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.

摘要

本文旨在探索使用马尔可夫状态切换模型确定比特币波动率的异构特征，并测试其预测能力。比特币收益风险测量的预测方法基于条件自回归风险价值模型（CAViaR）方法。我们的研究结果表明，比特币的波动性与加密资产回报的波动性显着相关，波动性的主要决定因素是投机、投资者注意力、市场互操作性以及投机与市场互操作性之间的相互作用。此外，我们提供证据表明投资者的注意力是波动的主要来源。投机和交互项以“U形”形式相关，