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

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.

Drivers of the next-minute Bitcoin price using sparse regressions

“…Based on the findings of Gonz alez et al (2021), Hassan et al (2021) and Okorie and Lin (2020), we include both gold and crude oil commodities as potential representatives of commodity markets in our analysis. Chinthapalli (2021) used daily opening and closing prices for four cryptocurrencies (XRP, BTC, ETH and Tether) and seven foreign exchange (FX) rates (EUR, AUD, MXN, GBP, BRL, SAR and JPY) for computing volatility clusters. Five-day volatility forecasts support the idea that FX markets have significant volatility patterns compared with cryptos such as BTC.…”

“…Chinthapalli (2021) used daily opening and closing prices for four cryptocurrencies (XRP, BTC, ETH and Tether) and seven foreign exchange (FX) rates (EUR, AUD, MXN, GBP, BRL, SAR and JPY) for computing volatility clusters. Five-day volatility forecasts support the idea that FX markets have significant volatility patterns compared with cryptos such as BTC.…”

Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience

“…Therefore, many researchers have targeted their studies on the FOREX market by using methods ranging from statistical methods to deep learning. However, because volatility behavior in the foreign exchange market has important implications for modeling and calculating risk in these markets, future researchers will need to use innovative and improved approaches to analyze the impact and forecasting of this volatility behavior (Kamal et al, 2012;Islam et al, 2020;Chinthapalli, 2021). Hence, the Quantum Monte Carlo method emerges as a sophisticated quantum approach, offering a dependable solution to capture the mentioned volatility.…”