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First published in Journal of Risk and Financial Management, v16I11 (Nov 2023) published by MDPI. DOI: https://doi.org/10.3390/jrfm16110474

Document Type

Article

Publication Version

Published Version

Keywords

bitcoin, bitcoin futures, causality, cointegration, investor attention, sentiment

Journal/Book/Conference Title

Journal of Risk and Financial Management

Volume

16

Issue

11

Abstract

This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.

Department

Department of Finance

Original Publication Date

11-1-2023

Object Description

1 PDF File

DOI of published version

10.3390/jrfm16110474

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Copyright

© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Language

en

File Format

application/pdf

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