Skip to Main content Skip to Navigation
New interface
Journal articles

Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks

Abstract : In this paper we study the daily volatility of four cryptocurrencies (BitCoin, Dash, LiteCoin, and Ripple) from June 2014 to November 2018. We first show that the cryptocurrency returns are strongly characterized by the presence of jumps as well as structural breaks (except Dash). Then, we estimate four GARCH-type models that capture short memory (GARCH), asymmetry (APARCH), strong persistence (IGARCH), and long memory (FIGARCH) from (i) original returns, (ii) jump-filtered returns, and (iii) jump-filtered returns with structural breaks. Results indicate the importance to take into account the jumps and structural breaks in modelling volatility of the cryptocurrencies. It appears that the cryptocurrency returns are well modelled by infinite persistence (BitCoin, Dash, and LiteCoin) or long memory (Ripple) with a Student-t distribution.
Complete list of metadata

https://hal-nantes-universite.archives-ouvertes.fr/hal-03794543
Contributor : Dépôt NantesU DEC Connect in order to contact the contributor
Submitted on : Monday, October 3, 2022 - 12:37:14 PM
Last modification on : Saturday, November 26, 2022 - 5:20:07 PM

Identifiers

  • HAL Id : hal-03794543, version 1

Citation

Amélie Charles, Olivier Darné. Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks. Economics Bulletin, 2019, 39 (2), pp.954-968. ⟨hal-03794543⟩

Share

Metrics

Record views

5