TY  - JOUR
T1  - Long Memory Analysis and Directional Return Forecasting in Cryptocurrency Markets: An Application of the PATSOS Method
AU  - Sak, Ahmet Furkan
AU  - Dalgar, Hüseyin
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.30798/makuiibf.1678534
JF  - Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty
JO  - MAKU IIBFD
PB  - Burdur Mehmet Akif Ersoy University
WT  - DergiPark
SN  - 2149-1658
SP  - 1130
EP  - 1152
VL  - 12
IS  - 3
LA  - en
AB  - This study aims to investigate the volatility dynamics of five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Solana, and Ripple) to determine whether they exhibit long memory properties, reveal their market efficiency, and perform directional return forecasts using PATSOS, a hybrid machine learning method. This study uses daily data on cryptocurrencies from 2014 to 2022. According to the study results, long memory in volatility is found in the return series of Bitcoin, Binance Coin, Solana, and Ripple. Therefore, this finding reveals that the Efficient Markets Hypothesis does not hold for these cryptocurrencies’ return series, except Ethereum. Based on the tests, the most appropriate models for predicting volatility are HYGARCH(1, d, 1) for Bitcoin, IGARCH(1, 1) for Ethereum, FIGARCH(1, d, 0) for Binance Coin, FIGARCH(1, d, 1) and FIAPARCH(1, d, 1) for Solana and Ripple. Predicting future prices using past price movements is possible in these inefficient cryptocurrencies. In this context, we conduct return forecasts using the PATSOS method, which yields successful results in non-linear data. As a result, the PATSOS method produces lower RMSE, MSE, and MAE values and higher accuracy rates than the ANFIS method for all cryptocurrencies in the analysis. These findings highlight the effectiveness of hybrid models in capturing the non-linear structure of cryptocurrency returns.
KW  - PATSOS
KW  - Cryptocurrencies
KW  - Volatility Modelling
KW  - Long Memory
KW  - Market Efficiency
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UR  - https://doi.org/10.30798/makuiibf.1678534
L1  - https://dergipark.org.tr/en/download/article-file/4783772
ER  -