Economics at your fingertips  

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

Christian Hotz‐Behofsits, Florian Huber and Thomas Otto Zörner
Authors registered in the RePEc Author Service: Thomas O. Zoerner

Journal of Forecasting, 2018, vol. 37, issue 6, 627-640

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 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed

Downloads: (external link)

Related works:
Working Paper: Predicting crypto-currencies using sparse non-Gaussian state space models (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2020-12-22
Handle: RePEc:wly:jforec:v:37:y:2018:i:6:p:627-640