Published Papers

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 illustrates the power of modern statistical modelling in estimating measures of market risk, here applied to the Brent and WTI spot price of oil. Both Value-at-Risk (VaR) and Expected Shortfall (ES) are cast in terms of conditional centiles based upon semiparametric regression models. Using the GAMLSS statistical framework, we stress the important aspects of selecting a highly flexible parametric distribution (skewed Student's t distribution) and of modelling both skewness and kurtosis as nonparametric functions of the price of oil futures. Furthermore, an empirical application characterises the relationship between spot oil prices and oil futures - exploiting the futures market to explain the dynamics of the physical market. Our results suggest that NYMEX WTI has heavier tails compared with the ICE Brent. Contrary to the common platitude of the industry, we argue that 'somebody knows something' in the oil business.

This article provides evidence regarding the performance of momentum investment strategies that is consistent with the Neoclassical Theory. More specifically, while momentum investment returns appear orthogonal to systematic risk in the extant literature, this article illustrates that they are due to correlated changes of hedge portfolio systematic risk exposures with market conditions. Momentum portfolios are excellent market timers in both expanding and contracting markets. Their returns however are generally not abnormal when timing is considered in an augmented unconditional Capital Asset Pricing Model (CAPM), while the standard version erroneously considers them to be so, possibly explaining why momentum studies have so far rejected the Neoclassical Theory.

As the world economy highly depends on crude oil, it is important to understand the dynamics of crude oil production and export capacity of major oil-exporting countries. Since crude oil resources are predominately located in the OPEC Middle East, these countries are expected to have significant leverage in the world crude oil markets by taking into account a range of uncertainties. In this study, we develop a scenario for crude oil export and production using the ACEGES model considering uncertainties in the resource limits, demand growth, production growth, and peak/decline point. The results indicate that the country-specific peak of both crude oil export and production comes in the early this century in the OPEC Middle East countries. On the other hand, they occupy most of the world export and production before and after the peak points. Consequently, these countries are expected to be the key group in the world crude oil markets. We also find that the gap between the world crude oil demand and production broadens over time, meaning that the acceleration of the development of ultra-deep-water oil, oil sands, and extra-heavy oil will be required if the world continuous to heavily rely on oil products.

Multiattribute additive value functions constitute an important class of models for multicriteria decision making. Such models are often used to rank a set of alternatives or to classify them into pre-defined groups. Preference disaggregation techniques have been used to construct additive value models using linear programming techniques based on the assumption of monotonic preferences. This paper presents a methodology to construct non-monotonic value function models, using an evolutionary optimization approach. The methodology is implemented for the construction of multicriteria models that can be used to classify the alternatives in pre-defined groups, with an application to credit rating.