.). STATA NARDL Package. google scholar
  • Sayed, A. A., Ahmed, F., Kamal, M. A., Ullah, A., & Ramos-Requena, J. P. (2022). Is There an Asymmetric Relationship between Eco-nomic Policy Uncertainty, Cryptocurrencies, and Global Green Bonds? Evidence from the United States of America. Mathematics, 10(5). https://doi.org/10.3390/math10050720 google scholar
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonometri (Diğer)
Bölüm ARAŞTIRMA MAKALESI
Yazarlar

Abdülsamet Aça 0000-0001-8956-5064

Kemal Dinçer Dingeç 0000-0002-5216-4651

Proje Numarası -
Yayımlanma Tarihi 27 Aralık 2023
Gönderilme Tarihi 28 Temmuz 2023
Yayımlandığı Sayı Yıl 2023 Sayı: 39

Kaynak Göster

APA Aça, A., & Dingeç, K. D. (2023). NARDL Yönteminin Kripto Para Birimlerine Yönelik Bir Monte Carlo Simülasyon Analizi. EKOIST Journal of Econometrics and Statistics(39), 37-48. https://doi.org/10.26650/ekoist.2023.39.1334288
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