Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks

Dehua Shen · Andrew Urquhart

92 citations0/0 files storedEuropean Financial Management

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

Abstract

Abstract This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.

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References

2003Modeling and Forecasting Realized Volatility 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 2008A Simple Approximate Long-Memory Model of Realized Volatility 2006Approximately normal tests for equal predictive accuracy in nested models 2001The distribution of realized stock return volatility 2004Power and Bipower Variation with Stochastic Volatility and Jumps 2003The Impact of Jumps in Volatility and Returns 2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

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