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Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models

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  • Rick Bohte
  • Luca Rossini

Abstract

This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.

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  • Rick Bohte & Luca Rossini, 2019. "Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models," Papers 1909.06599, arXiv.org.
    • Handle: RePEc:arx:papers:1909.06599
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    Cited by:

    1. Karl Oton Rudolf & Samer Ajour El Zein & Nicola Jackman Lansdowne, 2021. "Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis," Risks, MDPI, vol. 9(9), pages 1-22, August.
    2. Stefan Simeonov & Theodor Todorov & Daniel Nikolaev, 2020. "Testing Methods And Models To Forecast Cryptocurrencies Exchange Rate," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 10-26.
    3. Paolo Angelis & Roberto Marchis & Mario Marino & Antonio Luciano Martire & Immacolata Oliva, 2021. "Betting on bitcoin: a profitable trading between directional and shielding strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 883-903, December.
    4. Mauro Bernardi & Stefano Grassi & Francesco Ravazzolo, 2020. "Bayesian Econometrics," JRFM, MDPI, vol. 13(11), pages 1-2, October.
    5. Ana Fern'andez Vilas & Rebeca P. D'iaz Redondo & Daniel Couto Cancela & Alejandro Torrado Pazos, 2023. "Interplay between Cryptocurrency Transactions and Online Financial Forums," Papers 2401.10238, arXiv.org.
    6. Daniel Ogachi & Paul Mugambi & Lydia Bares & Zoltan Zeman, 2021. "Idiosyncrasies of Money: 21st Century Evolution of Money," Economies, MDPI, vol. 9(1), pages 1-19, March.
    7. Ying Chen & Paolo Giudici & Branka Hadji Misheva & Simon Trimborn, 2020. "Lead Behaviour in Bitcoin Markets," Risks, MDPI, vol. 8(1), pages 1-14, January.
    8. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    9. Onur Özdemir, 2022. "Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
    10. Ana Fernández Vilas & Rebeca P. Díaz Redondo & Daniel Couto Cancela & Alejandro Torrado Pazos, 2021. "Interplay between Cryptocurrency Transactions and Online Financial Forums," Mathematics, MDPI, vol. 9(4), pages 1-22, February.
    11. Samet Gunay & Kerem Kaskaloglu & Shahnawaz Muhammed, 2021. "Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants," Mathematics, MDPI, vol. 9(12), pages 1-18, June.

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    JEL classification:

    • C - Mathematical and Quantitative Methods
    • E - Macroeconomics and Monetary Economics
    • F2 - International Economics - - International Factor Movements and International Business
    • F3 - International Economics - - International Finance
    • G - Financial Economics

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