A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases.

The natural gas price is an essential financial variable that needs periodic modeling and predictive analysis for many practical implications. Macroeconomic euphoria and external uncertainty make its evolutionary patterns highly complex. We propose a two-stage granular framework to perform predictive analysis of the natural gas futures for the USA (NGF-USA) and the UK natural gas futures for the EU (NGF-UK) for pre-and during COVID-19 phases. The residuals of the previous stage are introduced as a new explanatory feature along with standard technical indicators to perform predictive tasks. The importance of the new feature is explained through the Boruta feature evaluation methodology. Maximal Overlap Discrete Wavelet Transformation (MODWT) is applied to decompose the original time-series observations of the natural gas prices to enable granular level forecasting. Random Forest is invoked on each component to fetch the respective predictions. The aggregated component-wise sums lead to final predictions. A rigorous performance assessment signifies the efficacy of the proposed framework. The results show the effectiveness of the residual as a feature in deriving accurate forecasts. The framework is highly efficient in analyzing patterns in the presence of a limited number of data points during the uncertain COVID-19 phase covering the first and second waves of the pandemic. Our findings reveal that the prediction accuracy is the best for the NGF-UK in the pre-COVID-19 period. Also, the prediction accuracy of the NGF-USA is better in the COVID-19 period than the pre-COVID-19 period.

A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials.

The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost.

Financial modeling, risk management of energy and environmental instruments and derivatives: past, present, and future.

In this preface, we investigate the past, study the present, and look for the future of financial modeling, risk management of energy and environmental instruments, and derivatives based on articles selected in this special issue (SI). We also summarize the significant findings of those articles and identify the research trends.

COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens.

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.