Learning Cross-dependency of Cryptocurrencies from Multivariate Time Series Models

8 Pages Posted: 18 Feb 2020

See all articles by Thanakorn Nitithumbundit

Thanakorn Nitithumbundit

affiliation not provided to SSRN

Jennifer Chan

The University of Sydney

Date Written: January 22, 2020

Abstract

The ever-growing volume of cryptocurrency transactions including those from major banks indicates the importance to understand the new cryptocurrency market. We analyse several cryptocurrencies simultaneously to study their cross-dependency while allowing for their wide volatility, high kurtosis and strong persistence. The vector autoregressive moving average model with Student's t innovations is proposed to capture these features. We consider four cryptocurrencies, namely Bitcoin, Ripple, Litecoin and Dash, which have top market capitalisation and estimate the model using the computational efficient expectation/conditional maximisation algorithm. We interpret the results in relation to their technological setups.

Keywords: Student's t distribution, vector ARMA model, persistence, ECM algorithm, cryptocurrencies

JEL Classification: C01

Suggested Citation

Nitithumbundit, Thanakorn and Chan, Jennifer, Learning Cross-dependency of Cryptocurrencies from Multivariate Time Series Models (January 22, 2020). Available at SSRN: https://ssrn.com/abstract=3523516 or http://dx.doi.org/10.2139/ssrn.3523516

Thanakorn Nitithumbundit

affiliation not provided to SSRN

Jennifer Chan (Contact Author)

The University of Sydney ( email )

University of Sydney
Sydney, NC NSW 2006
Australia
61293514873 (Phone)
2218 (Fax)

HOME PAGE: http://https://www.maths.usyd.edu.au/u/jchan/index.html

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