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Forecasting Volatility in Cryptocurrency Markets

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  • Mawuli Segnon
  • Stelios Bekiros

Abstract

In this paper, we revisit the stylized facts of cryptocurrency markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two loss functions and the model confidence set (MSC) test to evaluate the predictive ability of the models and the likelihood ratio test to assess their adequacy. Our results confirm that cryptocurrency markets are characterized by regime shifting, long memory and multifractality. We find that the Markov switching multifractal (MSM) and FIGARCH models outperform other GARCH-type models in forecasting bitcoin returns volatility. Furthermore, combined forecasts improve upon forecasts from individual models.

Suggested Citation

  • Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:7919
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    References listed on IDEAS

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    1. Ramzi Nekhili & Jahangir Sultan, 2020. "Jump Driven Risk Model Performance in Cryptocurrency Market," IJFS, MDPI, vol. 8(2), pages 1-18, April.

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    More about this item

    Keywords

    Bitcoin; Multifractal processes; GARCH processes; Model confidence set; Likelihood ratio test;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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