Re-examining Bitcoin Volatility: A CAViaR-based Approach

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.

Automated summary

The article explores the heterogeneous feature in determining Bitcoin volatility using a Markov regime-switching model and tests its forecasting ability based on the CAViaR approach. Results indicate significant relationship between Bitcoin volatility and crypto-asset returns, with speculation, investor attention, market interoperability, and their interactions being the main determinants of volatility. Additionally, evidence suggests that investor attention is the main source of volatility, while speculation and interaction show a U-shaped relationship and investor attention and market interoperability exhibit a linear trend on Bitcoin volatility.