B0841
Title: Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market
Authors: Beatrice Foroni - University of Pisa (Italy) [presenting]
Luca Merlo - European University of Rome (Italy)
Lea Petrella - Sapienza University of Rome (Italy)
Abstract: 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 the phenomena and develop statistical methods able to capture their interrelationships, the links with other global systems, and, at the same time, the serial heterogeneity. For these reasons, hidden Markov regression models are introduced for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows focus on extreme returns and describes their temporal evolution by introducing time-dependent coefficients evolving according to a latent Markov chain. Moreover, to model their time-varying dependence structure, elliptical copula functions are considered to be defined by state-specific parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm. The empirical analysis investigates the relationship between the daily returns of five cryptocurrencies and major world market indices.
