Title: Advanced statistical models for cryptocurrency research
Authors: Jennifer Chan - University of Sydney (Australia) [presenting]
Abstract: Cryptocurrencies as of late have commanded global attention on a number of fronts: heightened pecuniary, technological infrastructures, and investment interest. Cryptocurrency is different from fiat currency in many ways: no institutional control, near instantaneous transaction and a fast changing cryptocurrency community. Yet there are many controversies surrounding cryptocurrency, its monetary role, price formulation, investment devices, etc. As cryptocurrency is increasingly accepted even by some major banks, we aim to derive advanced time series models to understand the statistical properties of cryptocurrency, including its notoriously wild volatility. This speculative nature of cryptocurrency has raised debates over its monetary role against speculative asset. We highlight some stylised facts about the volatility dynamic including its oscillatory persistence and leverage effect and relate these results to their respective cryptographic designs. The data analysis is initiated with a broad scope of cryptocurrencies, then a more detailed understanding of the top 5 by market capitalization and followed up with a specific focus on Bitcoin. The results favour Gegenbauer long memory over standard long memory filter to model the logarithm of the daily range.