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BITCOIN FİYATLARINDA EŞİK DEĞER ETKİSİ

Year 2019, Volume: 6 Issue: 3, 669 - 681, 31.12.2019

Eray Gemici Müslüm Polat

https://doi.org/10.30798/makuiibf.593500
Cited By: 2

Abstract

Bu çalışma, Bitcoin’in fiyat davranışını
otoregresif birim kökü olan iki rejimli bir TAR modeli kullanarak araştırmaktadır.
Çalışmada, durağan dışılığı ve doğrusal olmamayı eş zamanlı olarak sınayan
Caner ve Hansen (2001) tarafından geliştirilen yöntem kullanılmıştır. Bu amaçla, 16.07.2010 – 27.11.2018
dönemi için (3.056 adet günlük gözlem) Bitcoin kapanış fiyatlarına ait veri
seti oluşturularak Bitcoin fiyatlarının etkin olup olmadığı incelenmiştir.  Elde edilen bulgular, Bitcoin fiyatlarının tüm
dönem dikkate alındığında zayıf formda etkin piyasalar hipotezini desteklemektedir.
Ancak rejimler arası geçiş dikkate alındığında Bitcoin fiyat serisinde iki
rejim olduğu sonucuna ulaşılmıştır. Birinci rejimde zayıf forma etkin piyasalar
hipotezinin geçerli olduğu, ancak ikinci rejimde geçerli olmadığı tespit
edilmiştir.

Keywords

Bitcoin Eşik Değer TAR Model; Etkin Piyasalar Hipotezi

References

  • AL-YAHYAEE, K. H., MENSI, W., YOON, S. M. (2018), Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234.
  • ALMUDHAF, F. (2018), Pricing efficiency of Bitcoin trusts. Applied Economics Letters, 25(7), 504-508.
  • ARDIA, D., BLUTEAU, K., RUEDE, M. (2019), Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
  • AYSAN, A. F., DEMİR, E., GÖZGÖR, G., LAU, C. K. M. (2019), Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business and Finance, 47, 511-518.
  • BAEK, C., ELBECK, M. (2015), Bitcoins as an Investment or Speculative Vehicle? A first look. Applied Economics Letters, 22(1), 30-34.
  • BALCILAR, M., BOURI, E., GUPTA, R., ROUBAUD, D. (2017), Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • BARIVIERA, A. F. (2017), The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • BRANDVOLD, M., MOLNÁR, P., VAGSTAD, K.,VALSTAD, O. C. A. (2015), Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
  • CANER, M., HANSEN, B. E. (2001), Threshold autoregression with a unit Root. Econometrica, 69(6), 1555-1596.
  • CAPORALE, G. M., GIL-ALANA, L., PLASTUN, A. (2018), Persistence in the cryptocurrency market. Research in International Business and Finance, 46, 141-148.
  • CHARLES, A., DARNÉ, O. (2018), Volatility estimation for bitcoin: Replication and extension. International Economics.
  • CHEAH, E. T., FRY, J. (2015), Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • 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), 2348-2358.
  • CIAIAN, P., RAJCANIOVA, M., KANCS, D. A. (2016), The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
  • CORBET, S., LUCEY, B., YAROVAYA, L. (2018), Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
  • DEMİR, E., GÖZGÖR, G., LAU, C. K. M.,VIGNE, S. A. (2018), Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145-149.
  • DYHRBERG, A. H. (2016), Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • DWYER, G. P. (2015), The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability, 17, 81-91.
  • FAMA, E. F. (1970), Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
  • HANSEN, B. (1999), Testing for linearity. Journal of economic surveys, 13(5), 551-576.
  • HONG, K. (2017), Bitcoin as an Alternative Investment Vehicle. Information Technology and Management, 18(4), 265-275.
  • JIANG, Y., NIE, H.,RUAN, W. (2018), Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284.
  • KOÇOĞLU, Ş., ÇEVİK, Y. E., TANRIÖVEN, C. (2016). Bitcoin piyasalarının etkinliği, likiditesi ve oynaklığı. İşletme Araştırmaları Dergisi, 8(2), 77-97.
  • KRISTOUFEK, L. (2018), On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257-262.
  • NADARAJAH, S., CHU, J. (2017), On the inefficiency of Bitcoin. Economics Letters, 150, 6-9.
  • PANAGIOTIDIS, T., STENGOS, T., VRAVOSINOS, O. (2018), On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, 235-240.
  • PHILLIP, A., CHAN, J. S., PEIRIS, S. (2018), A new look at Cryptocurrencies. Economics Letters, 163, 6-9.
  • ŞENSOY, A. (2019), The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73.
  • TIWARI, A. K., JANA, R. K., DAS, D., ROUBAUD, D. (2018), Informational efficiency of Bitcoin—An extension. Economics Letters, 163, 106-109.
  • URQUHART, A. (2016), The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • URQUHART, A. (2017), Price clustering in Bitcoin. Economics letters, 159, 145-148.
  • VIDAL-TOMÁS, D., IBAÑEZ, A. (2018), Semi-strong efficiency of Bitcoin. Finance Research Letters, 27, 259-265.

