Kostas Andriosopoulos

Multiple criteria decision aiding for finance: An updated bibliographic survey

Finance is a popular field for applied and methodological research involving multiple criteria decision aiding (MCDA) techniques. In this study we present an up-to-date bibliographic survey of the contributions of MCDA in financial decision making, focusing on the developments during the past decade. The survey covers all main areas of financial modeling as well as the different methodological approaches in MCDA and its connections with other analytical fields. On the basis of the survey results, we discuss the contributions of MCDA in different areas of financial decision making and identify established and emerging research topics, as well as future opportunities and challenges.

Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets

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 that include only energy-sector stocks from the US and the UK respectively. Daily data are used and the index-tracking problem for passive investment is addressed with two evolutionary algorithms – the differential evolution algorithm and the genetic algorithm. 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.

A Two-stage approach for energy efficiency analysis in European Union countries

This paper evaluates the energy efficiency of EU countries over the period 2000-2010. At the first stage, data envelopment analysis (DEA) is used, combining multiple energy consumption data and economic outputs. The efficiency estimates obtained from the analysis are evaluated in a second stage through a multiple criteria decision aiding methodology (MCDA). The proposed non-parametric approach combining DEA with MCDA enables modeling of the problem in an integrated manner, not only providing energy efficiency estimates but also supporting the analysis of the main contributing factors, as well as the development of a benchmarking model for energy efficiency evaluation at the country level.

Gold price forecasting with a neuro-fuzzy-based inference system

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.

A methodological approach for environmental characterization of ports

ABSTRACT This paper introduces a methodology for the characterization of ports, employing specifically defined eco-efficiency indicators and combining typically available data (handled cargo, containers and passengers) with ship exhaust pollutants values (mainly NOx, SOx and PM) and anticipated external costs (ECs) due to emitted air pollutants to provide a collective overview of all port-related economic and environmental activities. The results from an applied case study allow a comparative evaluation of 16 selected ports based on 17 different evaluation criteria and denote that the employment of such an overall approach can allow port authorities to improve managerial aspects, potentially lower operational costs and promote reduced environmental effects.

Risk management in the energy markets and Value-at-Risk modelling: A Hybrid approach

This paper proposes a set of VaR models appropriate to capture the dynamics of energy prices and subsequently quantify energy price risk by calculating VaR and ES measures. Amongst the competing VaR methodologies evaluated in this paper, besides the commonly used benchmark models, a MC simulation approach and a Hybrid MC with Historical Simulation approach, both assuming various processes for the underlying spot prices, are also being employed. All VaR models are empirically tested on eight spot energy commodities that trade futures contracts on NYMEX and the Spot Energy Index. A two-stage evaluation and selection process is applied, combining statistical and economic measures, to choose amongst the competing VaR models. Finally, both long and short trading positions are considered as it is extremely important for energy traders and risk managers to be able to capture efficiently the characteristics of both tails of the distributions.

Risk management in the energy markets and Value-at-Risk modelling: a hybrid approach

This paper proposes a set of Value-at-Risk (VaR) models appropriate to capture the dynamics of energy prices and subsequently quantify energy price risk by calculating VaR and expected shortfall measures. Amongst the competing VaR methodologies evaluated in this paper, besides the commonly used benchmark models, a Monte Carlo (MC) simulation approach and a hybrid MC with historical simulation approach, both assuming various processes for the underlying spot prices, are also being employed. All VaR models are empirically tested on eight spot energy commodities that trade futures contracts on the New York Mercantile Exchange (NYMEX) and the constructed Spot Energy Index. A two-stage evaluation and selection process is applied, combining statistical and economic measures, to choose amongst the competing VaR models. Finally, both long and short trading positions are considered as it is of utmost importance for energy traders and risk managers to be able to capture efficiently the characteristics of both tails of the distributions.

Contagion, Volatility Persistence, and Volatility Spill-Overs:The Case of Energy Markets During the European Financial Crisis

The aim of this paper is to investigate if, and to what extent, events from the three financially troubled EU markets (Greece, Ireland, and Portugal) affected energy prices during the recent EU financial crisis. More specifically, (i) we test for contagion effects from bond markets on energy/commodity prices, (ii) we examine whether the nature of energy price volatility is affected, and (iii) we investigate whether bond volatility from the financially distressed EU markets spills over, subsequently affecting energy/commodity return volatility. Our results indicate the existence of significant contagion effects from the bond markets of the three EU countries that received bailout packages on energy/commodity prices and significant changes in the nature of energy/commodity volatility during the EU financial crisis. We also report the existence of significant volatility spill-over effects.

Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

This study examines the development of corporate failure prediction models for European firms in the energy sector, using a large dataset from 18 countries. The construction of the models is based on a multiple criteria decision aid (MCDA) approach taking into account both ordinal criteria and nominal country-sector effects. The analysis is based on different modeling specifications. First, traditional financial variables are examined, which are then extended with additional country-level data related to the economic and business environment, as well as data about the energy efficiency policies of the countries and the characteristics of their energy markets and networks. The results indicate that energy-related attributes have high discriminating power and add valuable information compared to the other attributes.

Unconventional Oil: Will It Satisfy Future Global Oil Demand?

The role of unconventional resources (e.g., oil sands and extra-heavy oil) is anticipated to increase in the global oil market. Although we are facing a scarcity of conventional (low cost) oil resources, unconventional oil resources might manage (for a period of time) to supply constraints in terms of meeting expected increases in oil demand. Here, we use the ACEGES (Agent-based Computational Economics of the Global Energy System) model to investigate the potential impact of unconventional oil resources on the future evolution of the oil market on a global scale. The key assumption of the model is that technological improvements will allow unconventional oil production to increase at a rate similar to the rate of production of the conventional oil resources. An important observation from the ACEGES-based simulations is the significant shift of the peak production of oil (both conventional and unconventional) if and only if technological progress will allow upstream extraction rates for unconventional resources, similar to the historic extraction rates of conventional oil. Given the estimated potential of total oil resources, the ACEGES-based scenario suggests that the unconventional oil production may shift the peak year of total oil by 60 years or more, assuming favourable upstream investment plans and a continuous increase in the demand for crude oil products at a reasonable price. However, increased total oil production might not meet the unconstrained (high) growth rates of oil demand.