This research paper investigates the various dependence structures across oil prices,
emission prices and stock markets for Middle East and Gulf Cooperation Council (GCC) …

This paper proposes a hybrid ensemble forecasting methodology that integrating empirical
mode decomposition (EMD), long short-term memory (LSTM) and extreme learning machine …

This study investigates the impact of the Ukraine-Russia conflict on energy cycles by
extending a business cycle-based approach in the energy sector. We employ the Markov …

We analyze the dynamic correlation between the carbon price and the stock returns of green
energy companies and calculate the hedging effect of the carbon price on stock returns in …

In response to environmental and climate change issues in recent decades, clean energy
resources have been promoted as substitutes for fossil fuel-based dirty energy resources …

This paper examines the dynamics of connectedness among the realized volatility indices of
16 clean energy stocks belonging to the SPGCE and the implied volatility indices of two …

As the crude oil price movement is influenced by increasingly diverse range of risk factors in
the crude oil markets, the crude oil price exhibits more complex nonlinear behavior and …

To address the drawback of single machine learning prediction model which cannot capture
the complex hidden factors of crude oil price, ensemble learning method has been widely …

Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk
in insurance. It is often that we encounter several risks, in practice, instead of single risk. In …

Abstract Flow Duration Curve (FDC) is an essential graphical tool for illustrating the
variability of observed historical streamflow. Achieving an advanced understanding of the …