Volatility discovery in cryptocurrency markets. Issue 5 (27th September 2021)

• Record Type: Journal Article
• Authors: Dimpfl, Thomas; Elshiaty, Dalia
• Is Part Of: Journal of risk finance. Volume 22:Issue 5(2021)
• Page Start: 313
• Page End: 331
• Publication Date: 2021-09-27
• Subjects: Volatility -- Volatility discovery -- Bitcoin -- Cryptocurrency; C58 -- G14 -- G15; Risk management -- Periodicals; Risk (Insurance) -- Periodicals; Risk assessment -- Periodicals; 658.155
• DOI: 10.1108/JRF-11-2020-0238
• Languages: English
• ISSNs: 1526-5943

Abstract

Abstract : Purpose: Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market. Design/methodology/approach: The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares. Findings: The authors' findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies. Research limitations/implications: The authors' findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors' estimates. Originality/value: To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).