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

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Abstract

We analyze the role of the US–China trade war in forecasting 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 forecasting variable 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 that 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.

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Notes

  1. In the empirical-finance literature, the term volatility is often used for the standard deviation of returns. In our research, because there is no risk of confusion, we use the term volatility to denote the realized variance of Bitcoin returns (as defined in Eq. (1) in Section 2.1).

  2. To be precise, we should use the notation \(RV_{t+h}\) in Eq. (2) because we forecast realized volatility, where h is an index for the forecast horizon. For ease of notation, we use a somewhat simpler but less precise notation in Eq. (2).

  3. In the context of analyses of bagging and random forecasts the so-called out-of-bag forecasts often play a central role. Out-of-bag forecasts are forecasts of the dependent variable for those observations that are left out from a bootstrap sample. It should be emphasized that we rather study out-of-sample forecasts, that is, predictions of the period \(t+h\) realized volatility based on data available in period of time t. We only use out-of-bag forecasts in Table 3 to construct measures of fit of the estimated models.

  4. Notably, Google Trends provide data for up to 5 years on a weekly basis, whereas daily data can be download for 90 days only. To address this limitation and overcome the individual scaling that might prevent us from stringing several 90-day data together (Baur and Dimpfl 2016), we follow the procedure described below. We extracted five 90-day periods at once, and fortunately the search data from these periods will have the same maximum scale of 0–100. Given our sample period (1st July 2017 to 30th June 2019) includes at least 8 quarters, we repeat this process to connect the series together while using the overlapping quarters for chaining the series together (i.e., for 2018, we use each quarter of 2018 and the last quarter of 2017, etc.). Each daily series is based on a monthly moving window and rescaled with respect to monthly series over the whole period. Each term has been searched five times with random alphanumerical codes added to them and averaged over the whole period (we have run 20 repetitions for each term, and our main results are not be biased by this issue). Data are then converted to a 0–100 scale as they are typically given by Google Trends.

  5. All estimation results documented in this study were computed with the use of the R programming environment (R Core Team 2019). In particular, the following R packages were used: “RandomForest” for the estimation of random forests (see Liaw and Wiener 2002), the R package “forecast” (see Hyndman 2017; Hyndman and Khandakar 2008) for the computation of the Diebold–Mariano test’s p values (based on the well-known modified Diebold–Mariano test proposed by Harvey et al. 1997).

  6. Results (available from the authors upon request) turned out to be qualitatively similar when we increased the number of trees to 1000.

  7. The news-basedindex is constructed by counting the frequency of joint occurrences of trade policy (tariff, import duty, import barrier, and anti-dumping) and uncertainty (uncertainty, risk, or potential) terms across major newspapers (Boston Globe, Chicago Tribune, Guardian, Los Angeles Times, New York Times, Wall Street Journal, and Washington Post). The data is downloadable from the website of Professor Matteo Iacoviello at: https://www2.bc.edu/matteo-iacoviello/tpu.htm.

  8. The models that could select the alternative metrics of trade uncertainty also feature all other covariates of realized volatility except for the Google Trends-based metric of trade-related uncertainty. Because the alternative metrics do not add much forecasting value in such a model, we decided not to consider a model that includes simultaneously the Google-Trends-based metric and the alternative metrics in the list of predictors.

  9. Hastie et al. (2009) discuss the pros and cons of alternative machine-learning technique (see Table 10.1, Page 351 in their book) and state “Of all the well-known learning methods, decision trees come closest to meeting the requirements for serving as an off-the-shelf procedure for data mining.” (Page 352).

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Funding

Funding was provided by German Science Foundation (Grant No. 275693836).

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Authors and Affiliations

  1. USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon

    Elie Bouri

  2. Department of Business Administration, University of Patras, University Campus, Rio, P.O. Box 1391, 26500, Patras, Greece

    Konstantinos Gkillas

  3. Department of Economics, University of Pretoria, Pretoria, 0002, South Africa

    Rangan Gupta

  4. Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008, Hamburg, Germany

    Christian Pierdzioch

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  1. Elie Bouri
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Correspondence to Konstantinos Gkillas.

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Bouri, E., Gkillas, K., Gupta, R. et al. Forecasting Realized Volatility of Bitcoin: The Role of the Trade War. Comput Econ 57, 29–53 (2021). https://doi.org/10.1007/s10614-020-10022-4

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