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Detecting overreaction in the Bitcoin market: A quantile autoregression approach

Chevapatrakul, Thanaset; Mascia, Danilo V.


Danilo V. Mascia


We examine the persistence of returns on Bitcoin at different parts on the return distributions through the use of the quantile autoregressive (QAR) models. We find lower quantiles of the daily return distribution and upper quantiles of the weekly return distribution to exhibit positive dependence with past returns. The evidence points to overreaction in the Bitcoin market: investors overreact during days of sharp declines in the Bitcoin price and during weeks of market rallies.


Chevapatrakul, T., & Mascia, D. V. (2019). Detecting overreaction in the Bitcoin market: A quantile autoregression approach. Finance Research Letters, 30, 371-377.

Journal Article Type Article
Acceptance Date Nov 2, 2018
Online Publication Date Nov 6, 2018
Publication Date Sep 30, 2019
Deposit Date Nov 6, 2018
Publicly Available Date Nov 7, 2019
Journal Finance Research Letters
Print ISSN 1544-6123
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 30
Pages 371-377
Keywords JEL classification: C21; C51; C53; G00 Keywords: Bitcoin; cryptocurrencies; quantile regression; overreaction
Public URL
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