Forecasting Realized Volatility of Bitcoin Returns: Tail Events and Asymmetric Loss
Konstantinos Gkillas (Gillas) (),
Rangan Gupta and
No 201905, Working Papers from University of Pretoria, Department of Economics
We use intra-day data to construct measures of the realized volatility of bitcoin returns. We then use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether indices which capture the tail behaviour (heavy-tailedness and asymmetry) of the daily returns distribution help to forecast subsequent realized volatility. We find that mainly forecasters who suffer a higher loss in case of an underprediction of realized volatility than in case of an overprediction of the same absolute size benefit from using the tail indices as predictors of realized volatility at intermediate forecast horizons. This result is robust to controlling for realized skewness and realized kurtosis, and it also applies to “bad” and “good” realized volatility.
Keywords: Bitcoin; Realized volatility; Forecasting; Tail events (search for similar items in EconPapers)
JEL-codes: C22 F31 F37 (search for similar items in EconPapers)
Pages: 30 pages
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Journal Article: Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201905
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