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Bitcoin price modelling via analysis of Google Trends data: Lévy-based approach
Finance Research Letters. 2025. Vol. 86. No. A. Article 108301.
Morozova E., Panov V.
This paper presents a new approach to modeling the Bitcoin prices using the Lévy processes - a class of stochastic processes that are able to realistically capture the jump-type dynamics of financial time series. Our method is inspired by recent research on Bitcoin, which suggests that the prices are closely connected to the media attention to this topic. This attention can be measured by the number of searches for the word “Bitcoin” on Google, as tracked by Google Trends. We show that the dynamics of the media attention can be described by a subclass of Lévy processes, which consists of the sums of compound Poisson processes and Brownian motions. We fit the model for the Google Trends data, divide the timeline into several segments with no significant changes in media attention, and use stable processes (another subclass of Lévy processes) to model the Bitcoin prices within each segment. An empirical study shows the performance of this method.
Keywords: Fourier methodМетод ФурьеLevy processesбиткоинПроцессы Левиstable processesустойчивые процессыBitcoin pricingmedia attention
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