Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model
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.
The Bankwest Curtin Economics Centre team acknowledges the Wadjuk Nyungar people as the traditional owners of the land on which the Centre is situated.