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Forecasting bitcoin volatility: Evidence from the options market
Author(s) -
Hoang Lai T.,
Baur Dirk G.
Publication year - 2020
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22144
Subject(s) - volatility (finance) , econometrics , autoregressive model , implied volatility , economics , volatility smile , realized variance , forward volatility , financial economics
This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressive–Moving‐Average or Heterogeneous Auto‐Regressive model forecasts in predicting short‐term BTC volatility (1 day ahead), but superior in predicting long‐term volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and model‐based forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.