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

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 tre...

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Bibliographic Details
Published inJournal of forecasting Vol. 37; no. 6; pp. 627 - 640
Main Authors Hotz‐Behofsits, Christian, Huber, Florian, Zörner, Thomas Otto
Format Journal Article
LanguageEnglish
Published Chichester Wiley Periodicals Inc 01.09.2018
Subjects
Bayesian analysis
Bitcoin
density forecasting
Digital currencies
Econometrics
Economic models
Forecasting
Measurement
Normality
Specification
stochastic volatility
Time series
Trading
t‐distributed errors
Volatility
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Summary: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.
ISSN:0277-6693
1099-131X
DOI:10.1002/for.2524