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Decisions in Economics and Finance

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22.06.2019 | S.I.: Mathematical & Statistical Methods for Actuarial Sciences & Finance

Does market attention affect Bitcoin returns and volatility?

verfasst von: Gianna Figá-Talamanca, Marco Patacca

Erschienen in: Decisions in Economics and Finance | Ausgabe 1/2019

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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.

Vorheriger Artikel A stochastic model to evaluate pricing distortions in indemnity insurance methods for MTPL insurance

Nächster Artikel A market-consistent framework for the fair evaluation of insurance contracts under Solvency II

The term pseudonymous, rather than anonymous, is meant to stress the fact that sender and receiver addresses as well as currency amounts of all transactions recorded in the blockchain are completely disclosed, though the physical identities of the users are unknown.

Altcoin is the term commonly used for cryptocurrencies other than Bitcoin.

The reason for this change is beyond the scope of this paper though it gives a motivation for the selection of the two subsamples.

It is worth noticing that Google Trend provides time series of the SVI with maximum length related to the observation frequency. In order to have a long series of daily SVI values we had to merge contiguous series by uploading data for overlapping periods and building a proper algorithm to scale the observations accordingly.

If not scaled, the estimated coefficients in Eqs. (1), (2) and (3) would be negligible, though statistically significant.

We applied the function adftest.m provided in the Econometrics Toolbox of \(\hbox {Matlab}^{\textregistered }\). We use ‘ARD’ specification (autoregressive model with drift variant) for the volume in all different series while we use ‘AR’ specification (autoregressive model variant) for the SVI Google index in the whole series and 1st subsample, and ‘TS’ specification (trend-stationary model variant) for the SVI Google index in the 2nd subsample.

\(X_2\) is not used for the GARCH-X specification because it can take negative values.

The subsamples in Urquhart (2018) largely overlap the two periods in our analysis so we discuss them as if they were exactly the same.

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Titel: Does market attention affect Bitcoin returns and volatility?
verfasst von: Gianna Figá-Talamanca
Marco Patacca
Publikationsdatum: 22.06.2019
Verlag: Springer International Publishing
Erschienen in: Decisions in Economics and Finance / Ausgabe 1/2019
Print ISSN: 1593-8883
Elektronische ISSN: 1129-6569
DOI: https://doi.org/10.1007/s10203-019-00258-7

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