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

【Author】 Ivanovski, Kris; Hailemariam, Abebe

【Source】INTERNATIONAL REVIEW OF ECONOMICS & FINANCE

【影响因子】3.399

【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 be-tween digital currency and other assets. This paper utilises a multivariate generalised autore-gressive 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 su-perior forecasting ability beyond certain horizon days compared to the DCC-GARCH model.

【Keywords】Forecasting; Cryptocurrency; Stock price; Correlation; Bitcoin; Ethereum

【发表时间】2023 JUL

【收录时间】2023-04-10

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-数字货币