Predicting crypto‐currencies using sparse non‐Gaussian state space models
From MaRDI portal
Publication:4687677
DOI10.1002/FOR.2524zbMath1397.62540arXiv1801.06373OpenAlexW2962988653WikidataQ125938501 ScholiaQ125938501MaRDI QIDQ4687677
Thomas O. Zörner, Florian Huber, Christian Hotz-Behofsits
Publication date: 12 October 2018
Published in: Journal of Forecasting (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1801.06373
Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical ranking and selection procedures (62F07)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
- Bayesian stochastic search for VAR model restrictions
- Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of bitcoin
- Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
- Estimation of stochastic volatility models with diagnostics
- Price clustering in bitcoin
- Stochastic model specification search for Gaussian and partial non-Gaussian state space models
- Inference with normal-gamma prior distributions in regression problems
- Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
- On scale mixtures of normal distributions
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Hierarchical Shrinkage in Time‐Varying Parameter Models
- Time Varying Structural Vector Autoregressions and Monetary Policy
- Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling.
This page was built for publication: Predicting crypto‐currencies using sparse non‐Gaussian state space models
