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Analysis of Bitcoin prices using market and sentiment variables

Analysis of Bitcoin prices using market and sentiment variables
Analysis of Bitcoin prices using market and sentiment variables
This paper proposes an empirical model for analyzing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010-2017 and 2010-2019. Price discovery is achieved through the Gonzalo-Granger permanent-transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin, and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalized by individuals’ search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018-2019 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.
Bitcoin price, cointegration, cryptocurrency, factor models, permanent-transitory decomposition
Kapar, Burcu
92416c2b-880f-41a0-bf73-15042d9bf993
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Kapar, Burcu
92416c2b-880f-41a0-bf73-15042d9bf993
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e

Kapar, Burcu and Olmo, Jose (2020) Analysis of Bitcoin prices using market and sentiment variables. The World Economy. (doi:10.1111/twec.13020).

Record type: Article

Abstract

This paper proposes an empirical model for analyzing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010-2017 and 2010-2019. Price discovery is achieved through the Gonzalo-Granger permanent-transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin, and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalized by individuals’ search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018-2019 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.

Text
BitcoinPaper_WE_revised_final - Accepted Manuscript
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More information

Accepted/In Press date: 28 July 2020
e-pub ahead of print date: 11 August 2020
Keywords: Bitcoin price, cointegration, cryptocurrency, factor models, permanent-transitory decomposition

Identifiers

Local EPrints ID: 443978
URI: http://eprints.soton.ac.uk/id/eprint/443978
PURE UUID: 6a370103-0d1b-46ba-9fa2-a80abaacd1bd
ORCID for Jose Olmo: ORCID iD orcid.org/0000-0002-0437-7812

Catalogue record

Date deposited: 18 Sep 2020 16:31
Last modified: 18 Feb 2021 17:21

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