BİTCOİN İLE BORSA ENDEKSLERİ VE SEÇİLİ FİNANSAL VARLIKLAR ARASINDAKİ UZUN DÖNEM ASİMETRİK İLİŞKİSİ: KIRILGAN BEŞLİ ÜLKELER ÖRNEĞİ

Yıl 2023, , 1046 - 1065, 31.07.2023

Mortaza Ojaghlou , Özge Demirkale

https://doi.org/10.11616/asbi.1266989

Öz

Türkiye, Brezilya, Hindistan, Güney Afrika ve Endonezya'nın ekonomik büyümelerini finanse etmek için istikrarsız yabancı yatırımlara olan yüksek bağımlılıkları nedeniyle, bu ülkeler “Kırılgan Beşli” ülke olarak adlandırılmıştır. Aynı zamanda Global Crypto Adoption Index'e göre, bu ülkeler kripto para birimlerine yatırım yapma konusunda oldukça aktiflerdir. Bu çalışmada “Kırılgan Beşli” ülkeler dikkate alınarak Bitcoin ve finansal varlıklar arasındaki uzun dönemli asimetrik ilişki Ağustos 2010 - Temmuz 2022 dönemine ait aylık veriler baz alınarak ARDL ve NARDL yöntemleri ile incelenmiştir. Pozitif ve negatif Bitcoin şoklarından kaynaklanan dinamik çarpanların doğrusal kombinasyonu, beş ülkenin tümü için NARDL üzerinden Dinamik çarpan testine başvurarak grafikleri çizilmiştir. Sonuçlar, Bitcoin'in tüm borsa endekslerine olumlu bir etkisi olmasına rağmen, yalnızca Türkiye ve Hindistan'daki değişkenlerin eş bütünleşik olduğunu göstermektedir. Bitcoin'in olumsuz şoklarının Türkiye'de daha derin ve baskın etkiye sahip olduğu anlaşılmıştır. Ancak, Bitcoin’in olumlu şoklarının Hindistan'da daha baskın olduğu sonucuna rastlanmıştır.

Anahtar Kelimeler

Bitcoin, Borsa Endeksleri, Finansal Varlıklar, Kırılgan Beşli, NARDL Yöntemi

LONG-TERM ASYMMETRIC RELATIONSHIP BETWEEN BITCOIN AND STOCK INDEXES AND SELECTED FINANCIAL ASSETS: THE EXAMPLE OF FRAGILE FIVE COUNTRIES

Öz

Due to Turkey, Brazil, India, South Africa, and Indonesia's high dependence on unstable foreign investment to finance their economic growth, these countries are known as “five fragile” countries. At the same time according to Global Crypto Adoption Index, those countries are remarkably active in investing in cryptocurrencies. In this study, considering the “five fragile”, the long-term asymmetric relationship between Bitcoin and financial assets has been examined by ARDL and NARDL methods based on monthly data for August 2010 - July 2022. To clearly understand the linear mixture of the dynamic multipliers due to positive and negative Bitcoin shocks, we carried out Dynamic multiplier graphs plotted for NARDL for all five countries. The results show that although there is a positive impact of Bitcoin on all stock indices, Değişkens only in Turkey and India are cointegrated. The negative shocks of Bitcoin have a more deep impact in Turkey. While the positive shocks of Bitcoin have more deep impact in India.

