Analysis of precious metal price movements using long memory model and fuzzy time series Markov Chain

Precious metals occur naturally and have a high resistance to corrosion or oxidation. These natural resources are used as investment instruments to protect wealth values, such as gold, silver, and palladium. Price movements need to be understood when investing, and it is achieved through a time series model that predicts future prices. Also, autoregressive fractional integrated moving average (ARFIMA) is used to model price movements with long memory effects, while fuzzy time series Markov chain (FTSMC) is employed for performing numerical approach. It was observed that gold price movement has a long memory effect; therefore, it is eligible to be formed into the ARFIMA model. However, the silver and palladium prices do not contain a long memory effect, which means their movements are only formed through the FTSMC numerical model. The ARFIMA modeling results show that the gold price long memory model has the best accuracy with the smallest error value and also demonstrates excellent goodness of fit. Furthermore, the gold price long memory model movement has long-term stability compared to other precious metals. This provides an investment advantage because it is a stable asset, easy to liquidate in cash, free of interest, has an emergency fund role, and protects wealth’s value.