Regime changes in Bitcoin GARCH volatility dynamics
By David Ardia, Keven Bluteau, Maxime Rüede. Finance Research Letters, 2019
254 citations recorded by the source corpus.
Abstract We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log–returns using Markov–switching GARCH (MSGARCH) models. We also compare MSGARCH to traditional single–regime GARCH specifications in predicting one–day ahead Value–at–Risk (VaR). The Bayesian approach is used to estimate the model parameters and to compute the VaR forecasts. We find strong evidence of regime changes in the GARCH process and show that MSGARCH models outperform single–regime specifications when predicting the VaR.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/838183f6385ca51a0e3d8bd85658af49546532cc
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In Economics and Econometrics
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Citations in the corpus
- Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis (2019, unknown): “The most publicised Blockchain advancement has taken place in the development of the market for cryptocurrencies appearing to have manifested itself in substantial market valuations and product liquidity, with cryptocurrencies being mainly viewed as assets rather than currencies (Baek and Elbeck [20”
- Cryptocurrencies as a financial asset: A systematic analysis (2019, background): “The existence of structural breaks in Bitcoin volatilities has been also reported by Ardia et al. [2018]. Authors found that a two-regime MSGARCH model demonstrated the best insample forecasting performance with an inverted leverage effect in low- and high- volatility regimes, while for one-day ahea”
- Cryptocurrency trading: a comprehensive survey (2020, methodology): “[8] used the Markov Switching GARCH (MSGARCH) model to test the existence of institutional changes in the GARCH volatility dynamics of Bitcoin’s logarithmic returns.”