Integrated risk/cost planning models for the US Air Traffic system

A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size

Stochastic debt sustainability analysis for sovereigns and the scope for optimization modeling

We argue that sovereign debt sustainability analysis must be augmented by stochastic correlated risk factors and a risk measure to capture tail effects. Crisis situations can thus be adequately specified and analyzed with sufficient accuracy to warrant the relevance of policy decisions. In this context there is significant scope for optimization modeling for both strategic planning and operational management. We discuss diverse aspects of the problem of debt sustainability and highlight modeling approaches that can be brought to bear on the problem. Results with the fictitious, but nor unrealistic, Kingdom of Atlantis, which is sinking under excessive debt, illustrate the proposed models

Asset and Liability Modelling for Participating Policies with Guarantee

We study the problem of asset and liability management of participating insurance policies with guarantees. We develop a scenario optimization model for integrative asset and liability management, analyse the tradeoffs in structuring such policies, and study alternative choices in funding them. The nonlinearly constrained optimization model can be linearised through closed form solutions of the dynamic equations. Thus large-scale problems are solved with standard methods. We report on an empirical analysis of policies offered by Italian insurers. The optimized model results are in general agreement with current industry practices. However, some inefficiencies are identified and potential improvements are highlighted

Risk Management Optimization for Sovereign Debt Restructuring

Debt restructuring is one of the policy tools available for resolving sovereign debt crises and, while unorthodox, it is not uncommon. We propose a scenario analysis for debt sustainability and integrate it with scenario optimization for risk management in restructuring sovereign debt. The scenario dynamics of debt-to-GDP ratio are used to define a tail risk measure, termed conditional Debt-at-Risk. A multi-period stochastic programming model minimizes the expected cost of debt financing subject to risk limits. It provides an operational model to handle significant aspects of debt restructuring: it collects all debt issues in a common framework, and can include contingent claims, multiple currencies and step-up or linked contractual features. Alternative debt profiles \u2013 obtained by maturity rescheduling, interest payment concessions or nominal value haircuts \u2013 are analyzed for their expected cost-risk tradeoffs. With a suitable re-calculation of the efficient frontier, the risk of debt un-sustainability of alternative risk profiles can be ascertained with a given confidence level. The model is applied to Greece sovereign debt crisis analyzing the suitability of various proposals to restore debt sustainability

Designing portfolios of financial products via integrated simulation and optimization models

We analyze the problem of debt issuance through the sale of innovative financial products. The problem is broken down to questions of designing the financial products, specifying the debt structure with the amount issued in each product, and determining an optimal level of financial leverage. We formulate a hierarchical optimization model to integrate these three issues and provide constructive answers. Input data for the models are obtained from Monte Carlo simulation procedures that generate scenarios of holding period returns of the designed products. The hierarchical optimization model is specialized for the problem of issuing a portfolio of callable bonds to fund mortgage assets. The upper level optimization program is multimodal, and a tabu search procedure is developed for its solution. Empirical results illustrate the efficacy of the developed models in designing the appropriate structure of the callable bonds and making optimal allocations of equity and debt among the designed products. Computational results with the implementation of tabu search-on both serial and parallel computers-are also presented

A model for designing callable bonds and its solution using tabu search

We formulate the problem of designing callable bonds as a non-linear, global, optimization problem. The data of the model are obtained from simulations of holding-period returns of a given bond design, which are used to compute a certainty equivalent return, viz., some target assets. The design specifications of the callable bond are then adjusted so that the certainty equivalent return is maximized. The resulting problem is multi-modal, and a tabu search procedure, implemented on a distributed network of workstations, is used to optimize the bond design. The model is compared with the classical portfolio immunization model, and the tabu search solution technique is compared with simulated annealing for solving the global optimization program. It is shown that the global optimization model yields higher returns than portfolio immunization. It is also shown that tabu search is computationally more efficient than simulated annealing in solving the model, and it produces better solutions

Integrated simulation and optimization models for tracking international fixed income indices

Portfolio managers in the international fixed income markets must address jointly the interest rate risk in each market and the exchange rate volatility across markets. This paper develops integrated simulation and optimization models that address these issues in a common framework. Monte Carlo simulation procedures generate jointly scenarios of interest and exchange rates and, thereby, scenarios of holding period returns of the available securities. The portfolio manager's risk tolerance is incorporated either through a utility function or a (modified) mean absolute deviation function. The optimization models prescribe asset allocation weights among the different markets and also resolve bond-picking decisions. Therefore several interrelated decisions are cast in a common framework. Two models - an expected utility maximization and a mean absolute deviation minimization - are implemented and tested empirically in tracking a composite index of the international bond markets. Backtesting over the period January 1997 to July 1998 illustrate the efficacy of the optimization models in dealing with uncertainty and tracking effectively the volatile index. Of particular interest is the empirical demostration that the integrative models generate portfolios that dominate the portfolios obtained using classical disintegrated approaches