Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility

【Author】 Tapia, Sebastian; Kristjanpoller, Werner

【Source】PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

【影响因子】3.778

【Abstract】This paper deals with bitcoin volatility forecasting, through a framework based on Long-Short Term Memory, multiplicative error and residual analysis. This framework is based on modeling the nonlinear behavior of the multiplicative error in order to improve forecast accuracy. The rapid growth of the cryptocurrency market, its high volatility and its applications in different commercial transactions have attracted the attention of academics and investors. Among cryptocurrencies, bitcoin is the one with the highest volume of trading. For this reason, it is important for investors and companies that use bitcoin as a means of payment to be able to predict its volatility more accurately. Nonetheless, this presents a great challenge given the chaotic behavior of the bitcoin volatility series. The results indicate that the proposed model is able to better capture non-linear behavior of the volatility time series, allowing a more accurate forecast in terms of MSE. (c) 2021 Elsevier B.V. All rights reserved.

【Keywords】Residual analysis; Bitcoin volatility; Multiplicative error model; Artificial neural networks

【发表时间】2022 MAR 1

【收录时间】2022-07-17

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币