Suchergebnisse

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.