THRESHOLD EFFECT IN BITCOIN PRICES

Year 2019, Volume: 6 Issue: 3, 669 - 681, 31.12.2019

Eray Gemici Müslüm Polat

https://doi.org/10.30798/makuiibf.593500
Cited By: 2

Abstract

This study
investigates the price behavior of Bitcoin using a two-regime TAR model, which
is an autoregressive unit root. In the study, the method developed by Caner and
Hansen (2001) was used which simultaneously tested non-stationary and
non-linearity. For this purpose, the data set of Bitcoin closing prices for
16.07.2010 - 27.11.2018 period (3.056 daily observations) has been created to
determine whether Bitcoin prices are efficient or not. The findings support the
hypothesis that Bitcoin prices are efficient in weak form for the whole period.
However, considering the switching
between the regimes, it was concluded that there are two regimes in the Bitcoin
price series. In the first regime, the hypothesis of efficient markets in the
weak form is valid, but not in the second regime.

Keywords

Bitcoin Threshold Value; TAR Model Efficient Market Hypothesis

References

  • AL-YAHYAEE, K. H., MENSI, W., YOON, S. M. (2018), Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234.
  • ALMUDHAF, F. (2018), Pricing efficiency of Bitcoin trusts. Applied Economics Letters, 25(7), 504-508.
  • ARDIA, D., BLUTEAU, K., RUEDE, M. (2019), Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
  • AYSAN, A. F., DEMİR, E., GÖZGÖR, G., LAU, C. K. M. (2019), Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business and Finance, 47, 511-518.
  • BAEK, C., ELBECK, M. (2015), Bitcoins as an Investment or Speculative Vehicle? A first look. Applied Economics Letters, 22(1), 30-34.
  • BALCILAR, M., BOURI, E., GUPTA, R., ROUBAUD, D. (2017), Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • BARIVIERA, A. F. (2017), The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • BRANDVOLD, M., MOLNÁR, P., VAGSTAD, K.,VALSTAD, O. C. A. (2015), Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
  • CANER, M., HANSEN, B. E. (2001), Threshold autoregression with a unit Root. Econometrica, 69(6), 1555-1596.
  • CAPORALE, G. M., GIL-ALANA, L., PLASTUN, A. (2018), Persistence in the cryptocurrency market. Research in International Business and Finance, 46, 141-148.
  • CHARLES, A., DARNÉ, O. (2018), Volatility estimation for bitcoin: Replication and extension. International Economics.
  • CHEAH, E. T., FRY, J. (2015), Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • 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), 2348-2358.
  • CIAIAN, P., RAJCANIOVA, M., KANCS, D. A. (2016), The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
  • CORBET, S., LUCEY, B., YAROVAYA, L. (2018), Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
  • DEMİR, E., GÖZGÖR, G., LAU, C. K. M.,VIGNE, S. A. (2018), Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145-149.
  • DYHRBERG, A. H. (2016), Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • DWYER, G. P. (2015), The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability, 17, 81-91.
  • FAMA, E. F. (1970), Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
  • HANSEN, B. (1999), Testing for linearity. Journal of economic surveys, 13(5), 551-576.
  • HONG, K. (2017), Bitcoin as an Alternative Investment Vehicle. Information Technology and Management, 18(4), 265-275.
  • JIANG, Y., NIE, H.,RUAN, W. (2018), Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284.
  • KOÇOĞLU, Ş., ÇEVİK, Y. E., TANRIÖVEN, C. (2016). Bitcoin piyasalarının etkinliği, likiditesi ve oynaklığı. İşletme Araştırmaları Dergisi, 8(2), 77-97.
  • KRISTOUFEK, L. (2018), On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257-262.
  • NADARAJAH, S., CHU, J. (2017), On the inefficiency of Bitcoin. Economics Letters, 150, 6-9.
  • PANAGIOTIDIS, T., STENGOS, T., VRAVOSINOS, O. (2018), On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, 235-240.
  • PHILLIP, A., CHAN, J. S., PEIRIS, S. (2018), A new look at Cryptocurrencies. Economics Letters, 163, 6-9.
  • ŞENSOY, A. (2019), The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73.
  • TIWARI, A. K., JANA, R. K., DAS, D., ROUBAUD, D. (2018), Informational efficiency of Bitcoin—An extension. Economics Letters, 163, 106-109.
  • URQUHART, A. (2016), The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • URQUHART, A. (2017), Price clustering in Bitcoin. Economics letters, 159, 145-148.
  • VIDAL-TOMÁS, D., IBAÑEZ, A. (2018), Semi-strong efficiency of Bitcoin. Finance Research Letters, 27, 259-265.
There are 32 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Eray Gemici 0000-0001-5449-0568

Müslüm Polat 0000-0003-1198-4693

Publication Date December 31, 2019
Submission Date July 18, 2019
Published in Issue Year 2019 Volume: 6 Issue: 3

Cite

APA Gemici, E., & Polat, M. (2019). BITCOIN FİYATLARINDA EŞİK DEĞER ETKİSİ. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 6(3), 669-681. https://doi.org/10.30798/makuiibf.593500

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