Scheduling reconstruction operations with modes of execution

Since the decline of the Cold War, the risk of major conflict between powerful, industrialized nations has decreased. Insecurities in the 21st century are more apt to arise from the debris of imploding states. Such situations may require intervention—military or otherwise—by concerned states. To meet this new operational challenge, nations must adapt their planning procedures to account for Security, Stabilization, Transition, and Reconstruction Operations (SSTRO). This paper develops a project scheduling framework for post-conflict reconstruction that schedules reconstruction activities to maximize the positive impact to the local economy during the initial phase of SSTRO. Specifically, this paper builds on the Multimode Resource Constrained Project Scheduling Problem with Generalized Precedence Relations (MM-RCPSP-GPR) using goal programming to maximize the reconstruction operations’ positive impact to local population's economic welfare. This MM-RCPSP-GPR variant is applied to a notional example to illustrate its potential use in post-conflict SSTRO. The basic framework can be extended to other settings.     

Schauder estimates for higher-order parabolic systems with time irregular coefficients

We prove Schauder estimates for solutions to both divergence and non-divergence type higher-order parabolic systems in the whole space and a half space. We also provide an existence result for the divergence type systems in a cylindrical domain. All coefficients are assumed to be only measurable in the time variable and Hölder continuous in the spatial variables.     

Classical mean-variance model revisited: pseudo efficiency

Investigating the inverse problem of the classical Markowitz mean-variance formulation: Given a mean-variance pair, find initial investment levels and their corresponding portfolio policies such that the given mean-variance pair can be realized, we reveal that any mean-variance pair inside the reachable region can be achieved by multiple portfolio policies associated with different initial investment levels. Therefore, in the mean-variance world for a market of all risky assets, the common belief of monotonicity: ‘The larger you invest, the larger expected future wealth you can expect for a given risk (variance) level’ does not hold, which stimulates us to extend the classical two-objective mean-variance framework to an expanded three-objective framework: to maximize the mean and minimize the variance of the final wealth as well as to minimize the initial investment level. As a result, we eliminate from the policy candidate list the set of pseudo efficient policies that are efficient in the original mean-variance space, but inefficient in this newly introduced three-dimensional objective space.     

Formation of seasonal groups and application of seasonal indices

Estimating seasonal variations in demand is a challenging task faced by many organisations. There may be many stock-keeping units (SKUs) to forecast, but often data histories are short, with very few complete seasonal cycles. It has been suggested in the literature that group seasonal indices (GSI) methods should be used to take advantage of information on similar SKUs. This paper addresses two research questions: (1) how should groups be formed in order to use the GSI methods? and (2) when should the GSI methods and the individual seasonal indices (ISI) method be used? Theoretical results are presented, showing that seasonal grouping and forecasting may be unified, based on a Mean Square Error criterion, and K-means clustering. A heuristic K-means method is presented, which is competitive with the Average Linkage method. It offers a viable alternative to a company's own grouping method or may be used with confidence if a company lacks a grouping method. The paper gives empirical findings that confirm earlier theoretical results that greater accuracy may be obtained by employing a rule that assigns the GSI method to some SKUs and the ISI method to the remainder.     

Optimal ROE loan pricing with or without adverse selection

The authors describe the structural solution of the loan rate as a function of default and response risk that maximizes expected return on equity for a lender's portfolio of risky loans. Under the assumptions of our model, the non-linear differential equation for the optimizing price is found to be separable in transformed financial, response and risk variables. With an end-point condition where default-free borrowers are willing to borrow at loan rates higher than the lender's cost of funds, general solutions are obtained for cases where default probabilities may depend explicitly on the offered loan rate and where adverse selection may or may not be present. For the general solution, we suggest a numerical algorithm that involves the sequential solutions of two separate transcendental equations each one of which depends on parameters of the risk and response scores. For the special case where the borrower's default probability is conditionally independent of loan rate, it is shown that the optimal solution is independent of Basel regulations on equity capital.     

Company failure prediction with limited information: newly incorporated companies

Developing ‘Internal Rating Systems’ (IRB) for corporate risk management requires building risk (PD) models geared to the specific characteristics of corporate sub-populations (eg small and medium-sized enterprises (SMEs), private companies, listed companies, sector specific models), tuned to changes in the macro environment, and, of course, tailored to the available data. Tracking the risk of ‘newly incorporated companies’ provides a particular challenge since there is very limited publically available data in the time period from incorporation date until the submission of the first accounts. Yet a large number of these companies fail (via bankruptcy). We employ a substantial database to estimate discrete time hazard models (DHM) over the period 2000–2008 (4?427?896 firm-year observations and 34?903 incidences of insolvency), inclusive of macro and regional economic conditions, that capture early indicators of financial stress and measure aspects of the characteristics of board of directors in order to assess the utility of this type of non-financial information in failure prediction models.