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This paper introduces a unique and valuable research design aimed atanalyzing Bitcoin price volatility. To achieve this, a range of models from theMarkov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) modelclasses are considered and their out-of-sample forecasting performance isthoroughly examined. The paper provides insights into the rationale behind therecommendation for a two-stage estimation approach, emphasizing the separateestimation of coefficients in the mean and variance equations. The resultspresented in this paper indicate that Stochastic Volatility models,particularly SARV models, outperform MS-GARCH models in forecasting Bitcoinprice volatility. Moreover, the study suggests that in certain situations,persistent simple GARCH models may even outperform Markov-Switching GARCHmodels in predicting the variance of Bitcoin log returns. These findings offervaluable guidance for risk management experts, highlighting the potentialadvantages of SARV models in managing and forecasting Bitcoin price volatility.

Modelling and Predicting the Conditional Variance of Bitcoin Daily Returns: Comparsion of Markov Switching GARCH and SV Models