Forecasting the stock-cryptocurrency relationship: evidence from a dynamic GAS model

Kris Ivanovski, Abebe Hailemariam

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

The impact of cryptocurrency on other assets has become a subject of intense research, given the rise of digital currency over the last decade. However, unlike traditional assets, cryptocurrency has been subject to extreme movements in price and volatility. As a result, it has become important for investors and risk managers to model and forecast volatility and correlation between digital currency and other assets. This paper utilises a multivariate generalised autoregressive score (GAS) model to study the time-varying dependence between stock prices (S&P500, NASDAQ, Dow Jones Industrial) and cryptocurrencies (Bitcoin and Ethereum). The results show that the GAS framework outperforms the traditional DCC-GARCH model, capturing the volatility persistence and non-linearity between stock and cryptocurrency. Regarding the correlations, while we identify a time-varying relationship, the strength of this relationship is in the low-to-moderate range. In addition, our forecasting exercise shows that the GAS specification has superior forecasting ability beyond certain horizon days compared to the DCC-GARCH model.

Original languageEnglish
Pages (from-to)97-111
Number of pages15
JournalInternational Review of Economics and Finance
Volume86
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Bitcoin
  • Correlation
  • Cryptocurrency
  • Ethereum
  • Forecasting
  • Stock price

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