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ANALYSIS OF THE FREQUENCY DOMAIN CAUSAL RELATIONSHIPS BETWEEN CRYPTOCURRENCIES

Year 2021, Volume: 9 Issue: 2, 165 - 192, 20.08.2021
https://doi.org/10.22139/jobs.955795

Abstract

Aim: In this study, the causal relationships between four cryptocurrencies—Binance coin (BNB), Bitcoin cash (BCH), Stellar (XLM) and Cardano (ADA)—are examined.
Method: The Breitung and Candelon (2006) frequency domain causality test is applied to examine the causal relationships between the four cryptocurrencies. The Toda and Yamamoto (1995) causality test, which is a time domain causality test, is also included in the study for comparison purposes.
Findings: The Toda and Yamamoto (1995)causality test results show the bidirectional causal relationships between the prices of all the cryptocurrencies under study. The Breitung and Candelon (2006) frequency domain causality test results show no uniform / simple causal relationships between the prices of the related cryptocurrencies and that the direction, size and statistical significance of the causal relationships may change over different frequencies based on the information flow towards the market.
Results: The findings provide important information for cryptocurrency market investors by showing the price movements of the cryptocurrencies, which can be used to predict the price movements of other cryptocurrencies. 

References

  • Al-Mansour, B.Y. (2020). Cryptocurrency Market: Behavioral Finance Perspective. Journal of Asian Finance, Economics and Business, 7 (12), 159-168.
  • Alqahtani, F., Hamdi,B. ve Hammoudeh, S. (2021). The Effects of Global Factors On The Saudi Arabia Equity Market By Firm Size:Implications For Risk Management Based On Quantile Analysis And Frequency Domain Casuality. Journal of Multinational Financial Management, Yayınlanma aşamasında.
  • Aydin, M. (2018). Natural Gas Consumption and Economic Growth Nexus for Top 10 Natural Gas-Consuming Countries: A Granger Causality Analysis in The Freguency Domain. Energy, 165, 179-186.
  • Breitung, J., & Candelon, B. (2006). Testing for Short-And Long-Run Causality: A Frequency-Domain Approach. Journal of Econometrics, 132(2), 363-378.
  • Canh, N. P., Wongchoti, U., Thanh, S.D. ve Thong, N.T. (2019). Systematic Risk in Cryptocurrency Market: Evidence from DCC-MGARCH Model. Finance Research Letters 29, 90–100.
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B. ve Yarovaya, L. (2018). Exploring the Dynamic Relationships Between Cryptocurrencies and Other Financial Assets. Economics Letters, 165, 28–34.
  • Corelli, A. (2018). Cryptocurrencies and Exchange Rates: A Relationship and Causality Analysis. Risks, 6(4), 1-11.
  • Croux, C.,& Reusens, P. (2013). Do Stock Prices Contain Predictive Power for the Future Economic Activity ? A Granger Causality Analysis in the Frequency Domain. Journal of Macroeconomics, 35, 93-103.
  • Dickey, D.,& Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74, 427-431.
  • Geweke, J. (1982). Measurement of Linear Dependence and Feedback Between Multiple Time Series. Journal of the American Statistical Association, 77 (378), 304-324.
  • Gorus, M.S.,& Aydin, M. (2019). The Relationship Between Energy Consumption , Economic Growth, and CO2 Emission in MENA Countries: Causality Analysis in The Frequency Domain. Energy, 168, 815-822.
  • Granger, C.W.J. (1969). Investigating Causal Relations by Econometrics Models and Cross Spectral Methods. Econometrica, 37, 424-438.
