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Forecasting Realized Volatility of Bitcoin: The Role of the Trade War

Elie Bouri (), Konstantinos Gkillas (), Rangan Gupta () and Christian Pierdzioch
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Konstantinos Gkillas: Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece

No 202003, Working Papers from University of Pretoria, Department of Economics

Abstract: We analyze the role of the US-China trade war in predicting, both in- and out-of-sample, daily realized volatility of Bitcoin returns. We study intraday data spanning from 1st July 2017 to 30th June 2019. We use the heterogeneous autoregressive realized volatility model (HAR-RV) as the benchmark model to capture stylized facts such as heterogeneity and long-memory. We then extend the HAR-RV model to include a metric of US-China trade tensions. This is our primary predictor of interest, and it is based on Google Trends. We also control for jumps, realized skewness, and realized kurtosis. For our empirical analysis, we use a machine-learning technique which is known as random forests. Our findings reveal that US-China trade uncertainty does improve forecast accuracy for various configurations of random forests and forecast horizons.

Keywords: Bitcoin; Realized volatility; Trade war; Random forests (search for similar items in EconPapers)
JEL-codes: G17 Q02 Q47 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-for and nep-pay
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