Contents of Volume 1, Number 2

December 2005

A Simple Method for Computing Value at Risk Using PCA and QMC

V. Son Lai, Y. Sakni, I. Soumaré

Abstract: Managing financial risk is a complex task for financial institutions and portfolio managers worldwide. Value at Risk (VaR) is a popular risk metric. In this paper we propose a simple approach to compute VaR using a Canadian fixed income portfolio. Our approach is three fold. First, to reduce the dimensionality, we construct orthogonal factors using Principal Components Analysis (PCA) on Canadian term structure data. Second, the entire term structure is constructed by linear interpolation from the existing spot rates. And third, to derive VaR values, we perform simulations by Quasi Monte Carlo (QMC) and compare the QMC results with those obtained by standard Monte Carlo (MC). We find that QMC converges faster than MC. However, for the same number of iterations, QMC takes more computational time. The methodology can be easily implemented with available software, since many software include subroutines to generate QMC sequences. The normative prescription is that one should use QMC whenever PCA produces few factors explaining the original variables affecting the portfolio value.

Exchange Rate Forecasting by Neuro-Fuzzy Techniques

G. Atsalakis

Abstract: Modelling the human behaviour in the market of the exchange rate has always been an important challenge for the researchers. Apart from the traditional methods used in forecasting short-term foreign exchange rates, during the last years many researchers started to use soft computing techniques in order to achieve better results. In this paper a neuro-fuzzy model is presented. The model is trained by the training data and, then, the testing data are used for model validation. The model uses a time series data of daily quotes of the euro/dollar exchange rate. Root mean square error and other statistical measures are used to evaluate the model performance. Additionally a comparison with conventional forecasting methods is employed.

Pattern Recognition in the Athens Stock Market: Further Evidence Using a Non-Parametric Approach

E.P. Koumanakos, C. Siriopoulos, C.G. Papoulias

Abstract: A nonparametric test like stochastic dominance analysis, which relies on cumulative densities of observed returns, requires no assumptions regarding the nature of underlying return distributions and imposes few restrictions on investor utility functions. Therefore, this study uses stochastic dominance comparisons to audit previous parametric tests of the day of the week anomaly in Athens Stock Exchange between 1985 and 2004. The results of stochastic dominance analysis, by taking into consideration the thin trading that is common in such a market, show that the day effect is a fact in ASE.

Financial Performance Measurement: A Multicriteria Methodology

L. Biggiero, G. Iazzolino, D. Laise

Abstract: In this paper we propose an ELECTRE I methodology to choose among different strategic alternatives. We argue that business performance and therefore strategic alternatives choices should be evaluated taking into account a set of various criteria. This thesis holds not only if we consider the many stakeholders (shareholders, customers, workers, etc.) as, for example, in a balanced scorecard, but even if we exclusively refer to the shareholder point of view. As a multicriteria valuation is generally incompatible with a maximizing approach, we propose a multicriteria decision making approach that offers satisfying solutions. We will show that satisfying solutions to a multicriteria valuation problem may be rigorously obtained through an outranking methodology. Application of this in a real case study will be presented.

Hierarchical Modeling for the Agricultural Sector in Greece and Bilevel Programming

A. Mavrommati, A. Migdalas

Abstract: In this article a linear bilevel model is presented, the first level of which concerns the EU and the second level the Greek government as the representative of the Greek agricultural sector. In this problem the EU is the leader who would like to minimize the subsidies it pays for yields and the expenses in terms of products. The agricultural sector is the follower and its objective is to maximize profits by selecting the best mix of crops to grow in terms of the EU regulations. The mathematical problem includes a large scale model. A new cutting-plane methodology is proposed.