Anahtar Kelimeler

Bitcoin, Stock Market, Financial Assets, Five Fragile, NARDL Method

Kaynakça

• Aslanidis, N., Bariviera, A.-F., & Martínez-Ibañez, O. (2019). An Analysis of Cryptocurrencies Conditional Cross Correlations. Finance Research Letters, 31, s.130–13.
• Baur, D. G., & McDermott, T. K. (2010). Is Gold A Safe Haven? International Evidence. Journal of Banking & Finance, 34(8), s.1886-1898.
• Baur, D. G., Dimpfl, T. and Kuck, K. (2018). Bitcoin, Gold And The US Dollar – A Replication And Extension, Finance Research Letters, Cilt 25, s.103-110.
• Bouri, E., Molnar P., Azzi G., Roubaud D. & Hagfors, L. I. (2017). On The Hedge And Safe Haven Properties Of Bitcoin: Is İt Really More Than A Diversifier? Finance Research Letters, 20, s.192-198.
• Bouri, E., Das, M., Gupta, R., & Roubaud, D. (2018). Spillovers Between Bitcoin And Other Assets During Bear And Bull Markets. Applied Economics, 50(55), s.5935–5949.
• 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, s.224–235. https://doi.org/10.1016/j.resourpol.2018.03.008.
• Bouri, E., Shahzad, S. J. H., Roubaud D., Kristoufek L. & Lucey, B. (2020). Bitcoin, Gold, And Commodities As Safe Havens For Stocks: New İnsight Through Wavelet Analysis. The Quarterly Review of Economics and Finance, 77, s.156-164.
• Chadwick, M. G. (2019). Dependence of the “Fragile Five” And “Troubled Ten” Emerging Market Financial Systems On US Monetary Policy And Monetary Policy Uncertainty. Research in International Business and Finance, 49, s.251–268. https://doi.org/10.1016/j.ribaf.2019.04.002.
• Ciaian, P., Rajcaniova, M. ve Kancs, A. (2016). The Economics Of Bitcoin Price Formation. Applied Economics, 48(19), s.1799-1815.
• Ciaian, P., Rajcaniova, M. ve Kancs, d’A. (2018). Virtual Relationships: Short And Long Run Evidence From Bitcoin And Altcoin Markets. Journal of International Financial Markets, Institutions and Money, 52, s.173-195.
• Corbet, S., McHugh, G. ve Meegan, A. (2017). The İnfluence Of Central Bank Monetary Policy Announcements On Cryptocurrency Return Volatility. Investment Management & Financial Innovations, 14(4), s.60-72. http://dx.doi.org/10.21511/imfi.14(4).2017.07.
• Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring The Dynamic Relationships Between Cryptocurrencies And Other Fnancial Assets. Economics Letters, 165, s.28–34. https:// doi.org/10.1016/j.econlet.2018.01.004.
• Das, D., Le Roux, C.L., Jana, R.K. ve Dutta, A. (2020). Does Bitcoin Hedge Crude Oil İmplied Volatility And Structural Shocks? A Comparison With Gold, Commodity And The US Dollar. Finance Research Letters, 36, 101335, s.1-11.
• Demir, E., Simonyan, S., Garcia-Gomez, C. D. and Lau, C. K. M. (2021). The Asymmetric Effect Of Bitcoin On Altcoins: Evidence From The Nonlinear Autoregressive Distributed Lag (NARDL) Model, Finance Research Letters, (40), s.1-6.
• Dyhrberg, A. (2016). Hedging Capabilities Of Bitcoin. Is İt The Virtual Gold? Finance Research Letters, 16, s.136-144.
• Elie, B., Naji, J., Dutta, A., & Uddin, G. S. (2019). Gold And Crude Oil As Safe-Haven Assets For Clean Energy Stock İndices: Blended Copulas Approach. Energy, 178, s.544-553.
• Ghorbel, A. ve Jeribi, A. (2021). Investigating The Relationship Between Volatilities Of Cryptocurrencies And Other Financial Assets, Decisions İn Economics And Finance, https://doi.org/10.1007/s.10203-020-00312-9.
• Gonzalez, M. O., Jareno, F. and 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, s.810, 2-22.
• Hood, M., & Malik, F. (2013). Is Gold The Best Hedge And A Safe Haven Under Changing Stock Market Volatility?. Review of Financial Economics, 22(2), s.47-52.
