Expectile hidden Markov regression models for analyzing cryptocurrency returns

TL;DR

This paper introduces a novel expectile hidden Markov model tailored for analyzing cryptocurrency returns, emphasizing extreme events and their temporal dynamics within a risk management context.

Contribution

It develops a linear expectile hidden Markov model with time-dependent coefficients driven by a Markov chain, specifically designed for cryptocurrency return analysis.

Findings

Effective modeling of extreme cryptocurrency returns.

Successful application to Bitcoin and market indices.

Enhanced risk assessment capabilities.

Abstract

In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.