Expectile hidden Markov regression models for analyzing cryptocurrency returns

Foroni, Beatrice; Merlo, Luca; Petrella, Lea

doi:10.1007/s11222-023-10377-2

Stat Comput

2024

DOI: 10.1007/s11222-023-10377-2

|View full text |Cite

Expectile hidden Markov regression models for analyzing cryptocurrency returns

Beatrice Foroni,

Luca Merlo,

Lea Petrella

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

Foroni,

Merlo,

Petrella

2024

Statistical Modelling

View full text Add to dashboard Cite

The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate this phenomenon and develop statistical methods able to capture their interrelationships, the links with other global systems and, at the same time, the serial heterogeneity. Here we introduce hidden Markov regression models for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows us to focus on extreme returns and describe their temporal evolution by introducing time-dependent coefficients, evolving according to a latent Markov chain. Moreover to model their time-varying dependence structure, we consider elliptical copula functions defined by state-specific parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm. The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and major world market indices.

show abstract

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

Foroni,

Merlo,

Petrella

2024

Statistical Modelling

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Expectile hidden Markov regression models for analyzing cryptocurrency returns

Cited by 1 publication

References 54 publications

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

Contact Info

Product

Resources

About