<p dir="ltr">This paper examines the resilience of global food and energy supply chains against the background of recent world disruptions such as China-US trade war, novel coronavirus disease 2019 (COVID-19) pandemic, and Russia’s incursion into Ukraine. It aims at improving forecast methodologies and providing early indications of market stressors by considering three key cracks or spreads within the food and energy industries soy crush spread, crude crack spread, and cattle finish spread. The study uses Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space (ETS) and Vector Error Correction Model (VECM). The profit relationships are examined in these models with regard to potential problems for supply chains in the soybean crushing industry, cattle finishing, and crude oil refining sectors. It also compares forecasting approaches like univariate (ARIMA & ETS) and multivariate (VECM). This means that it tries to gauge how accurate each one is in predicting where a given sector may be heading or where there are risks likely to happen. The situation is further complicated by on-going capacity expansions in these sectors which are expected to face more challenges due to geopolitical tensions as well as efforts to mitigate climate change internationally.The overall goal of the research is to develop forecasting methods to help industry participants, policymakers, and small producers make informed decisions amid volatility and the threat of imminent supply chain disruptions.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25676265 |
| Date | 26 April 2024 |
| Creators | Xiaoyu Hu (18431343) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Forecasting_Commodity_Production_Spread/25676265 |
Page generated in 0.0017 seconds