Predicting the stock prices of G7 countries with Bitcoin prices

Abstract

This paper attempts to establish that some inherent features of the Bitcoin price can be exploited to produce better forecast results for stock prices. It does so by constructing predictive models for stock prices of G7 countries with symmetric and asymmetric prices of Bitcoin. The underlying statistical properties of Bitcoin prices such as persistence and conditional heteroscedasticity are captured in the estimation process using the Westerlund and Narayan (2015) estimator that allows for such effects in forecasting. There are two striking findings from the analysis. First, the results suggest that accounting for asymmetries is more likely to enhance the predictive power of Bitcoin in forecasting stock prices regardless of the data sample and forecast horizon. Secondly, the Bitcoin-based predictive model for stock prices, particularly the asymmetric variant, outperforms the Fractionally Integrated Autoregressive Moving Average (ARFIMA) model. While there are concerns as to whether the cryptocurrencies are veritable substitutes to the conventional financial assets, their close link with the developed stock exchanges such as those in the G7 countries suggests that they share some common characteristics such as news effects [asymmetries] which can be exploited when forecasting the behaviour of stock prices.