Abstract
AbstractIn 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 witht‐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.
Highlights
In the present paper we develop a non-Gaussian state space model to predict the price of three crypto-currencies
To control for potential outliers we propose a timevarying parameter VAR model (Cogley and Sargent, 2005; Primiceri, 2005) with heavy tailed innovations1 as well as a stochastic volatility specification of the error variances
In a forecasting comparison, we find that time-varying parameter VARs with some form of shrinkage perform well, beating univariate benchmarks like the AR(1) model with stochastic volatil
Summary
In the present paper we develop a non-Gaussian state space model to predict the price of three crypto-currencies. The conditional mean of the process is changing This implies that, within a standard regression framework, the relationship between an asset price and a set of exogenous covariates is time-varying. The goal of this paper is to systematically assess how different empirically relevant forecasting models perform when used to predict daily changes in the price of Bitcoin, Ethereum and Litecoin. During days which are characterized by large price changes, controlling for heteroscedasticity in combination with a flexible error variance covariance structure pays off in terms of predictive accuracy. These findings are generally corroborated by considering probability integral transforms, showing that more flexible models lead to better calibrated predictive distributions.
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