We provide an analysis of the market data of the major cryptocurrencies by summing a multivariate hidden Markov process also known as the latent Markov process. We model jointly the daily log-returns of BTC, ETH, XRP, LTC, and BCH. The observed log-returns are assumed to be correlated according to a variance-covariance matrix conditionally on a latent Markov process of first-order having a discrete number of latent states. In order to compare states according to their volatility, we estimate the specific variance-covariance matrix of each state. Maximum likelihood estimation of the model parameters is carried out by the Expectation-Maximization algorithm. The latent states can be ordered according to expected average values of the log-returns and their estimated volatility. We consider different model specifications in terms of number of latent states, which are identified in terms of expected log-returns and level of volatility. Under each considered scenario we also predict the latent state by the maximum a posteriori rule.

Pennoni, F., Bartolucci, F., Forte, G., & Ametrano, F. (2020). Multivariate Hidden Markov model: An application to study correlations among cryptocurrency log-returns. In The 2nd Crypto Asset Lab Conference (pp.1-27).

Multivariate Hidden Markov model: An application to study correlations among cryptocurrency log-returns

Pennoni, F.;Forte, G.;Ametrano, F.
2020

Abstract

We provide an analysis of the market data of the major cryptocurrencies by summing a multivariate hidden Markov process also known as the latent Markov process. We model jointly the daily log-returns of BTC, ETH, XRP, LTC, and BCH. The observed log-returns are assumed to be correlated according to a variance-covariance matrix conditionally on a latent Markov process of first-order having a discrete number of latent states. In order to compare states according to their volatility, we estimate the specific variance-covariance matrix of each state. Maximum likelihood estimation of the model parameters is carried out by the Expectation-Maximization algorithm. The latent states can be ordered according to expected average values of the log-returns and their estimated volatility. We consider different model specifications in terms of number of latent states, which are identified in terms of expected log-returns and level of volatility. Under each considered scenario we also predict the latent state by the maximum a posteriori rule.
Si
abstract
Scientifica
Expectation-Maximization algorithm, latent variables, time series
English
The 2nd Crypto Asset Lab Conference
https://cryptoassetlab.diseade.unimib.it/docs/CAL2020/Quantitative_Modelling_3.pdf
Pennoni, F., Bartolucci, F., Forte, G., & Ametrano, F. (2020). Multivariate Hidden Markov model: An application to study correlations among cryptocurrency log-returns. In The 2nd Crypto Asset Lab Conference (pp.1-27).
Pennoni, F; Bartolucci, F; Forte, G; Ametrano, F
File in questo prodotto:
File Dimensione Formato  
2ndCryptoAsset_LabConference2020_Pennoni.pdf

accesso aperto

Descrizione: Slides of the presentation
Tipologia di allegato: Other attachments
Dimensione 640.87 kB
Formato Adobe PDF
640.87 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/319989
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact