In this article, novel mixture of conditional volatility models of Generalized Autoregressive Conditional Heteroscedasticity (GARCH); Exponential GARCH (EGARCH); Glosten, Jagannathan, and Runkle GARCH (GJR-GARCH); and dependent variable-GARCH (TGARCH) were thoroughly expounded in a Bayesian paradigm. Expectation-Maximization (EM) algorithm was employed as the parameter estimation technique to work-out posterior distributions of the involved hyper-parameters after setting-up their corresponding prior distributions. Mode was considered as the stable location parameter instead of the mean, because it could robustly adapt to symmetric, skewedness, heteroscedasticity and multimodality effects simulteanously needed to redefine switching conditional variance processes conceived as mixture components based on shifting number of modes in the marginal density of Skewed Generalized Error Distribution (SGED) set as the prior random noise. 
In application to ten (10) most used cryptocurrency coins and tokens via their daily open, high, low, close and volume converted and transacted in USD from the same date of inception. Binance Coin (BNB) via its daily lower units transacted in USD (that is, low-BNB), yielded the most reduced Deviance Information Criteria (DIC) of 3651.1935. The low-BNB process yielded a two-regime process of TGARCH, that is, Mixture dependent variable-GARCH (TGARCH (2: 2, 2)) with stable probabilities of 33% and 66% respectively. The first regime was attributed with low unconditional volatility of 16.96664, while the second regime was traded with high unconditional volatility of 585.6190. In summary, Binance Coin (BNB) was a mixture of tranquil market conditions and stormy market conditions. Implicatively, this implies that the first regime of the low-BNB was characterized with strong fluctuating reaction to past negative daily returns of low-BNB converted to USD, while the second regime was attributed with weak fluctuating reaction. Additionally, the first regime was attributed with low repetitive volatility process, while the second regime was characterized with high persistence fluctuating process. For financial and economic decision-making, crypocurrency users and financial bodies should look-out for financial and economic sabotage agents, like war, exchange rate instability, political crises, inflation, browsing network fluctuation etc. that arose, declined or fluctuated doing the ten (10) years to study of the coins and tokens to ascertain which of this/these agent(s) contributed to the volatility process. 
Mixture models from a Bayesian perspective were of interest because; some of the classical (traditional) models cannot accommodate and absolve regime-switching traits, and as well contain prior information known about cryptocurrency coins and tokens. In light of model performance, DIC values were compared on the basis of most performed to less perform via lower to higher values of DICs.  

This paper aims at providing in-depth refinement to switching time-variant autoregressive processes via the mode as a stable location parameter in adopted noisy Fisher’s z-distribution that was impelled in a Bayesian setting. Explicitly, a four-parameter Fisher’s z-distribution of Bayesian Mixture Autoregressive (FZBMAR) process was proposed to congruous  k-mixture components of Fisher’s z-switching mixture autoregressive processes that was based on shifting number of modes in the marginal density of any switching time-variant series of interest. The proposed FZBMAR process was not only used to seize what is term “most likely mode value” of the present conditional modal distribution given the immediate past but was also used to capture the conditional modal distribution of the observations given the immediate past that can either be perceived as an asymmetric or symmetric distributed varieties. The proposed FZBMAR process was compared with the existing Student-t Mixture Autoregressive (StMAR) and Gaussian Mixture Autoregressive (GMAR) processes with the demonstration of monthly average share prices (stock prices) of sixteen (16) swaying European economies. Based on the findings, the FZBMAR process outperformed the existing StMAR and GMAR processes in explaining the sixteen (16) swaying European economies share prices via a minimum Pareto-Smoothed Important Sampling Leave-One-Out Cross-Validation (PSIS-LOO) error process performance in comparison with AIC, HQIC by the latters. The same singly truncated student-t prior distribution was adopted for the noisy adoption of Fisher’s z hyper-parameters and the embedded autoregressive coefficients in the proposed FZBMAR process; such that their resulting posterior distributions gave the same singly truncated student-t distribution (conjugate) with an embedded Gamma variate.