AN EMPIRICAL ANALYSIS OF SPECULATIVE BEHAVIOR AND THE SPILLOVER EFFECT IN CRYPTOCURRENCY MARKETS

Abstract

For risk management and stable pricing in the cryptocurrency market, it is necessary to determine the interdependence of speculative behaviour and crypto assets. The correlation and high volatility caused by the interdependence of financial assets in the cryptocurrency market can lead to spreading risks. The study aims to measure the speculative behaviour and spillover effect in the prices of financial assets in the cryptocurrency market. The study used the SADF test, the generalized Dickey-Fuller test (GSADF), and the frequency domain causality test of Breitung and Candelon (2006) to determine the speculative behaviour and spillover effect in the prices of financial assets in the cryptocurrency market. Empirical evidence of speculative bubble formation between January 1, 2018, and December 2021 for the cryptocurrency assets covered in the study (ADA, BNB, BTC, DOGE, ETH, XLM, and XRP) is presented. Moreover, the frequency domain causality results obtained in the study show a contagion and spillover effect between crypto assets. The results provide essential information on the development of speculative behaviour and spread risk in the formation of financial asset prices in the cryptocurrency market.

Keywords

• Cryptocurrency Markets
• Bubbles
• Spread Risks
• Right-tailed Unit Root Tests
• Frequency Domain Causality

References

1. Başarır, Ç. (2018). Korku Endeksi (VİX) ile Bist 100 arasındaki ilişki: Frekans alanı nedensellik analizi. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 19(2), 177-191.
2. Bodart, V. ve Candelon, B. (2009). Evidence of interdependence and contagion using a frequency domain framework. Emerging Markets Review, 10(2), 140–150.
3. Bouri, E., Das, M., Gupta, R. and Roubaud, D. (2018). Spillovers between Bitcoin and other assets during bear and bull markets. Applied Economics, 50(55), 5935-49.
4. Bouri, E., Shahzad, S.J.H. and Roubau, David. (2019). Co-explosivity in the cryptocurrency market. Finance Research Letters, 29, 178-183.
5. Bozoklu, S., & Yilanci, V. (2013). Energy consumption and economic growth for selected OECD countries: Further evidence from the Granger causality test in the frequency domain. Energy Policy, 63, 877- 881.
6. Breitung, J. ve Candelon, B. (2006). Testing for short and long-run causality: A frequency domain approach. Journal of Econometrics, 132(2), 363–378.
7. Buğan, M.F. (2021). Bitcoin ve altcoin kripto para piyasalarında finansal balonlar. Akademik Araştırmalar ve Çalışmalar Dergisi, 13(24), 165-180.
8. Çağlı, E. Ç., & Evrim, P. (2017). Borsa İstanbul’da Rasyonel BalonVarlığı: Sektör Endeksleri Üzerine Bir Analiz. Finans Politik ve Ekonomik Yorumlar, (629), 63-76.

