Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models

@article{Koki2022ExploringTP,
  title={Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models},
  author={Constandina Koki and Stefanos Leonardos and Georgios Piliouras},
  journal={Research in International Business and Finance},
  year={2022}
}

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References

SHOWING 1-10 OF 83 REFERENCES
Modelling Volatility of Cryptocurrencies Using Markov-Switching GARCH Models
Predicting crypto-currencies using sparse non-Gaussian state space models
TLDR
A time-varying parameter VAR with t-distributed measurement errors and stochastic volatility is developed that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner to control for overparameterization.
Forecasting cryptocurrencies under model and parameter instability
An empirical investigation of volatility dynamics in the cryptocurrency market
By employing an asymmetric Diagonal BEKK model, this paper examines volatility dynamics of five major cryptocurrencies, namely Bitcoin, Ether, Ripple, Litecoin, and Stellar Lumen. It is shown that
Asymmetric mean reversion of Bitcoin price returns
Abstract Non-linearity is characterised by an asymmetric mean-reverting property, which has been found to be inherent in the short-term return dynamics of stocks. In this paper, we explore as to
Today I Got a Million, Tomorrow, I Don't Know: On the Predictability of Cryptocurrencies by Means of Google Search Volume
TLDR
It is found that returns are not predictable while volatility is predictable to some extent while an increasing accuracy of the forecast when the sampling frequency is lowered.
Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting
This paper brings new evidence of predicting the U.S. recessions through Markovian models. The Markovian models, including the Hidden Markov and Markov models, incorporate the temporal
Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation
TLDR
This paper proposes a new latent variable scheme that utilizes the Polya-Gamma class of distributions that allows for model uncertainty regarding the predictors that affect the series both linearly and non-linearly in the transition matrix.
Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks
Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the
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