Bitcoin volatility forecasting: An artificial differential equation neural network

【Author】 Azizi, S. Pourmohammad; Huang, Chien Yi; Chen, Ti An; Chen, Shu Chuan; Nafei, Amirhossein

【Source】AIMS MATHEMATICS

【影响因子】2.739

【Abstract】In this article, an alternate method for estimating the volatility parameter of Bitcoin is provided. Specifically, the procedure takes into account historical data. This quality is one of the most critical factors determining the Bitcoin price. The reader will notice an emphasis on historical knowledge throughout the text, with particular attention paid to detail. Following the production of a historical data set for volatility utilizing market data, we will analyze the fundamental and computed values of Bitcoin derivatives (futures), followed by implementing an inverse problem modeling method to obtain a second-order differential equation model for volatility. Because of this, we can accomplish what we set out to do. As a direct result, we will be able to achieve our objective. Following this, the differential equation of the second order will be solved by an artificial neural network that considers the dataset. In conclusion, the results achieved through the utilization of the Python software are given and contrasted with a variety of other research approaches. In addition, this method is determined with alternative ways, and the outcomes of those comparisons are shown.

【Keywords】Bitcoin; volatility; differential equation; artificial neural network; forecasting; inverse problem

【发表时间】2023

【收录时间】2023-05-09

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-市场分析