Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic...

This paper comprehensively investigates the connection between oil futures volatility and the financial market based on a data-rich and model-rich environment, which contains traditional prediction models, machine learning models, and combination models. The results highlight the efficiency of...

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the...

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the jump component is persistent when forecasting the oil...

The acquisition of external financing is an important factor affecting the development of enterprises and even the economic growth of a country. However, changes in the external environment often expose enterprises to uncertainties in obtaining external financing. Taking China’s initial public...

Our research focuses on forecasting the GDP growth rate by taking a considerable number of economic indicators from various frequencies into account within a new penalty-based mixed-frequency data model called the MIDAS-LASSO model. The empirical findings reveal that the MIDAS-LASSO model has a...