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Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet?

King, Timothy, Koutmos, Dimitrios, Zoppounidis, Constantin (2021) Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet? Journal of Financial Research, . ISSN 0270-2592. E-ISSN 1475-6803. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:88339)

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Abstract

Are cryptocurrencies useful minimum-variance hedging instruments? This paper develops a twostep analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation (DCC) approach of Engle (2016). Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty.

Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID-19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two-step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.

Item Type: Article
Uncontrolled keywords: Bitcoin; cryptocurrencies; fractional regression; dynamic conditional beta; minimum-variance optimization
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance > Centre for Quantitative Finance
Depositing User: Timothy King
Date Deposited: 24 May 2021 12:43 UTC
Last Modified: 15 Jun 2021 16:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/88339 (The current URI for this page, for reference purposes)
King, Timothy: https://orcid.org/0000-0002-7326-2162
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