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

Sebastian Tapia and Werner Kristjanpoller

Physica A: Statistical Mechanics and its Applications, 2022, vol. 589, issue C

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.

Keywords: Residual analysis; Bitcoin volatility; Multiplicative error model; Artificial neural networks (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121008724

DOI: 10.1016/j.physa.2021.126613

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