Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach^{ †}
Abstract
:1. Introduction
Summary & Results
 Q1. Do the same explanatory variables affect both the BTC and ETH cryptocurrencies?
 Q2. What is the predictive power of the NHPG model on the BTC and ETH price series?
 Q3. Do the same explanatory variables affect the BTC price series both on the long and short run?
2. Modeling Cryptocurrency Price Series
The NonHomogeneous PólyaGamma Hidden Markov Model
 Given the model’s parameters, the hidden states are simulated using the Scaled Forward Backward of algorithm of [42].
 The posterior mean regression parameters are simulated using the standard conjugate analysis, via a Gibbs sampler method.
 The logistic regression coefficients are simulated using the PólyaGamma data augmentation scheme [43], as a better and more accurate sampling methodology compared to the existing schemes.
 The set of covariates that affect the model linearly and nonlinearly (via the transition probabilities) are updated using a double reversible jump algorithm.
 Predictions are made conditional on the simulated unknown quantities.
Algorithm 1 MCMC Sampling Scheme for Inference on Model Specification and Parameters 

3. The DataExperiment
Explanatory Variables  
Description  Symbol  Retrieved from 
US dollars to Euros exchange rate  USD/EUR  investing.com 
US dollars to GBP exchange rate  USD/GBP  investing.com 
US dollars to Japanese Yen exchange rate  USD/JPY  investing.com 
US dollars to Chinese Yuan exchange rate  USD/CNY  investing.com 
Standard & Poor’s 500 index  SP500  finance.yahoo.com 
Dow Jones Industrial Average  DOW  finance.yahoo.com 
NASDAQ Composite index  NASDAQ  finance.yahoo.com 
Crude Oil Futures price  CO  finance.yahoo.com 
Price of Gold  GOLD  finance.yahoo.com 
CBOE Volatility index  VIX  finance.yahoo.com 
Equity market related Economic Uncertainty index  EUI  fred.stlouisfed.org 
Hash Rate  HR  quandl.com/etherscan.io 
Average Block Size  AVS  quandl.com/etherscan.io 
Mean Posterior Variance  

BTC  BTC  ETH  
Sample period  4/2013–6/2019  6/2016–6/2019  6/2016–6/2019 
${\sigma}_{1}^{2}$  $0.001\times {10}^{6}$  $2.63\times {10}^{6}$  $7.05\times {10}^{3}$ 
${\sigma}_{2}^{2}$  $1.49\times {10}^{6}$  $0.03\times {10}^{6}$  $0.002\times {10}^{3}$ 
Mean Square Forecast Error  
MSFE  $4.528\times {10}^{6}$  $7.986\times {10}^{6}$  $2.756\times {10}^{4}$ 
4. Results
Posterior probabilities of inclusion  

Predictors  BTC  BTC  ETH 
Sample period  4/2013  6/2019  6/20166/2019  6/20166/2019 
USD/EUR  0.65 0.39  0.72 0.50  0.58 0.50 
USD/GBP  1.00 0.36  0.62 0.48  0.47 0.48 
USD/JPY  1.00 017  0.59 0.27  0.36 0.22 
USD/CNY  1.00 0.39  0.81 0.44  0.67 0.36 
CO  1.00 0.07  0.97 0.24  1.00 0.15 
VIX  1.00 0.07  0.70 0.16  1.00 0.12 
SP500  0.46 0.13  0.42 0.20  0.47 0.18 
DOW  0.95 0.08  0.48 0.16  0.45 0.11 
NASDAQ  1.00 0.13  0.78 0.23  0.77 0.11 
GOLD  1.00 0.12  0.97 0.32  1.00 0.13 
EUI  0.05 0.01  0.07 0.01  0.00 0.00 
HR  0.55 0.02  0.32 0.22  1.00 0.01 
AVS  1.00 0.02  1.00 0.01  0.57 0.06 
5. Conclusions
Funding
Conflicts of Interest
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Koki, C.; Leonardos, S.; Piliouras, G. Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach. Proceedings 2019, 28, 5. https://doi.org/10.3390/proceedings2019028005
Koki C, Leonardos S, Piliouras G. Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach. Proceedings. 2019; 28(1):5. https://doi.org/10.3390/proceedings2019028005
Chicago/Turabian StyleKoki, Constandina, Stefanos Leonardos, and Georgios Piliouras. 2019. "Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach" Proceedings 28, no. 1: 5. https://doi.org/10.3390/proceedings2019028005