Detecting overreaction in the Bitcoin market: A quantile autoregression approach

Chevapatrakul, Thanaset; Mascia, Danilo V.

Abstract

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.

Citation

Chevapatrakul, T., & Mascia, D. V. (2019). Detecting overreaction in the Bitcoin market: A quantile autoregression approach. Finance Research Letters, 30, 371-377. https://doi.org/10.1016/j.frl.2018.11.004

• 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
• Electronic ISSN: 1544-6123
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Pages: 371-377
• DOI: https://doi.org/10.1016/j.frl.2018.11.004
• Keywords: JEL classification: C21; C51; C53; G00 Keywords: Bitcoin; cryptocurrencies; quantile regression; overreaction
• Public URL: https://nottingham-repository.worktribe.com/output/1232446
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1544612318305920
• Contract Date: Nov 6, 2018