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

Christian Hotz-Behofsits, Florian Huber and Thomas O. Z\"orner
Authors registered in the RePEc Author Service: Thomas O. Zoerner

Papers from arXiv.org

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.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Date: 2018-01, Revised 2018-02
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http://arxiv.org/pdf/1801.06373 Latest version (application/pdf)

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Journal Article: Predicting crypto‐currencies using sparse non‐Gaussian state space models (2018) Downloads
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