  • Hu, Y., Hou, Y.G., ve Oxley, L. (2020). What Role Do Futures Markets Play in Bitcoin Pricing? Causality, Cointegration and Price Discovery From A Time-Varying Perspective? International Review of Financial Analysis,72, 1-18.
  • Hyung, T.L.D. (2019). Spillover Risks on Cryptocurrency Markets: A Look From VAR-SVAR Granger Causality and Student’s-t Capulas. Risk and Financial Management, 12, 1-52.
  • Joseph, A., Sisodia, G. ve Tiwari, A.K. (2014). A Frequency Domain Causality Investigation between Futures and Spot Prices of Indian Commodity Markets. Economic Modelling, 40, 250-258.
  • Kassouri, Y., & Altınbaş, H. (2020). Threshold Cointegration, Nonlinearity, And Frequency Domain Causality Relationship Between Stock Price And Turkish Lira. Research in International Business and Finance, 52, 1-18.
  • Kaya, Y. (2018). Analysis of Cryptocurrency Market and Drivers of the Bitcoin Price: Understanding The Price Drivers Of Bitcoin Under Speculative Environment. Master of Science Thesis, Stockholm: KTH Industrial Engineering and Management.
  • Kayhan, S., Bayat, T. ve Yüzbaşı, B. (2013). Goverment Expenditures and Trade Deficits in Turkey: Time Domain and Frequency Domain Analyses. Economic Modelling, 35, 153-158.
  • Keller, A., & Scholz, M. (2019). Trading Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors. Fortieth International Conference on Information Systems. Munich: International Conference on Information Systems (ICIS) , 15-18 December, p.1-17. Kim, M.J., Canh, N.P. ve Park, S.Y. (2021). Causal Relationship Among Cryptocurrencies: A Conditional Quantile Approach. Finance Reserach Letters. Yayınlanma aşamasında.
  • Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific Reports, 3, 1-7.
  • Mokni, K., & Ajmi, A.N. (2021). Cryptocurrencies vs. US Dolar : Evidence From Causality in Quantiles Analysis. Economic Analysis and Policy, 69,238-252.
  • Phillips, P.C.B.,& Perron, P. (1988). Testing For A Unit Root in Time Series Regression. Biometrika, 75, 335-346.
  • Pradhan, A.K., Mishra, B.R., Tiwari, A.K. ve Hammoudeh, S. (2020). Macroeconomic Factors and Frequency Domain Causality Between Gold And Silver Returns in India. Resources Policy, 68, 1-12.
  • Sarkodie, S.A. (2020). Causal Effect of Environmental Factors, Economic Indicators and Domectic Material Consumption Using Freguency Domain Causality Test. Science of The Total Environment, 736, 1-17.
  • Schwert, G. W. (1989). Tests for Unit Root: A Monte Carlo Investigation. Journal of Business and Economic Statistics, 7, 147-160.
  • Shi, S., Phillips, P. C. ve Hurn, S. (2018). Change Detection and the Causal Impact of the Yield Curve. Journal of Time Series Analysis, 39 , 966–987.
  • Tastan, H. (2015). Testing for Spectral Granger Causality. The Stata Journal, 15(4), 1157-1166.
  • Toda, H.Y., &Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. Journal of Econometrics, 66, 225-250.
  • Tu, Z., & Xue, C. (2021). Effect of Bifurcation on the Interaction between Bitcoin and Litecoin. Finance Research Letters, 31, 382–385.
  • Wei, Y. (2015). The Informational Role of Commodity Prices in Formulating Monetary Policy: A Reexamination under the Frequency Domain. Empirical Economics,49, 537-549.
  • Wei,Y.,& Guo, X. (2016). An Empirical Analysis of the Relationship Between Oil Prices and the Chinese Macro-Economy. Energy Economics,56,88-100.
  • Yahoo Finance. Cryptocurency Data. 19 Mayıs 2021 tarihinde https://finance.yahoo.com/cryptocurrencies/ web sitesinden temin edildi.

KRİPTO PARA BİRİMLERİ ARASINDAKİ FREKANS ALANLI NEDENSELLİK İLİŞKİNİN ANALİZİ

Year 2021, Volume: 9 Issue: 2, 165 - 192, 20.08.2021
https://doi.org/10.22139/jobs.955795

