IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v43y2021ics1544612321001033.html
   My bibliography  Save this article

Price volatilities of bitcoin futures

Author

Listed:
  • Guo, Zi-Yi

Abstract

In this study, we first investigate the volatility term structure of the Bitcoin futures prices and observe that the price volatilities of Bitcoin futures contracts decrease as the delivery date nears, which is opposite to the Samuelson effect frequently observed in the commodity market. Then, we modify the stochastic multifactor model in Schwartz and Smith (2000) and fit it to the Bitcoin futures prices data. The results show that the standard stochastic multifactor model performs well in explaining the fluctuations of Bitcoin futures prices and generates an upward volatility term structure of the Bitcoin futures contracts prices. Nevertheless, some of the parameter's estimations exhibit non-negligible differences compared with those based on other commodity futures prices data in the existing literature, which indicates that the Bitcoin futures market might be different from the standard commodity futures market.

Suggested Citation

  • Guo, Zi-Yi, 2021. "Price volatilities of bitcoin futures," Finance Research Letters, Elsevier, vol. 43(C).
    • Handle: RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321001033
    DOI: 10.1016/j.frl.2021.102022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612321001033
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2021.102022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    2. Carol Alexander & Jaehyuk Choi & Heungju Park & Sungbin Sohn, 2020. "BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 23-43, January.
    3. Zi‐Yi Guo, 2020. "Stochastic multifactor models in risk management of energy futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(12), pages 1918-1934, December.
    4. Duong, Huu Nhan & Kalev, Petko S., 2008. "The Samuelson hypothesis in futures markets: An analysis using intraday data," Journal of Banking & Finance, Elsevier, vol. 32(4), pages 489-500, April.
    5. Edouard Jaeck & Delphine Lautier, 2016. "Volatility in electricity derivative markets: the Samuelson effect revisited," Post-Print hal-01488127, HAL.
    6. Carsten Sørensen, 2002. "Modeling seasonality in agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(5), pages 393-426, May.
    7. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    8. Andrés García-Mirantes & Beatriz Larraz & Javier Población, 2016. "An alternative method to estimate parameters in modelling the behaviour of commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 16(7), pages 1111-1127, July.
    9. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    10. Cortazar, Gonzalo & Schwartz, Eduardo S., 2003. "Implementing a stochastic model for oil futures prices," Energy Economics, Elsevier, vol. 25(3), pages 215-238, May.
    11. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    12. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Yoon, Seong-Min, 2018. "Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets," Finance Research Letters, Elsevier, vol. 27(C), pages 228-234.
    13. Takahiro Hattori & Ryo Ishida, 2021. "The relationship between arbitrage in futures and spot markets and Bitcoin price movements: Evidence from the Bitcoin markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 105-114, January.
    14. Jaeck, Edouard & Lautier, Delphine, 2016. "Volatility in electricity derivative markets: The Samuelson effect revisited," Energy Economics, Elsevier, vol. 59(C), pages 300-313.
    15. Gonzalo Cortazar & Lorenzo Naranjo, 2006. "An N‐factor Gaussian model of oil futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(3), pages 243-268, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yan, Lei & Mirza, Nawazish & Umar, Muhammad, 2022. "The cryptocurrency uncertainties and investment transitions: Evidence from high and low carbon energy funds in China," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.
    3. Guo, Zi-Yi, 2022. "Risk management of Bitcoin futures with GARCH models," Finance Research Letters, Elsevier, vol. 45(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Power, Gabriel J. & Eaves, James & Turvey, Calum & Vedenov, Dmitry, 2017. "Catching the curl: Wavelet thresholding improves forward curve modelling," Economic Modelling, Elsevier, vol. 64(C), pages 312-321.
    2. Andrés García-Mirantes & Beatriz Larraz & Javier Población, 2020. "A Proposal to Fix the Number of Factors on Modeling the Dynamics of Futures Contracts on Commodity Prices," Mathematics, MDPI, vol. 8(6), pages 1-13, June.
    3. Población, Javier & Serna, Gregorio, 2016. "Is the refining margin stationary?," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 169-186.
    4. Zi-Yi Guo & Yangxiaoteng Luo, 2017. "Dynamic Stochastic Factors, Risk Management and the Energy Futures," International Business Research, Canadian Center of Science and Education, vol. 10(9), pages 50-59, September.
    5. Andrés García Mirantes & Javier Población & Gregorio Serna, 2012. "The Stochastic Seasonal Behaviour of Natural Gas Prices," European Financial Management, European Financial Management Association, vol. 18(3), pages 410-443, June.
    6. Guo, Zi-Yi, 2017. "Models with Short-Term Variations and Long-Term Dynamics in Risk Management of Commodity Derivatives," EconStor Preprints 167619, ZBW - Leibniz Information Centre for Economics.
    7. Cheng, Benjamin & Nikitopoulos, Christina Sklibosios & Schlögl, Erik, 2018. "Pricing of long-dated commodity derivatives: Do stochastic interest rates matter?," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 148-166.
    8. Gonzalo Cortazar & Simon Gutierrez & Hector Ortega, 2016. "Empirical Performance of Commodity Pricing Models: When is it Worthwhile to Use a Stochastic Volatility Specification?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 457-487, May.
    9. V., Ernesto Guerra & H., Eugenio Bobenrieth & H., Juan Bobenrieth & Wright, Brian D., 2023. "Endogenous thresholds in energy prices: Modeling and empirical estimation," Energy Economics, Elsevier, vol. 121(C).
    10. Cortazar, Gonzalo & Lopez, Matias & Naranjo, Lorenzo, 2017. "A multifactor stochastic volatility model of commodity prices," Energy Economics, Elsevier, vol. 67(C), pages 182-201.
    11. Javier Población, 2017. "Are recent tanker freight rates stationary?," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 650-666, December.
    12. Luis M. Abadie & José M. Chamorro, 2009. "Monte Carlo valuation of natural gas investments," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 10-22, January.
    13. Max F. Schöne & Stefan Spinler, 2017. "A four-factor stochastic volatility model of commodity prices," Review of Derivatives Research, Springer, vol. 20(2), pages 135-165, July.
    14. Shao, Chengwu & Bhar, Ramaprasad & Colwell, David B., 2015. "A multi-factor model with time-varying and seasonal risk premiums for the natural gas market," Energy Economics, Elsevier, vol. 50(C), pages 207-214.
    15. Delphine H. Lautier, Franck Raynaud, and Michel A. Robe, 2019. "Shock Propagation Across the Futures Term Structure: Evidence from Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    16. Cortazar, Gonzalo & Kovacevic, Ivo & Schwartz, Eduardo S., 2015. "Expected commodity returns and pricing models," Energy Economics, Elsevier, vol. 49(C), pages 60-71.
    17. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    18. Hoang‐Long Phan & Ralf Zurbruegg, 2020. "The time‐to‐maturity pattern of futures price sensitivity to news," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 126-144, January.
    19. Zi‐Yi Guo, 2020. "Stochastic multifactor models in risk management of energy futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(12), pages 1918-1934, December.
    20. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print halshs-01183166, HAL.

    More about this item

    Keywords

    Bitcoin; Samuelson effect; Stochastic multifactor model;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321001033. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.