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Does market attention affect Bitcoin returns and volatility?

Author

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  • Gianna Figá-Talamanca

    (University of Perugia)

  • Marco Patacca

    (University of Perugia
    Léonard de Vinci Pôle Universitaire, Research Center)

Abstract

In this paper, we analyze the relative impact of attention measures either on the mean or on the variance of Bitcoin returns by fitting nonlinear econometric models to historical data: Two non-overlapping subsamples are considered from January 1, 2012, to December 31, 2017. Outcomes confirm that market attention has an impact on Bitcoin returns and volatility, when measured by applying several transformations on time series for the trading volume or the SVI Google searches index. Specifically, best candidate models are selected via the so-called Box–Jenkins methodology and by maximizing out-of-sample forecasting performance. Overall, we can conclude that trading volume-related measures affect both the mean and the volatility of the cryptocurrency returns, while Internet searches volume mainly affects the volatility. An interesting side finding is that the inclusion of attention measures in model specification makes forecast estimates more accurate.

Suggested Citation

  • Gianna Figá-Talamanca & Marco Patacca, 2019. "Does market attention affect Bitcoin returns and volatility?," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 135-155, June.
    • Handle: RePEc:spr:decfin:v:42:y:2019:i:1:d:10.1007_s10203-019-00258-7
    DOI: 10.1007/s10203-019-00258-7
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    References listed on IDEAS

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    Cited by:

    1. Cretarola, Alessandra & Figà-Talamanca, Gianna, 2020. "Bubble regime identification in an attention-based model for Bitcoin and Ethereum price dynamics," Economics Letters, Elsevier, vol. 191(C).
    2. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    3. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021. "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    5. Murat Akkaya, 2021. "The Determinants of the Volatility in Cryptocurrency Markets: The Bitcoin Case," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 35(1), pages 87-97.
    6. Nikolaos A. Kyriazis, 2021. "Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 845-861, December.
    7. 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.
    8. Davide Provenzano & Rodolfo Baggio, 2021. "Complexity traits and synchrony of cryptocurrencies price dynamics," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 941-955, December.
    9. Ibikunle, Gbenga & McGroarty, Frank & Rzayev, Khaladdin, 2020. "More heat than light: Investor attention and bitcoin price discovery," International Review of Financial Analysis, Elsevier, vol. 69(C).
    10. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2023. "An analysis of the return–volume relationship in decentralised finance (DeFi)," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 236-254.
    11. Hu, Yitong & Shen, Dehua & Urquhart, Andrew, 2023. "Attention allocation and cryptocurrency return co-movement: Evidence from the stock market," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1173-1185.
    12. Gianna Figà-Talamanca & Marco Patacca, 2020. "Disentangling the relationship between Bitcoin and market attention measures," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 71-91, March.
    13. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.
    14. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
    15. Alessandra Cretarola & Gianna Figà-Talamanca & Cyril Grunspan, 2021. "Blockchain and cryptocurrencies: economic and financial research," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 781-787, December.
    16. Alessandra Cretarola & Gianna Figà-Talamanca, 2021. "Detecting bubbles in Bitcoin price dynamics via market exuberance," Annals of Operations Research, Springer, vol. 299(1), pages 459-479, April.
    17. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    18. Derya Güler, 2023. "The Impact of Investor Sentiment on Bitcoin Returns and Conditional Volatilities during the Era of Covid-19," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 276-289, July.

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    More about this item

    Keywords

    Bitcoin; Market attention; ARMA time series models; GARCH time series models; Box–Jenkins procedure; Forecasting analysis;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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