House price prediction has traditionally been approached using linear or spatial linear hedonic models and focused on big cities. In this study, we developed a flexible spatiotemporal model (FSTM) to explore the spatiotemporal characteristics of the residential house price and the impact factors...

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...

Addressing climate challenges and achieving the Sustainable Development Goals (SDGs) necessitate a deep understanding of how green policies influence carbon emissions. Therefore, this study explores the impact of green finance, innovation, and taxation on carbon emissions in OECD countries while...

While the relationship among land price, transportation cost, and urban sprawl under the condition of local governments monopolizing land supply has been rarely discussed in the literature, this paper addresses this gap by hypothesizing based on the monocentric city model. The hypothesis...

To investigate the impact of Green Digital Finance (GDF) policies on sustainable regional development goals, this study exploits the implementation of China's green fnance reform and innovation pilot zones as a quasi-natural experiment to examine the theory and impact of policy channels on...