Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239398 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 12 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-16
Publisher: 
MDPI, Basel
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.
Subjects: 
semivariance
bitcoin
volatility asymmetry
high-frequency data
HAR
quantile regression
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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