IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1709.08621.html
   My bibliography  Save this paper

A sentiment-based model for the BitCoin: theory, estimation and option pricing

Author

Listed:
  • Alessandra Cretarola
  • Gianna Fig`a-Talamanca
  • Marco Patacca

Abstract

In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.

Suggested Citation

  • Alessandra Cretarola & Gianna Fig`a-Talamanca & Marco Patacca, 2017. "A sentiment-based model for the BitCoin: theory, estimation and option pricing," Papers 1709.08621, arXiv.org.
  • Handle: RePEc:arx:papers:1709.08621
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1709.08621
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    2. Ladislav Kristoufek, 2015. "What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    3. repec:men:wpaper:58_2015 is not listed on IDEAS
    4. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    5. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
    6. Moshe Arye Milevsky & Steven E. Posner, 1999. "Asian Options, The Sum Of Lognormals, And The Reciprocal Gamma Distribution," World Scientific Book Chapters, in: Marco Avellaneda (ed.), Quantitative Analysis In Financial Markets Collected Papers of the New York University Mathematical Finance Seminar, chapter 7, pages 203-218, World Scientific Publishing Co. Pte. Ltd..
    7. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    8. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    9. David Yermack, 2013. "Is Bitcoin a Real Currency? An economic appraisal," NBER Working Papers 19747, National Bureau of Economic Research, Inc.
    10. Levy, Edmond, 1992. "Pricing European average rate currency options," Journal of International Money and Finance, Elsevier, vol. 11(5), pages 474-491, October.
    11. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    12. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    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. Adam Hayes, 2018. "Bitcoin price and its marginal cost of production: support for a fundamental value," Papers 1805.07610, arXiv.org.
    2. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    3. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    4. Goodell, John W. & Alon, Ilan & Chiaramonte, Laura & Dreassi, Alberto & Paltrinieri, Andrea & Piserà, Stefano, 2023. "Risk substitution in cryptocurrencies: Evidence from BRICS announcements," Emerging Markets Review, Elsevier, vol. 54(C).
    5. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.

    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. Alessandra Cretarola & Gianna Figà-Talamanca & Marco Patacca, 2020. "Market attention and Bitcoin price modeling: theory, estimation and option pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 187-228, June.
    2. Alessandra Cretarola & Gianna Fig`a-Talamanca, 2017. "A confidence-based model for asset and derivative prices in the BitCoin market," Papers 1702.00215, arXiv.org.
    3. Gouriéroux, Christian & Monfort, Alain & Tenreiro, Carlos, 1994. "Kernel m-estimators : non parametric diagnostics for structural models," CEPREMAP Working Papers (Couverture Orange) 9405, CEPREMAP.
    4. Georges Dionne & Genevieve Gauthier & Nadia Ouertani & Nabil Tahani, 2011. "Heterogeneous Basket Options Pricing Using Analytical Approximations," Multinational Finance Journal, Multinational Finance Journal, vol. 15(1-2), pages 47-85, March - J.
    5. Guégan, Dominique & Ielpo, Florian & Lalaharison, Hanjarivo, 2013. "Option pricing with discrete time jump processes," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2417-2445.
    6. Wooldridge, Jeffrey M., 2014. "Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 182(1), pages 226-234.
    7. John Mullahy, 2010. "Multivariate Fractional Regression Estimation of Econometric Share Models," NBER Working Papers 16354, National Bureau of Economic Research, Inc.
    8. Chen, Willa W. & Deo, Rohit S., 2006. "Estimation of mis-specified long memory models," Journal of Econometrics, Elsevier, vol. 134(1), pages 257-281, September.
    9. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    10. repec:gnv:wpaper:unige:76321 is not listed on IDEAS
    11. Dietmar Harhoff, 1999. "Firm Formation And Regional Spillovers - Evidence From Germany," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 8(1-2), pages 27-55.
    12. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    13. Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.
    14. C. Gouriéroux & A. Monfort & J.‐M. Zakoïan, 2019. "Consistent Pseudo‐Maximum Likelihood Estimators and Groups of Transformations," Econometrica, Econometric Society, vol. 87(1), pages 327-345, January.
    15. Francisco Blasques & Christian Francq & Sébastien Laurent, 2020. "A New Class of Robust Observation-Driven Models," Tinbergen Institute Discussion Papers 20-073/III, Tinbergen Institute.
    16. Gabriele Fiorentini & Enrique Sentana, 2021. "Specification tests for non‐Gaussian maximum likelihood estimators," Quantitative Economics, Econometric Society, vol. 12(3), pages 683-742, July.
    17. Wooldridge, Jeffrey M., 2020. "On the consistency of the logistic quasi-MLE under conditional symmetry," Economics Letters, Elsevier, vol. 194(C).
    18. Geon Ho Choe & Minseok Kim, 2021. "Closed‐form lower bounds for the price of arithmetic average Asian options by multiple conditioning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 1916-1932, December.
    19. Antoine, Bertille & Dovonon, Prosper, 2021. "Robust estimation with exponentially tilted Hellinger distance," Journal of Econometrics, Elsevier, vol. 224(2), pages 330-344.
    20. Moeltner, Klaus, 2003. "Addressing aggregation bias in zonal recreation models," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 128-144, January.
    21. Gouriéroux, Christian, 1994. "Modèles économétriques : utilisation et interprétation (les)," CEPREMAP Working Papers (Couverture Orange) 9423, CEPREMAP.

    More about this item

    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:arx:papers:1709.08621. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.