Panos Pardalos :

PhD , Professor of Industrial and Systems Engineering at the University of Florida and affiliated faculty member of the Computer Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program, Director of the Center for Applied Optimization, President of Deal-FX S.A.
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PhD DIC, Director of the Centre for Quantitative Finance, Imperial College UK School of Management.
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PhD, Director, Department of Research and Monitoring of the Capital Market Hellenic Capital Market Commission.

PhD, Alumni Professor of Management Science Faculty of Commerce, University of British Columbia
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President Athens Stock Exchange S.A.

PhD, HERMES Center on Computational Finance and Economics, University of Cyprus and The Wharton Financial Institutions Center.
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PhD, Tokyo Institute Of Technology Institute Of Industrial Engineering And Management.
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PhD, Associate Professor , Department of Industrial and Systems Engineering, University of Florida.
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In this talk we focus on global optimization issues in Supply Chain (SC) and E-commerce, particularly distribution and transportation systems in a SC. We propose
solution methods for minimum network flow problems with piecewise linear concave cost functions. Computational results with large scale problems indicate that the proposed techniques find good quality sub-optimal solutions.

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"Pricing and hedging in incomplete markets"
Nicos Christofides

The classical approach to the pricing and hedging of derivative instruments involves the construction and trading of a portfolio of basic assets so as to replicate the possible derivative payoffs. The whole approach is based on the no-arbitrage principle. In many markets, however, such replication is not always possible (the market is incomplete) either because of jumps in the underlying price process, (as is the case with pricing credit derivatives) or because the underlying cannot be traded in the quantities needed (because of liquidity restrictions), or for a variety of other reasons. In such cases, the arbitrage considerations alone can only provide upper and lower bounds on the option price - not an exact value. The talk will develop the "pseudo-arbitrage" and "near-arbitrage" arguments which can form a sufficient basis for an exact pricing methodology. Computational pricing comparisons for some credit derivatives will be given.

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"An attempt to understand the world stock markets 1996 to 2001"
William T. Ziemba, UBC

This talk traces the US stock market and it's interaction with other financial and equity markets around the world focusing on the recent period 1996-2001. A historical record for the past 100 plus years will serve as background. A review of the Japanese 1949-1989 rise and the 1990-2001 decline sets the stage to focus on the US. We see a dramatic rise from 1996 to early 2000 during which two variables dominated: size and momentum. Then we see a decline in the rest of 2000 and the emergence of a bear market in February/March 2001. As usual interest rates and earnings play a key role but other factors are involved. Bubble versus changing fundamentals is discussed in Japan and in the US Nasdaq. The behavior of various signals and anomaly ideas are assessed. The wealth effect and a scorecard of the losers and winners will also be discussed.

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"Estimation of Failure Probability by Semi-Definite Logit Model"
Prof. Hiroshi Konno
Tokyo Institute Of Technology
Institute Of Industrial
Engineering And Management
2-12-1 Oh-Okayama Meguro-Ku
Tokyo
152 Japan
konno@me.titech.ac.jp

Linear logit model is often used for estimating the failure probability of enterprises. This model is based upon the assumptions that
the failure probability is a monotonic function of the financial factors, which is not universally valid. To handle non-monotonic situation,
we introduce a semi-definite logit model where the exponential term of the logit function is replaced by a semi-definite quadratic
function. The resulting likelihood maximization problem becomes a concave maximization problem under semi-definite constraints,
which can be solved efficiently by using cutting plane algorithm in an efficient way. We will demonstrate that this model outperforms
linear and general quadratic logit models.

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"Scenario optimization asset and liability modeling for endowments with minimum guarantees"
Stavros A. Zenios
HERMES Center on Computational Finance and Economics
University of Cyprus and The Wharton Financial Institutions Center

Endowments with a minimum guaranteed rate of return appear in insurance policies, pension plans and social security plans. In several cases, especially in the insurance industry, such endowments also participate in the business and receive bonuses from the firm's asset portfolio. In this paper we develop a scenario based optimization model for asset and liability management of participating insurance policies with minimum guarantees. The model allows the analysis of the tradeoffs facing an insurance firm in structuring its policies as well as the choices in covering their cost. The model is applied to the analysis of policies offered by Italian insurance firms. While the optimized model results are in general agreement with current industry practices, inefficiencies are still identified and potential improvements are suggested.

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"Risk Management Using Conditional Value-at-Risk"
Stanislav Uryasev
Uryasev@ise.ufl.edu
http://www.ise.ufl.edu/uryasev

Value-at-Risk (VaR), a widely used performance measure, answers the question: what is the maximum loss with a specified confidence level? Although VaR is a very popular measure of risk, it has undesirable properties such as lack of
sub-additivity, i.e., VaR of a portfolio with two instruments may be greater than the sum of individual VaRs of these two instruments. Also, VaR is difficult to optimize when calculated using scenarios. In this case, VaR is non-convex, non-smooth as a function of positions, and it has multiple local extrema.
An alternative measure of losses, with more attractive properties, is Conditional Value-at-Risk (CVaR), which coincides in some special cases with Mean Excess Loss, (Mean Shortfall). CVaR, is a coherent measure of risk (sub-additive, convex, and other nice mathematical properties). Moreover, as it was shown recently, it can be optimized using linear programming (LP), which allow handling portfolios with very large numbers of instruments and scenarios. Numerical experiments indicate that the minimization of CVaR also leads to near optimal solutions in VaR terms because CVaR is always greater than or equal to VaR. Moreover, when the return-loss distribution is normal, these two measures are equivalent, i.e., they provide the same optimal portfolio.
CVaR can be used in conjunction with VaR and is applicable to the estimation of risks with non-symmetric return-loss distributions. Although CVaR has not become a standard in the finance industry, it is likely to play a major role as it currently does in the insurance industry. Similar to the Markowitz mean-variance approach, CVaR can be used in return-risk analyses. For instance, we can calculate a portfolio with a specified return and minimal CVaR.

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