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


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Abstract

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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Forecasting
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, INF-A, MAR-B, STRAT-B, VW-D, WH-B
Language English
Title Predicting crypto-currencies using sparse non-Gaussian state space models
Volume 37
Number 6
Year 2018
Page from 627
Page to 640
Reviewed? Y
URL https://onlinelibrary.wiley.com/doi/full/10.1002/for.2524
DOI https://doi.org/10.1002/for.2524
Open Access Y
Open Access Link https://onlinelibrary.wiley.com/doi/full/10.1002/for.2524
JEL C11, C32, E51, G12

Associations

People
Hotz-Behofsits, Christian (Details)
Huber, Florian (Details)
Zörner, Thomas (Details)
Organization
Institute for Macroeconomics IN (Details)
Institute for International Economics and Development IN (Details)
Interactive Marketing and Social Media IN (Details)
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