• Huang, Y., Duan, K., & Mishra, T. (2021). Is Bitcoin Really More Than A Diversifer? A Pre-And Post-COVID-19 Analysis. Finance Research Letters, 43, 102016, s.1-11.
• Jareno, F., Gonzalez, M. O., Tolentino, M. and Sierra, K. (2020). Bitcoin And Gold Price Returns: A Quantile Regression And NARDL Analysis, Resources Policy (67), s.1-14.
• Jin, J., Yu, J., Hu, Y. ve Shang, Y. (2019). Which One İs More İnformative İn Determining Price Movements Of Hedging Assets? Evidence From Bitcoin, Gold And Crude Oil Markets. Physica A: Statistical Mechanics and its Applications, 527, 121121,s.1-13.
• Kang, S.H., Yoon, S.M., Bekiros, S. ve Uddin, G. S. (2019). Bitcoin As Hedge Or Safe Haven: Evidence From Stock, Currency, Bond And Derivatives Markets. Computational Economics, s.1-17.
• Kostika, E., & Laopodis, N. T. (2019). Dynamic Linkages Among Cryptocurrencies, Exchange Rates And Global Equity Markets’. Studies in Economics and Finance, 37(2), s.243–265.
• Kyriazis, N.A. (2020). Is Bitcoin Similar To Gold? An İntegrated Overview Of Empirical Findings. Journal of Risk and Financial Management, 13(5), s.1-19.
• Lin, M.Y. ve An, C.L. (2021). The Relationship Between Bitcoin And Resource Commodity Futures: Evidence from NARDL Approach. Resources Policy, 74, 102383, s.1-7.
• Narayan, Paresh Kumar – Smyth, Russell (2005). Trade Liberalization and Economic Growth in Fiji. An Empirical Assessment Using the Ardl Approach”, Journal of The Asia Pacific Economy, 10(1), s.96-115.
• Panagiotidis, T., Thanasis S. ve Orestis V. (2018). On The Determinants Of Bitcoin Returns: A LASSO Approach. Finance Research Letters 27, s. 235–40.
• Pesaran, M. Hashem - Shin, Yongcheol (1999). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis, Econometrics and Economic Theory in The 20th Century: The Ragnar Frish Centennial Symposium. Ed. / Steinar Strom. Cambridge: Cambridge University Press”, s.371-413.
• Pesaran, M. Hashem - Shin, Yongcheol - Smith, Richard J. (2001). “Bounds Testing Approaches to The Analysis of Level Relationships”, Journal of Applied Econometrics, Vol 16, Issue 3, s.289–326.
• Reboredo, J. C. (2013). Is Gold A Safe Haven Or A Hedge For The US Dollar? Implications For Risk Management. Journal of Banking & Finance, 37(8), s.2665-2676.
• Shahzad, S. J. H., Nor, M. S., Ferrer, R. ve Hammoudeh, S. (2017). Asymmetric Determinants Of CDS Spreads: U.S. İndustry-Level Evidence Through NARDL Approach. Economic Modelling, 60, s.211-230.
• Shin, Y., Yu, B. ve Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework, W.C. Horrace and R.C. Sickles (Ed.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications içinde (s. 281–314). New York: Springer.
• Thampanya, N., Nasir, M. A. and Huynh, T. L. (2020). Asymmetric Correlation And Hedging Effectiveness Of Gold & Cryptocurrencies: From Pre-İndustrial To The 4th İndustrial Revolution, Technological Forecasting and Social Change, Volume 159, s.1-13.
• Trabelsi, N. (2018). Are There Any Volatility Spill-Over Efects Among Cryptocurrencies And Widely Traded Asset Classes? Journal of Risk and Financial Management, 11(4), s.1-17.
• Umar, M., Hung, N. T., Chen, S., Iqbal, A., & Jebran, K. (2020). Are Stock Markets And Cryptocurrencies Connected? The Singapore Economic Review, s.1–16.
• Wang, J., Xue, Y. and Liu, M. (2016). An Analysis of Bitcoin Price Based on VEC Model, International Conference on Economics and Management Innovations (ICEMI 2016), s.146-152.
• Yang L. T. Y. (2020). The İnfuence Of Taiwan’s Stock Market On Bitcoin’s Price Under Taiwan’s Monetary Policy Threshold. Applied Economics, 52(45), s.4967–4975.