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

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  • Ivanovski, Kris
  • Hailemariam, Abebe

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.

Suggested Citation

  • Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.
    • Handle: RePEc:eee:reveco:v:86:y:2023:i:c:p:97-111
    DOI: 10.1016/j.iref.2023.03.008
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