Forecasting and comparing the trend of price return using hidden Markov method and Garch-Markov method, case study of Bitcoin, Ethereum, Solana and BNB

Document Type : Original Article

Authors
maryam bagherzadeh sohrabi
Hossein Mombeini
Safieh Mehrinejad
Financial Management Group, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
10.30495/jik.2024.77948.4598
Abstract
This research was carried out for the four cryptocurrencies Bitcoin, Ethereum, BNB and Solana by predicting the dynamic trend of returns using hidden Markov and hidden Garch Markov methods during the one year period of 2022 to 2023. Therefore, in order to predict the efficiency trend more accurately, at first, the efficiency fluctuations were modeled using the famous Garch models, then by using the efficiency fluctuations as input, the hidden Markov model was estimated, and finally, to compare and draw conclusions about The DAP criterion was used to predict the efficiency trend.
Based on the results, the accuracy of the model with the simple hidden Markov method is 76% for the Bitcoin cryptocurrency and 82% with the combined method. For Ethereum Cryptocurrency Hidden Markov is 65% and hybrid method is 91%. Also, for the BNB currency, the simple hidden Markov model is 66%, and the combined method reaches 74%. Regarding the Solana currency, the simple hidden Markov method has reached 55% and the combined method has reached 74%. Therefore, in general, the use of the Garch-Hidden Markov combination method for all four currencies significantly improves the detection of movement trends.
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