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Forecasting realized volatility of bitcoin returns: Tail events and asymmetric loss

Author

Listed:
  • Konstantinos Gkillas

    () (Department of Business Administration, University of Patras-University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Christian Pierdzioch

    () (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B.700822, 22008 Hamburg, Germany)

Abstract

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.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting realized volatility of bitcoin returns: Tail events and asymmetric loss," Working Papers 201905, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201905
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    More about this item

    Keywords

    Bitcoin; Realized volatility; Forecasting; Tail events;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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