Published Papers

This paper reproduces the performance of an international market capitalization shipping stock index and two physical shipping indexes by investing only in US stock portfolios. The index-tracking problem is addressed using the differential evolution algorithm and the genetic algorithm. Portfolios are constructed by a subset of stocks picked from the shipping or the Dow Jones Composite Average indexes. To test the performance of the heuristics, three different trading scenarios are examined: annually, quarterly and monthly rebalancing, accounting for transaction costs where necessary. Competing portfolios are also assessed through predictive ability tests. Overall, the proposed investment strategies carry less risk compared to the tracked benchmark indexes while providing investors the opportunity to efficiently replicate the performance of both the stock and physical shipping indexes in the most cost-effective way.

We show that the application of flexible semi-parametric statistical techniques enables significant improvements in model fitting of macroeconomic models. As applied to the explanation of the past economic growth (since 1900) in US, UK and Japan, the new results demonstrate quite conclusively the non-linear relationships between capital, labour and useful energy with economic growth. They also indicate that output elasticities of capital, labour and useful energy are extremely variable over time. We suggest that these results confirm the economic intuition that growth since the industrial revolution has been driven largely by declining energy costs due to the discovery and exploitation of relatively inexpensive fossil fuel resources. Implications for the 21st century, which are also discussed briefly by exploring the implications of an ACEGES-based scenario of oil production, are as follows: (a) the provision of adequate and affordable quantities of useful energy as a pre-condition for economic growth and (b) the design of energy systems as 'technology incubators' for a prosperous 21st century.

Due to the increasing importance of natural gas for modern economic activity, and gas's non-renewable nature, it is extremely important to try to estimate possible trajectories of future natural gas production while considering uncertainties in resource estimates, demand growth,production growth and other factors that might limit production. Inthisstudy, we develop future scenarios for natural gas supply using the ACEGES computational laboratory. Conditionally on the currently estimated ultimate recover-able resources, the 'Collective View' and 'Golden Age' Scenarios suggest that the supply of natural gas is likely to meet the increasing demand for natural gas until at least 2035. The 'Golden Age' Scenario suggests significant 'jumps' of natural gas production - important for testing the resilience of long-term strategies.

In this article the financial/ownership structures of agribusiness cooperatives are analyzed to examine whether new cooperative models perform better than the more traditional ones. The assessment procedure introduces a new financial decision-aid approach, which is based on data-analysis techniques in combination with a preference ranking organization method of enrichment evaluations (PROMETHEE II). The application of this multicriteria decision-aid approach allows the rank ordering of cooperatives based on the most prominent financial ratios. The financial ratios were selected using principal component analysis. This analytical procedure reduces the dimensionality of large numbers of interrelated financial performance measures. We assess the financial success of Dutch agribusiness cooperatives for the period 1999-2010. Results show that there is no clear-cut evidence that cooperative models used to attract extra members' investments and/or outside equity perform better than the more traditional models. This suggests that ownership structure of cooperatives is not always a decisive factor for their financial success. [EconLit citations: Q130, G320, C440].

This paper investigates the behaviour of spot prices in eight energy markets that trade futures contracts on NYMEX. We consider two types of models, a mean-reverting model, and a spike model with mean reversion that incorporates two different speeds of mean reversion; one for the fast mean-reverting behaviour of prices after a jump occurs, and another for the slower mean reversion rate of the diffusive part of the model. We also extend these models to incorporate time-varying volatility in their specification, modelled as a GARCH and an EGARCH process. We compare the relative goodness of fit of the different modelling variations both in sample, using Monte Carlo simulations, as well as out-of-sample, in a Value-at-Risk (VaR) setting.

Our results indicate the presence of a "leverage effect" for WTI, Heating Oil and Heating Oil-WTI crack spread, whereas for the remaining energy markets we find the presence of an "inverse leverage" effect. Also, the addition of the EGARCH specification for the volatility process improves both the in-sample fit as well as the out-of-sample VaR performance for most energy markets that we examine.

Abstract  

In times of uncertainties scenarios offer a solution. Starting with Royal Dutch Shell by the late 1960s, corporate scenarios are intended to challenge managers' "personal microcosms" and to reflect the present and the past, before structuring the uncertainties of the future. Therefore, scenarios act as 'early warning systems' by focusing on the driving forces that makes a difference to decision-makers.

Keywords

Oil scenarios, ACEGES, energy scenarios, Shell's scenarios, oil strategies

This paper reproduces the performance of a geometric average Spot Energy Index by investing only in a subset of stocks from the Dow Jones Composite Average, the FTSE 100 and Bovespa Composite indexes, and in two pools including only stocks of the energy sector from the US and the UK respectively. Daily data are used and the index-tracking problem for passive investment is addressed with two innovative evolutionary algorithms; the differential evolution algorithm and the genetic algorithm, respectively. The performance of the suggested investment strategy is tested under three different scenarios: buy-and-hold, quarterly, and monthly rebalancing;accounting for transaction costs where necessary.

Abstract 

The fusion of agent-based and geospatial models represents an exciting new synthesis for social science and economics. It has the potential to improve the theory and the practice of modelling complex real-world phenomena. Yet, to date, there has been little systematic analysis at the conceptual and logical levels of how to fuse agent-based and geospatial models for the representation and reasoning of socioeconomic phenomena. Here both sets of issues are explored. In particular, it will be argued that the development of synthetic models requires autonomous agents and flexible organisational structures that can complete their objectives while situated in a dynamic and uncertain geoenvironment represented by the concept of Elementary_geoParticle. As an example of the concept, I present a preliminary conceptual model of global energy to demonstrate the validity and possible uses of the proposed technique.