Information transmission between bitcoin derivatives and spot markets: high-frequency causality analysis with Fourier approximation


  • Efe Caglar Cagli Dokuz Eylul University
  • Pinar Evrim Mandaci Dokuz Eylul University



Bitcoin, Price Discovery, High-Frequency Data, Fourier Approximation, Structural Shifts


This paper examines information transmission between Bitcoin derivatives and spot exchanges using 15-minutes interval data over May 2016 - September 2020. We employ a novel econometric framework with Fourier approximation, taking structural shifts in causal linkages, on the prices, returns, and volatilities of BitMEX, the derivatives market, and five other major spot exchanges, Coinbase, Bitstamp, Kraken,, and Poloniex. Overall, the results provide robust evidence of information flow between the derivatives and spot exchanges, implying the markets react to new information simultaneously. The results are of importance for investors conducting portfolio allocation exercises and risk management strategies.

Author Biography

Pinar Evrim Mandaci, Dokuz Eylul University

Professor of Finance

Department of Business Administration
Faculty of Business
Dokuz Eylul University


Alexander, C., Choi, J., Park, H., and Sohn, S. (2020) BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness, Journal of Futures Markets, 40(1), 23–43.

Alexander, C., and Heck, D. (2019) Price Discovery and Efficiency in Bitcoin Markets. SSRN Electronic Journal.

Bariviera, A. F. (2017) The inefficiency of Bitcoin revisited: A dynamic approach, Economics Letters, 161, 1–4.

Baur, D. G., and Dimpfl, T. (2019) Price discovery in bitcoin spot or futures? Journal of Futures Markets, 39(7), 803–817.

Brandvold, M., Molnár, P., Vagstad, K., and Andreas Valstad, O. C. (2015) Price discovery on Bitcoin exchanges, Journal of International Financial Markets, Institutions and Money, 36, 18–35.

Brauneis, A., and Mestel, R. (2018) Price discovery of cryptocurrencies: Bitcoin and beyond, Economics Letters, 165, 58–61.

Corbet, S., Lucey, B., Peat, M., and Vigne, S. (2018) Bitcoin Futures—What use are they? Economics Letters, 172, 23–27.

Diebold, F. X., and Yilmaz, K. (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers, International Journal of Forecasting, 28(1), 57–66.

Enders, W., and Jones, P. (2016) Grain prices, oil prices, and multiple smooth breaks in a VAR, Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.

Enders, W., and Lee, J. (2012) The flexible Fourier form and Dickey-Fuller type unit root tests, Economics Letters, 117(1), 196–199.

Fortune Business Insights (2020) Cryptocurrency Market, Global Analysis, Insights and Forecast.

Giudici, P., and Pagnottoni, P. (2019) High frequency price change spillovers in bitcoin markets, Risks, 7(4), 111.

Gormus, A., Nazlioglu, S., and Soytas, U. (2018) High-yield bond and energy markets, Energy Economics, 69, 101–110.

International Banker. (2020) Crypto Derivatives Are on the Rise, International Banker.

Jarque, C. M., and Bera, A. K. (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters, 6(3), 255–259.

Ji, Q., Bouri, E., Kristoufek, L., and Lucey, B. (2019) Realised volatility connectedness among Bitcoin exchange markets, Finance Research Letters, 101391.

Jiang, Y., Nie, H., and Ruan, W. (2018) Time-varying long-term memory in Bitcoin market, Finance Research Letters, 25, 280–284.

Kapar, B., and Olmo, J. (2019) An analysis of price discovery between Bitcoin futures and spot markets, Economics Letters, 174, 62–64.

Kristoufek, L. (2018) On Bitcoin markets (in)efficiency and its evolution, Physica A: Statistical Mechanics and Its Applications, 503, 257–262.

Nadarajah, S., and Chu, J. (2017) On the inefficiency of Bitcoin, Economics Letters, 150, 6–9.

Nazlioglu, S., Gormus, A., and Soytas, U. (2019) Oil Prices and Monetary Policy in Emerging Markets: Structural Shifts in Causal Linkages, Emerging Markets Finance and Trade, 55(1), 105–117.

Nazlioglu, S., Gormus, N. A., and Soytas, U. (2016) Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis, Energy Economics, 60, 168–175.

Pagnottoni, P., and Dimpfl, T. (2019) Price discovery on Bitcoin markets, Digital Finance, 1(1–4), 139–161.

Parkinson, M. (1980) The Extreme Value Method for Estimating the Variance of the Rate of Return, The Journal of Business, 53(1), 65.

Sensoy, 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., and Roubaud, D. (2018) Informational efficiency of Bitcoin—An extension, Economics Letters, 163, 106–109.

Toda, H. Y., and Yamamoto, T. (1995) Statistical inference in vector autoregressions with possibly integrated processes, Journal of Econometrics, 66(1–2), 225–250.

Urquhart, A. (2016) The inefficiency of Bitcoin, Economics Letters, 148, 80–82.




How to Cite

Cagli, E. C., & Evrim Mandaci, P. (2021). Information transmission between bitcoin derivatives and spot markets: high-frequency causality analysis with Fourier approximation. Economics and Business Letters, 10(4), 394–402.