  This paper introduces a unique and valuable research design aimed at
analyzing Bitcoin price volatility. To achieve this, a range of models from the
Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model
classes are considered and their out-of-sample forecasting performance is
thoroughly examined. The paper provides insights into the rationale behind the
recommendation for a two-stage estimation approach, emphasizing the separate
estimation of coefficients in the mean and variance equations. The results
presented in this paper indicate that Stochastic Volatility models,
particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin
price volatility. Moreover, the study suggests that in certain situations,
persistent simple GARCH models may even outperform Markov-Switching GARCH
models in predicting the variance of Bitcoin log returns. These findings offer
valuable guidance for risk management experts, highlighting the potential
advantages of SARV models in managing and forecasting Bitcoin price volatility.
