Bayesian change point analysis of Bitcoin returns

Sven Thies and Peter Molnár

Finance Research Letters, 2018, vol. 27, issue C, 223-227

Abstract: This paper studies existence of structural breaks in the average return and volatility of the Bitcoin price. We utilize a Bayesian change point model to detect structural breaks and to partition the time series into segments. We find that structural breaks in average returns and volatility of Bitcoin are very frequent. By merging segments with similar properties into regimes we identify several regimes with positive average returns and one regime with negative average returns. Across regimes, higher volatility is associated with higher average returns, with exception of the most volatile regime, which is the only regime with negative average returns.

Keywords: Bitcoin; Return; Volatility; Regimes; Bayesian change point model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (54)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612318300710
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:27:y:2018:i:c:p:223-227

DOI: 10.1016/j.frl.2018.03.018

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().