Publication details

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

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Authors

HOTZ-BEHOFSITS Christian HUBER Florian ZÖRNER Thomas

Year of publication 2018
Type Article in Periodical
Magazine / Source JOURNAL OF FORECASTING
MU Faculty or unit

Faculty of Economics and Administration

Citation
Doi http://dx.doi.org/10.1002/for.2524
Keywords Bitcoin; density forecasting; stochastic volatility; t-distributed errors
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Description 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 overparametrization, we rely on the Bayesian literature on shrinkage priors, which 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.
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