Everything you always wanted to know about bitcoin modelling but were afraid to ask
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- Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
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Citations
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- Schilling, Linda & Uhlig, Harald, 2019.
"Some simple bitcoin economics,"
Journal of Monetary Economics, Elsevier, vol. 106(C), pages 16-26.
- Linda Schilling & Harald Uhlig, 2018. "Some Simple Bitcoin Economics," NBER Working Papers 24483, National Bureau of Economic Research, Inc.
- Uhlig, Harald & Schilling, Linda, 2018. "Some simple Bitcoin Economics," CEPR Discussion Papers 12831, C.E.P.R. Discussion Papers.
- Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2023.
"Equilibrium Bitcoin Pricing,"
Journal of Finance, American Finance Association, vol. 78(2), pages 967-1014, April.
- Biais, Bruno & Bisière, Christophe & Bouvard, Matthieu & Casamatta, Catherine & Menkveld, Albert J., 2018. "Equilibrium Bitcoin Pricing," TSE Working Papers 18-973, Toulouse School of Economics (TSE), revised Feb 2022.
- Bruno Biais & Albert Menkveld & Catherine Casamatta & Christophe Bisière & Matthieu Bouvard, 2019. "Equilibrium Bitcoin Pricing," 2019 Meeting Papers 360, Society for Economic Dynamics.
- Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2020. "Equilibrium Bitcoin Pricing," EconPol Working Paper 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
- Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2023. "Equilibrium bitcoin pricing," Post-Print hal-04067665, HAL.
- Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021.
"Is It Possible to Forecast the Price of Bitcoin?,"
Forecasting, MDPI, vol. 3(2), pages 1-44, May.
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- Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Post-Print halshs-04250269, HAL.
- Viviane Naimy & Omar Haddad & Gema Fernández-Avilés & Rim El Khoury, 2021. "The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.
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Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
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- Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2018. "Cryptocurrencies, Metcalfe's law and LPPL models," IRTG 1792 Discussion Papers 2018-056, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Zura Kakushadze & Jim Kyung-Soo Liew, 2018. "CryptoRuble: From Russia with Love," Papers 1801.05760, arXiv.org.
- Sofoklis Vogiazas & Constantinos Alexiou, 2019. "Bitcoin: The Road to Hell Is Paved With Good Promises," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 48(1), February.
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International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
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- Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
- Caporale, Guglielmo Maria & Zekokh, Timur, 2019.
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- Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2019. "Metcalfe's law and herding behaviour in the cryptocurrencies market," Economics Discussion Papers 2019-16, Kiel Institute for the World Economy (IfW Kiel).
- Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2019. "Metcalfe's law and log-period power laws in the cryptocurrencies market," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-26.
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More about this item
Keywords
Bitcoin; Crypto-currencies; Hash rate; Investors' attractiveness; Social interactions; Money supply; Money Demand; Speculation; Forecasting; Algorithmic trading; Bubble; Price discovery.;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
- E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
- E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
- E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-MAC-2016-06-18 (Macroeconomics)
- NEP-MON-2016-06-18 (Monetary Economics)
- NEP-PAY-2016-06-18 (Payment Systems and Financial Technology)
Statistics
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