Abstract
We start by presenting a short description of the concept of cryptocurrency and the history behind it. Recently-developed literature that attempt to predict volatilities of cryptocurrency valuations through creation of hybrid artificial neural network models are then discussed. For the major part of the paper, we delve into details of multiple hybrid artificial neural networks that were thoroughly implemented to predict cryptocurrency volatilities. Results are reported within the form of a survey. Finally, we compare different methods and discuss their results follow at the end.
Highlights
The last decade has witnessed an enormous hike in usage and popularity of cryptocurrencies
Throughout this paper, we explain the various methodologies and results used in artificial neural network models predicting cryptocurrency volatility
Establishing on Bayesian Probabilistic theory and principle, Jang et al (2017) attempts to explain the volatility by proposing an artificial neural network with a multi-layer perceptron maximizing the value of posterior
Summary
The last decade has witnessed an enormous hike in usage and popularity of cryptocurrencies. The initial idea of a virtual currency was given by American computer scientist and cryptographer David Chaum in early 80’s. The primary enormous price hikes of Bitcoin, as a result of a sudden increase in demand with supply not increasing equivalently, took place in 2017. All cryptocurrencies, especially Bitcoin, have been experiencing sudden hikes repetitively Such price hikes make Bitcoin and most other cryptocurrencies extremely volatile financial assets in a holistic time-series view. Recent time-series Bitcoin prices show extreme volatilities that may not be explained by classical financial and economic theory. In recent time-series econometrics literature, there exist research papers that successfully attempt to model and forecast such unexplained volatility (e.g., Andersen et al, 2003). Throughout this paper, we explain the various methodologies and results used in artificial neural network models predicting cryptocurrency volatility. For a survey of predicting cryptocurrency prices, refer to Charandabi and Kamyar, 2021A
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