Search

Summarization: Following the importance of gold in the global economy and the high interest that has attracted recently, the objective of this paper is twofold: to predict the price of gold by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and compare its forecasting accuracy with various time-series forecasting methods and the 'Buy and Hold' (B&H) strategy. The results show that the ANFIS's accuracy is far superior to the performance of all compared methods and therefore ANFIS demonstrates the potential of neuro fuzzy-based modelling for predicting the gold's price. Presented on: International Journal of Financial Engineering and Risk Management

A dissertation submitted to the School of Production Engineering and Management at the Technical University of Crete in partial fulfilment of the requirements for the degree of Doctor of Philosophy Summarization: The aim of this thesis is the evaluation of energy efficiency in EU countries and industrial sectors over the period 2000-10 and 2000-09, respectively. In the first stage of analysis in which the energy efficiency in 26 EU countries is evaluated, we follow a two-stage approach based on Data Envelopment Analysis (DEA) and Multicriteria Decision Aiding approach (MCDA). The proposed two-stage DEA/MCDA approach can be easily used for benchmarking purposes, allowing for the formulation of a complete ranking of all countries under consideration, as well as the monitoring of the performance of a country over time using data solely at the country level, without having to resort to relative assessments in comparison to data from a set of peer countries. The results of the empirical analysis indicate that despite the considerable improvements achieved in terms of energy intensity, a more refined view of energy consumption and economic activity data shows that there is still much to be done to improve the actual energy efficiency of European countries. Additionally, the economic crisis of the past few years has had negative effects on energy efficiency. Furthermore, it is has been found that for European countries, the effect due to the consideration of the structure of their economic activity is stronger than the effect due to the introduction of a breakdown by their energy mix. Next, we extend our research to evaluating the energy efficiency trends of ten energy-intensive industries in 23 EU countries. Specifically, the performance of the construction, electricity, mining and quarrying, transport, food and tobacco, textiles and leather, pulp and paper, coke and chemicals, other non-metallic mineral and fabricated metal, machinery and equipment is examined. In the first stage, the DEA combined with the Malmquist Productivity Index (MPI) is performed to identify the energy efficiency trends and distinguish between the effects of efficiency and technology changes. In the second stage of our analysis, a two-level cross-classified multilevel modeling is applied to analyse the main drivers behind efficiency performance using a number of sector- and country- characteristics. The DEA results show that the pulp and coke are the most inefficient sectors, on average. Regarding the decomposition of the MPI, technology change is mainly responsible for the improvements achieved in most of the sectors. The two-level cross-classified model shows that the combination of sector and country levels is the most relevant in explaining the energy efficiency variance. It also shows that energy efficiency is higher for sectors that contribute more to the overall economic activity of a country (high contribution of sector value added to the total of the economy), as well as in a country with a diversified energy mix, and open and competitive energy production market. Overall, the evaluation models that are developed through this research are of major practical usefulness for monitoring, benchmarking and policy planning purposes. Furthermore, the conclusions of this research can help policy makers take effective policy decisions for energy efficiency improvement at both the country and industry level.

Summarization: This study evaluates the energy efficiency trends of five energy-intensive industries in 23 European Union (EU) countries over the period 2000–2009. In particular, the performance of the construction, electricity, manufacturing, mining and quarrying, and transport sectors is examined. The analysis is based on Data Envelopment Analysis (DEA) combined with the Malmquist Productivity Index (MPI), which allows for distinctions between efficiency and technology changes over time. At the second stage of the analysis, cross-classified multilevel modelling is applied to analyse the main drivers behind efficiency performance using a number of sector and country characteristics. Based on DEA results, an overall improvement in efficiency is observed in all sectors over the period. The decomposition of the MPI indicates that technology change is primarily responsible for the improvements achieved in most sectors. The results obtained by the cross-classified model show, among other things, that the high electricity prices, energy taxes, and market share of the largest generator in the electricity market have a negative effect on industrial energy efficiency. Presented on: Energy Policy