Comparing different economic linkages among commodity futures

Gasoline (GA) and kerosene (KO) are extracted from crude oil (CO), such that the three fuel commodities share a chemical link. On the other hand, GA also shares an industrial link with natural rubber (NR) and palladium (PA) as complementary commodities that are heavily consumed by the automobile industry. We contrast the information content embedded in the two economic linkages. Focusing on TOCOM futures contracts written on the five commodities and centering on GA, we confirm that incremental information provided by either CO, KO or NR, PA over a buy-and-hold strategy and a naive forecast, are both statistically and economically significant. While the chemical link forecast is more profitable, a double-link forecast generated from a VECM with two cointegrating vectors (KO-GA and GANR prices) outperforms both single-link forecasts based on risk-adjusted profit net of transaction costs. Further comparisons against the profitability of commodity-based momentum strategies documented in Erb and Harvey (2006) and Miffre and Rallis (2007) show that the double-link forecast holds its own against the most profitable of the 75 momentum strategies considered. This strongly suggests that not only are there incremental profits to be gained from harnessing and combining economic links among commodity futures, the resultant incremental profits are economically significant against other proven commodity-based trading strategies in the existing literature.

Are shocks to commodity prices persistent?

This paper considers the issue of whether shocks to ten commodity prices (gold, silver, platinum, copper, aluminum, iron ore, lead, nickel, tin, and zinc) are persistent or transitory. We use two recently developed unit root tests, namely the Narayan and Popp (NP) [14] test and the Liu and Narayan (LN) [26] test. Both tests allow for two structural breaks in the data series. Using the NP test, we are able to reject the unit root null for iron ore and tin. Using the GARCH-based unit root test of LN, we are able to reject the unit root null for five commodity prices (iron ore, nickel, zinc, lead, and tin). Our findings, thus, suggest that only shocks to gold, silver, platinum, aluminum, and copper are persistent.

A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

A modelling framework for optimisation of commodity production by minimising the impact of climate change

The agricultural sector is vulnerable to the impact of climate change due to decreasing rainfall, increasing temperature, and the frequency of extreme weather events. A modelling framework was developed and applied to identify issues, problems and opportunities arising in regional agricultural systems as a consequence of climate change. This integrated framework blends together land suitability analysis, uncertainty analysis and an optimisation approach to establish optimal agricultural land-use patterns on a regional scale for current and possible future climate scenarios. The framework can also be used to identify (i) regions under threat of productivity decline, and (ii) alternative crops and their locations that can cope better with changing climate. The methods and contents of the framework are presented by means of a case study developed in the South West Region of Victoria, Australia. The results can be used to assess land suitability in support of optimised crop allocations across a local region, and to underpin the development of a regional adaptation policy framework designed to reduce the vulnerability of the agriculture sector to the impacts of climate change.

Supporting decision making : the BI sweet spot

In this paper, we propose the concept: the BI Sweet Spot. The BI Sweet Spot ecosystem includes mobile computing, cloud computing and Big Data. We provide an overview for each of the key components and explain how these three components support the BI Sweet Spot. We also discuss best practices for managing these essential components. This study is the first-of-its-kind work in the BI research that considers the inter-relationships and the combined effect of mobile, cloud and Big Data.