Predicting crypto-currencies using sparse non-Gaussian state space models

Hotz-Behofsits, Christian and Huber, Florian ORCID: and Zörner, Thomas (2018) Predicting crypto-currencies using sparse non-Gaussian state space models. Journal of Forecasting, 37 (6). pp. 627-640. ISSN 02776693

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a time-varying parameter VAR with t-distributed measurement errors and stochastic volatility. To control for overparameterization, we rely on the Bayesian literature on shrinkage priors that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data we perform a real-time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we moreover run a simple trading exercise.

Item Type: Article
Additional Information: Funding information: Czech Science Foundation, Grant/Award Number: 17-14263S
Keywords: Bitcoin, density forecasting, stochastic volatility, t-distributed errors
Classification Codes: JEl C11, C32, E51, G12
Divisions: Departments > Marketing > Interactive Marketing and Social Media
Departments > Volkswirtschaft > Außenwirtschaft und Entwicklung
Departments > Volkswirtschaft > Makroökonomie
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 16 Apr 2018 10:26
Last Modified: 02 Sep 2020 15:19
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