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Forecast Bitcoin Volatility with Least Squares Model Averaging

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  • Tian Xie

    (College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.

Suggested Citation

  • Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:3:p:40-:d:267321
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    References listed on IDEAS

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