Re-examining Bitcoin volatility: a CAViaR-based approach

Zhenghui Li, Hao Dong, Christos Floros, Athanasios Charemis, Pierre Failler

Research output: Contribution to journalArticlepeer-review

Abstract

The article aims to explore the heterogeneous feature in the determination of Bitcoin volatility using a Markov regime-switching model and test its forecasting ability. The forecasting methodology of the risk measurement of Bitcoin’s returns is based on the Conditional Autoregressive Value at Risk models (CAViaR) approach. Our results show that Bitcoin’s volatility is significantly related to the volatility of the crypto-asset’s return and the main determinants of volatility are speculation, investor attention, market interoperability and the interaction between speculation and market interoperability. In addition, we present evidence that investors’ attention is the main source of volatility. Speculation and the interaction term are related in a “U-shaped” form, whereas investor attention and market interoperability show a linear trend on the volatility of Bitcoin.
Original languageEnglish
JournalEmerging Markets Finance and Trade
Early online date24 Jan 2021
DOIs
Publication statusEarly online - 24 Jan 2021

Keywords

  • Bitcoin
  • heterogeneous
  • CAViar
  • Markov regime-switching regression model

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