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Forecasting Bitcoin Volatility with Markov-Switching GARCH and Stochastic Models

Article arXiv papers · Author: Dennis Koch et al.
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Summary

The paper compares conditional-variance forecasts for Bitcoin daily returns using Markov-switching GARCH and stochastic volatility models, including stochastic autoregressive volatility (SARV). Its design evaluates out-of-sample forecasting performance and recommends estimating mean and variance equation coefficients in separate stages. This separation is presented as a rationale for the estimation procedure.

The reported results favor stochastic volatility models, especially SARV, over the Markov-switching GARCH alternatives. They also indicate that a persistent standard GARCH model can sometimes forecast return variance better than a Markov-switching GARCH model. These comparisons offer evidence for choosing among volatility models in Bitcoin risk forecasting, but the supplied summary gives no sample dates, forecast horizons, scoring measures, or detailed conditions behind the rankings. The findings therefore do not establish that one model will dominate in every market regime or forecasting setup.

Key ideas

  • The study compares out-of-sample Bitcoin volatility forecasts from Markov-switching GARCH and stochastic volatility models.
  • It considers stochastic autoregressive volatility models as part of the stochastic volatility class.
  • The paper recommends a two-stage estimation procedure that separates mean and variance coefficients.
  • Stochastic volatility models, particularly SARV, perform better than MS-GARCH in the reported comparison.
  • Persistent simple GARCH can sometimes outperform MS-GARCH for Bitcoin return variance forecasts.

Tags

CryptoVolatilityStatisticsRisk management
Full text
# 2401.03393


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.