Predicting crypto-currencies using sparse non-Gaussian state space models
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- Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
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- Jin-Bom Han & Sun-Hak Kim & Myong-Hun Jang & Kum-Sun Ri, 2020. "Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 337-353, August.
- Camilla Muglia & Luca Santabarbara & Stefano Grassi, 2019. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?," JRFM, MDPI, vol. 12(2), pages 1-10, May.
- Hachicha, Fatma & Masmoudi, Afif & Abid, Ilyes & Obeid, Hassan, 2023. "Herding behavior in exploring the predictability of price clustering in cryptocurrency market," Finance Research Letters, Elsevier, vol. 57(C).
- Theodore Panagiotidis & Thanasis Stengos & Orestis Vravosinos, 2020.
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JRFM, MDPI, vol. 13(2), pages 1-10, February.
- Thanasis Stengos & Theodore Panagiotidis & Orestis Vravosinos, 2020. "A principal component-guided sparse regression approach for the determination of bitcoin returns," Working Papers 2001, University of Guelph, Department of Economics and Finance.
- Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
- Rick Bohte & Luca Rossini, 2019.
"Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models,"
JRFM, MDPI, vol. 12(3), pages 1-18, September.
- Rick Bohte & Luca Rossini, 2019. "Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models," Papers 1909.06599, arXiv.org.
- Gupta, Rangan & Huber, Florian & Piribauer, Philipp, 2020.
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International Review of Financial Analysis, Elsevier, vol. 68(C).
- Rangan Gupta & Florian Huber & Philipp Piribauer, 2018. "Predicting International Equity Returns: Evidence from Time-Varying Parameter Vector Autoregressive Models," Working Papers 201826, University of Pretoria, Department of Economics.
- Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
- Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
- Pattnaik, Debidutta & Hassan, M. Kabir & Dsouza, Arun & Tiwari, Aviral & Devji, Shridev, 2023. "Ex-post facto analysis of cryptocurrency literature over a decade using bibliometric technique," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
- Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers 2011.03741, arXiv.org, revised Dec 2020.
- Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach," Future Internet, MDPI, vol. 12(3), pages 1-19, March.
- Tak Kuen Siu, 2023. "Bayesian nonlinear expectation for time series modelling and its application to Bitcoin," Empirical Economics, Springer, vol. 64(1), pages 505-537, January.
- Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
- Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
- Cássio Roberto de Andrade Alves & Márcio Laurini, 2023. "Estimating the Capital Asset Pricing Model with Many Instruments: A Bayesian Shrinkage Approach," Mathematics, MDPI, vol. 11(17), pages 1-20, September.
- Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Shaen Corbet & Les Oxley, 2023. "Investigating the Academic Response to Cryptocurrencies: Insights from Research Diversification as Separated by Journal Ranking," Review of Corporate Finance, now publishers, vol. 3(4), pages 487-528, September.
- Anoop S Kumar & Taufeeq Ajaz, 2019. "Co-movement in crypto-currency markets: evidences from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
- Pratha Khandelwal & Philip Nadler & Rossella Arcucci & William Knottenbelt & Yi-Ke Guo, 2021. "A Scalable Inference Method For Large Dynamic Economic Systems," Papers 2110.14346, arXiv.org.
- Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
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This paper has been announced in the following NEP Reports:- NEP-ECM-2018-02-05 (Econometrics)
- NEP-ETS-2018-02-05 (Econometric Time Series)
- NEP-FOR-2018-02-05 (Forecasting)
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