Affiliation:
1. Zhongnan University of Economics and Law, Wuhan, China
2. Shanghai Business School, Shanghai, China
Abstract
After Bitcoin futures were introduced by the Chicago Mercantile Exchange in December 2017, their trading volume has stayed in an uptrend due to speculation, though the scale is still small compared to other traditional futures. As increasing trading indicates more attention and the presence of institutional traders, there exists a need for reliable return and variance forecasts of Bitcoin futures contracts. Therefore, this paper first applies LASSO to pick out best-fitting predictors by shrinking the dimension of a universe of potential determinants sourced from intraday Bitcoin spot trades and daily futures variables. Then, a second round of predictor selection is conducted via Bayesian model averaging so that the modeling uncertainty can be mitigated. We find that factors standing out from this two-step procedure possess a strong predictive power for Bitcoin futures return and volatility in different time horizons. It is further demonstrated that the investment and hedging strategies established based on our forecasts perform well in out-of-sample validations.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Subject
General Social Sciences,General Arts and Humanities
Cited by
3 articles.
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