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Predicting crypto‐currencies using sparse non‐Gaussian state space models
Author(s) -
HotzBehofsits Christian,
Huber Florian,
Zörner Thomas Otto
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2524
Subject(s) - econometrics , prior probability , computer science , stochastic volatility , random walk , heteroscedasticity , bayesian probability , volatility (finance) , state space representation , benchmark (surveying) , bayesian inference , gaussian , artificial intelligence , economics , statistics , mathematics , algorithm , physics , geodesy , quantum mechanics , geography
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.