The risks of cryptocurrencies with long memory in volatility, non-normality and behavioural insights

Tak Kuen Siu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper aims to study the impacts of long memory in conditional volatility and conditional non-normality on market risks in Bitcoin and some other cryptocurrencies using an Autoregressive Fractionally Integrated GARCH model with non-normal innovations. Two tail-based risk metrics, namely Value at Risk (VaR) and Expected Shortfall (ES), are adopted to study the tail behaviour of market risks in Bitcoin and some other cryptocurrencies. Empirical investigations for the tail behaviour based on real exchange rate data of cryptocurrencies are conducted. An extreme-value-theory-based approach is used to study potential improvements in the estimation for the risk metrics under GARCH-type models. The possibility of explosive regimes in cryptocurrencies’ volatilities is examined using Markov-switching GARCH models.

Original languageEnglish
Pages (from-to)1991-2014
Number of pages24
JournalApplied Economics
Volume53
Issue number17
DOIs
Publication statusPublished - 9 Apr 2021

Keywords

  • behavioural Anomalies
  • conditional Non-Normality
  • conditional Volatility
  • Cryptocurrencies
  • long Memory
  • tail Risk

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