Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes

TL;DR

This paper introduces a novel bivariate Le9vy process model to jointly capture the jump dynamics of Bitcoin prices and media attention, with a focus on low-frequency data and semiparametric inference methods.

Contribution

It develops a new joint Le9vy process model for Bitcoin prices and media attention, including a semiparametric estimation procedure for parameters and jump measures.

Findings

Market attention modeled effectively by finite Le9vy measures

Proposed data-driven approach for Bitcoin price modeling

Demonstrated realistic jump dynamics in both series

Abstract

In this paper, we present a new bivariate model for the joint description of the Bitcoin prices and the media attention to Bitcoin. Our model is based on the class of the L\'evy processes and is able to realistically reproduce the jump-type dynamics of the considered time series. We focus on the low-frequency setup, which is for the L\'evy - based models essentially more difficult than the high-frequency case. We design a semiparametric estimation procedure for the statistical inference on the parameters and the L\'evy measures of the considered processes. We show that the dynamics of the market attention can be effectively modelled by the L\'evy processes with finite L\'evy measures, and propose a data-driven procedure for the description of the Bitcoin prices.