Abstract

Amaç: Bu çalışmada günlük veriler kullanılarak Binance coin (BNB), Bitcoin cash (BCH), Stellar (XLM) ve Cardano’dan (ADA) oluşan dört kripto para birimi arasındaki nedensellik ilişkileri incelenmiştir.
Yöntem: Nedensellik analizlerinde Breitung ve Candelon (2006) frekans alanı nedensellik testinden yararlanılmıştır. Karşılaştırma amacıyla çalışmada Toda ve Yamamoto (1995) nedensellik testine de yer verilmiştir.
Bulgular: Toda ve Yamamoto (1995) nedensellik testi inceleme kapsamındaki tüm kripto para birimlerinin fiyatları arasında çift yönlü bir nedensellik ilişkisinin söz konusu olduğunu göstermektedir. Çalışmanın ana konusunu oluşturan Breitung ve Candelon (2006) frekans alanı nedensellik testi ise ilgili kripto para birimlerinin fiyatları arasında tek tip / basit bir nedensellik ilişkisinin söz konusu olmadığını, piyasaya dönük bilgi akışına göre nedensellik ilişkisinin yönünün, boyutunun ve istatistiki anlamlılığının değişebileceğini göstermektedir.
Sonuç: Yapılan analizler kripto para piyasalarında yatırım yapan yatırımcılar için özellikle düşük frekanslarda hangi kripto para birimlerindeki fiyat hareketlerine bakarak önceden diğer kripto para birimlerindeki fiyat hareketleri konusunda bilgi sahibi olunabileceği konusunda oldukça önemli bilgiler sunmaktadır. 

