Informace o publikaci

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

Logo poskytovatele
Autoři

HOTZ-BEHOFSITS Christian HUBER Florian ZÖRNER Thomas

Rok publikování 2018
Druh Článek v odborném periodiku
Časopis / Zdroj JOURNAL OF FORECASTING
Fakulta / Pracoviště MU

Ekonomicko-správní fakulta

Citace
Doi http://dx.doi.org/10.1002/for.2524
Klíčová slova Bitcoin; density forecasting; stochastic volatility; t-distributed errors
Přiložené soubory
Popis 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info