Regime-Dependent Good and Bad Volatility of Bitcoin

Kislay Kumar Jha and Dirk G. Baur
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Kislay Kumar Jha: Indian Institute of Technology (IIT) Kanpur, Uttar Pradesh 208016, India
Dirk G. Baur: Business School, University of Western Australia, Perth, WA 6009, Australia

JRFM, 2020, vol. 13, issue 12, 1-16

Abstract: This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

Keywords: semivariance; bitcoin; volatility asymmetry; high-frequency data; HAR; quantile regression (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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