References

  • Al-Mansour, B.Y. (2020). Cryptocurrency Market: Behavioral Finance Perspective. Journal of Asian Finance, Economics and Business, 7 (12), 159-168.
  • Alqahtani, F., Hamdi,B. ve Hammoudeh, S. (2021). The Effects of Global Factors On The Saudi Arabia Equity Market By Firm Size:Implications For Risk Management Based On Quantile Analysis And Frequency Domain Casuality. Journal of Multinational Financial Management, Yayınlanma aşamasında.
  • Aydin, M. (2018). Natural Gas Consumption and Economic Growth Nexus for Top 10 Natural Gas-Consuming Countries: A Granger Causality Analysis in The Freguency Domain. Energy, 165, 179-186.
  • Breitung, J., & Candelon, B. (2006). Testing for Short-And Long-Run Causality: A Frequency-Domain Approach. Journal of Econometrics, 132(2), 363-378.
  • Canh, N. P., Wongchoti, U., Thanh, S.D. ve Thong, N.T. (2019). Systematic Risk in Cryptocurrency Market: Evidence from DCC-MGARCH Model. Finance Research Letters 29, 90–100.
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B. ve Yarovaya, L. (2018). Exploring the Dynamic Relationships Between Cryptocurrencies and Other Financial Assets. Economics Letters, 165, 28–34.
  • Corelli, A. (2018). Cryptocurrencies and Exchange Rates: A Relationship and Causality Analysis. Risks, 6(4), 1-11.
  • Croux, C.,& Reusens, P. (2013). Do Stock Prices Contain Predictive Power for the Future Economic Activity ? A Granger Causality Analysis in the Frequency Domain. Journal of Macroeconomics, 35, 93-103.
  • Dickey, D.,& Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74, 427-431.
  • Geweke, J. (1982). Measurement of Linear Dependence and Feedback Between Multiple Time Series. Journal of the American Statistical Association, 77 (378), 304-324.
  • Gorus, M.S.,& Aydin, M. (2019). The Relationship Between Energy Consumption , Economic Growth, and CO2 Emission in MENA Countries: Causality Analysis in The Frequency Domain. Energy, 168, 815-822.
  • Granger, C.W.J. (1969). Investigating Causal Relations by Econometrics Models and Cross Spectral Methods. Econometrica, 37, 424-438.
  • Hu, Y., Hou, Y.G., ve Oxley, L. (2020). What Role Do Futures Markets Play in Bitcoin Pricing? Causality, Cointegration and Price Discovery From A Time-Varying Perspective? International Review of Financial Analysis,72, 1-18.
  • Hyung, T.L.D. (2019). Spillover Risks on Cryptocurrency Markets: A Look From VAR-SVAR Granger Causality and Student’s-t Capulas. Risk and Financial Management, 12, 1-52.
  • Joseph, A., Sisodia, G. ve Tiwari, A.K. (2014). A Frequency Domain Causality Investigation between Futures and Spot Prices of Indian Commodity Markets. Economic Modelling, 40, 250-258.
  • Kassouri, Y., & Altınbaş, H. (2020). Threshold Cointegration, Nonlinearity, And Frequency Domain Causality Relationship Between Stock Price And Turkish Lira. Research in International Business and Finance, 52, 1-18.
  • Kaya, Y. (2018). Analysis of Cryptocurrency Market and Drivers of the Bitcoin Price: Understanding The Price Drivers Of Bitcoin Under Speculative Environment. Master of Science Thesis, Stockholm: KTH Industrial Engineering and Management.
  • Kayhan, S., Bayat, T. ve Yüzbaşı, B. (2013). Goverment Expenditures and Trade Deficits in Turkey: Time Domain and Frequency Domain Analyses. Economic Modelling, 35, 153-158.
  • Keller, A., & Scholz, M. (2019). Trading Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors. Fortieth International Conference on Information Systems. Munich: International Conference on Information Systems (ICIS) , 15-18 December, p.1-17. Kim, M.J., Canh, N.P. ve Park, S.Y. (2021). Causal Relationship Among Cryptocurrencies: A Conditional Quantile Approach. Finance Reserach Letters. Yayınlanma aşamasında.
  • Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific Reports, 3, 1-7.
  • Mokni, K., & Ajmi, A.N. (2021). Cryptocurrencies vs. US Dolar : Evidence From Causality in Quantiles Analysis. Economic Analysis and Policy, 69,238-252.
  • Phillips, P.C.B.,& Perron, P. (1988). Testing For A Unit Root in Time Series Regression. Biometrika, 75, 335-346.
  • Pradhan, A.K., Mishra, B.R., Tiwari, A.K. ve Hammoudeh, S. (2020). Macroeconomic Factors and Frequency Domain Causality Between Gold And Silver Returns in India. Resources Policy, 68, 1-12.
  • Sarkodie, S.A. (2020). Causal Effect of Environmental Factors, Economic Indicators and Domectic Material Consumption Using Freguency Domain Causality Test. Science of The Total Environment, 736, 1-17.
  • Schwert, G. W. (1989). Tests for Unit Root: A Monte Carlo Investigation. Journal of Business and Economic Statistics, 7, 147-160.
  • Shi, S., Phillips, P. C. ve Hurn, S. (2018). Change Detection and the Causal Impact of the Yield Curve. Journal of Time Series Analysis, 39 , 966–987.
  • Tastan, H. (2015). Testing for Spectral Granger Causality. The Stata Journal, 15(4), 1157-1166.
  • Toda, H.Y., &Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. Journal of Econometrics, 66, 225-250.
  • Tu, Z., & Xue, C. (2021). Effect of Bifurcation on the Interaction between Bitcoin and Litecoin. Finance Research Letters, 31, 382–385.
  • Wei, Y. (2015). The Informational Role of Commodity Prices in Formulating Monetary Policy: A Reexamination under the Frequency Domain. Empirical Economics,49, 537-549.
  • Wei,Y.,& Guo, X. (2016). An Empirical Analysis of the Relationship Between Oil Prices and the Chinese Macro-Economy. Energy Economics,56,88-100.
  • Yahoo Finance. Cryptocurency Data. 19 Mayıs 2021 tarihinde https://finance.yahoo.com/cryptocurrencies/ web sitesinden temin edildi.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Original Articles
Authors

Önder Büberkökü 0000-0002-7140-557X

Publication Date August 20, 2021
Submission Date June 22, 2021
Acceptance Date August 16, 2021
Published in Issue Year 2021 Volume: 9 Issue: 2

Cite

APA Büberkökü, Ö. (2021). KRİPTO PARA BİRİMLERİ ARASINDAKİ FREKANS ALANLI NEDENSELLİK İLİŞKİNİN ANALİZİ. İşletme Bilimi Dergisi, 9(2), 165-192. https://doi.org/10.22139/jobs.955795