9. Canh, N.P., Wongchoti, U., Thanh, S.D. & Thonga, N.T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, 90-100.
10. Cao, G. & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China’s financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49(103070), 1-10.
11. Cheah, E.T. and Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
12. Cheung, A., Roca, E. & Su, J.J. (2015). Crypto-currency bubbles: An application of the Phillips–Shi–Yu (2013) methodology on Mt. Gox bitcoin prices. Applied Economics, 47(23), 2348–58.
13. Ciner, Ç. (2011). Eurocurrency interest rate linkages: A frequency domain analysis. International Review of Economics and Finance, 20(4), 498–505.
14. Elsayed, A.H., Gozgor, G. & Lau, C. K. M. (2020). Causality and dynamic spillovers among cryptocurrencies and currency markets. International Journal of Fİnance and Economics, 27(2), 2026-2040.
15. Enoksen, F.A., Landsnes, C. J., Lučivjanská, K. & Molnár, P. (2020). Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behavior & Organization, 176, 129-144.
16. Evlimoğlu, U. and Güder, M. (2021). Tarihteki ekonomi balonlar ışığında kripto paralara genel bakış. Abant Sosyal Bilimler Dergisi, 21(3), 469-496.
17. Francés, C. J., Carles, P. G., & Arellano, D. J. (2018). The cryptocurrency market: A network analysis. Esic Market Economics and Business Journal, 49(3), 569-583.
18. Fry, J. & Cheah, E.T. (2016). Negative bubbles and shocks in cryptocurrency markets. International Review of Financial Analysis, 47, 343-352.
19. Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal ofthe American statistical association, 77(378), 304-313.
20. Gharib, C., Mefteh-Wali, S. & Jabeur, S.B. (2021). The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets. Finance Research Letters, 38, 1-10.
21. Giudici, P., & Abu-Hashish, I. (2019). What determines bitcoin exchange prices? A network VAR approach. Finance Research Letters, 28, 309-318.
22. Homm, U., & Breitung, J. (2012). Testing for speculative bubbles in stock markets: a comparison of alternative methods. Journal of Financial Econometrics, 10(1), 198-231.
23. Hosoya, Y. (1991). The decomposition and measurement of the interdependency between second-order stationary processes. Probability theory and related fields, 88(4), 429-444.
24. Huynh, T.L.D. (2019). Spillover risks on cryptocurrency markets: A look from VAR-SVAR granger causality and student’s-t copulas. J. Risk Financial Management, 12(2), 1-19.
25. Ji, Q., Bouri, E., Lau, C.K.M. & Roubaud, D. (2019) Dynamic connectedness and integration among large cryptocurrencies. International Review of Financial Analysis, 63, 257-272.
26. Katsiampa, P., Corbet, S. & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKKMGARCH analysis. Finance Research Letters, 29, 68-74.
27. Kim, M.J., Canh, N.P. & Park, S.Y. (2021). Causal relationship among cryptocurrencies: A conditional quantile approach. Finance Research Letters, 42, 1-8.
28. Kırıkkaleli, D., Çağlar, E. & Onyibor, K. (2020). Crypto-currency: Empirical evidence from GSADF and wavelet coherence techniques. Accounting, 6, 199-208.
29. Kristoufek, L. (2013). Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Nature Scientific Reports, 3: 3415, 1-7.
30. Maouchi, Y., Charfeddine, L., and Montasser G. (2022). Understanding digital bubbles amidst the COVID-19 pandemic: Evidence from DeFi and NFTs. Finance Research Letters, 47(102584), 1-8.
31. Moratis, G. (2021). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, 101534.
32. Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, 235-240.
33. Phillips, P. C. B., Wu, Y. & Yu, J. (2011). Explosive behavior in the 1990s Nasdaq: when did exuberance escalate asset values? International Economic Review, vol. 52, no. 1, pp. 201–26.
34. Phillips, P.C.B., Shi, S. & Yu, J. (2015). Testing for multiple bubbles: historical epısodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1043-1077.
35. Polat, O. & G. Eş-Polat (2022), “Kriptopara Bağlantılılığı ve COVID-19: DieboldYılmaz ve Frekans Bağlantılılığı Yöntemleri”, Sosyoekonomi, 30(51), 283-300.
36. Şahin, E.E. (2020). Kripto para fiyatlarında balon varlığının tespiti: Bitcoin, IOTA ve Ripple örneği. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 43, 62-69.
37. Shahzad, S. J. H., Anas, M., and Bouri, E. (2022). Price explosiveness in cryptocurrencies and Elon Musk’s tweets. Finance Research Letters. 47(102695), 1-11.
38. Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering?. Finance Research Letters, 30, 385-393.
39. Souza, M.C., Souza, E.T.C. & Pereira, H.C.I. (2017). Cryptocurrencies bubbles: New evidences. The Empirical Economics Letters, 16(7), 739-746.
40. Su, C.W., Li, Z.Z., Tao, R. & Si, D.K. (2018). Testing for multiple bubbles in bitcoin markets: A generalized sup ADF test. Japan and the World Economy, 46, 56-63.
41. Tarı,R., Abasız, T. & Pehlivanoğlu, F. (2012). TEFE (ÜFE) – TÜFE fiyat endeksleri arasındaki nedensellik ilişkisi: Frekans alanı yaklaşımı” Akdeniz İ.İ.B.F. Dergisi, (24), 1 – 15.
42. Waters, G.A. & Bui, T. (2021). An empirical test for bubbles in cryptocurrency markets. Journal of Economics and Finance, 1-14.
43. Yanık, S. and Aytürk, Y. (2011). Rational speculative bubbles in Istanbul Stock Exchange. Muhasebe ve Finansman Dergisi, 51, 175-190.
44. Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. D.L.K. Chuen (Ed.), In: Handbook of Digital Currency, (pp 31-43), Cambridge: Elsevier.
45. Yi, S., Xu, Z. & Wang, G.J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? International Review of Financial Analysis, 60, 98–114.