A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, cryptocurrency was designed as a viable digital currency implementation, subsequently, numerous derivatives were produced range sectors, including nonmonetary activities, financial transactions, even capital management. The high volatility exchange rates is one main features cryptocurrencies. article presents an interesting way to estimate probability clusters. this regard, paper explores exponential hybrid methodologies GARCH (or EGARCH) through its portrayal asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling needed fit variable characteristics such because dynamic, nonlinear association structure between variables. For forecasting, BP contained most popular methods neural network training. backpropagation method employed train two determine which performs best terms predicting. This architecture consists hidden layer input with N neurons. Recent theoretical work on crypto-asset return behavior risk management supported by research. comparison other traditional asset classes, these results give appropriate data behavior, allowing them adopt suitable investment decision. study conclusions are based dynamic cryptocurrencies FOREX Currency’s mass asset. Thus, result illustrates how well clusters show impact currencies. research covers sample period August 2017 2020, became around that period. following methodology implemented simulated using Eviews SPSS software. performance evaluation compared currencies for better comparative respectively.