Abstract
In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.
Highlights
Volatility is commonly employed for estimating the distribution of returns on a given financial asset to assess financial market instability
The choice of the EGARCH framework is to accommodate the examination of conditional variance, asymmetric effect, and volatility persistence
In θt2 is the one-term anticipation of volatility, and ω refers to the mean level, β is the parameter of persistence and α is the volatility clustering coefficient
Summary
Volatility is commonly employed for estimating the distribution of returns on a given financial asset to assess financial market instability. The simulation and estimation of volatility, play a significant role in the management and pricing of derivatives (Kocenda and Moravcová 2019). While volatility is not explicitly observed, several volatility measures are suggested. The dynamic of this “observed” volatility mechanism was built to model and predict (Ramos-Pérez et al 2019). The pattern of significant market swings in the financial asset is known as volatility clustering (Pongsena et al 2018), resulting in these price shifts being permanent. Financial literature is relevant as the market conditions are considered a key market risk predictor
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