A mixed integer linear programming model for optimal sovereign debt issuance

Copyright @ 2011, Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in the European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version is available at the link below.Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data

Úloha Wnt signalizace v embryonálním vývoji

Ph.D. thesis: The role of Wnt signaling in embryonic development Naoko Dupačová (Fujimura) ABSTRACT Wnt signaling plays important roles in multiple developmental processes. The binding of Wnt ligands to their receptors and coreceptors activates three main downstream pathways: canonical Wnt/β-catenin signaling, which results in the activation of β-catenin/Tcf mediated gene expression and noncanonical Wnt/PCP and Wnt/Ca2+ pathways. In this thesis, we aimed at studying the role of Wnt/β-catenin signaling during embryonic development, especially in the telencephalon and the eye. Wnt/β-catenin signaling is essential for the maintenance of proliferation of neuronal progenitor cells and dorso-ventral specification during the telencephalon development. To provide further insights, we studied transcriptional targets of canonical Wnt signaling. We show that the ectopic activation of Wnt/β-catenin signaling results in the up-regulation of Sp5 gene, which encodes a member of the Sp1 transcription factor family. A proximal promoter of Sp5 gene contains five Tcf/Lef binding sites that mediate direct regulation of Sp5 expression by canonical Wnt signaling. We further provide evidence that Sp5 works as a transcriptional repressor. Finally, our data strongly suggest that Sp5 has the same DNA binding specificity as Sp1 and...Disertační práce: Úloha Wnt signalizace v embryonální vývoj Naoko Dupačová (Fujimura) Abstrakt Wnt signalizace hraje důležitou roli v mnoha vývojových procesech. Vazba ligandu Wnt na příslušné receptory následně aktivuje tři hlavní dráhy: kanonickou Wnt/β-catenin signalizaci, která má za následek aktivaci β-catenin/Tcf zprostředkované genové exprese a nekanonické Wnt/PCP a Wnt/Ca2+ dráhy. V této práci jsem se zaměřila na studium úlohy Wnt/β-catenin signalizace během embryonálního vývoje, zejména v koncovém mozku (telencefalonu) a očích. Wnt/β-catenin signalizace je nezbytná pro zachování proliferace neurálních progenitorových buněk a pro dorzo-ventrální specifikaci koncového mozku. V této práci jsme se zabývali transkripčními cíly kanonické Wnt signalizace během tohoto procesu. Ukázali jsme, že výsledkem ektopické aktivace Wnt/β-catenin signalizace je zvýšená exprese genu Sp5, který kóduje protein Sp5 z rodiny Sp1 transkripčních faktorů. Proximální promotor genu Sp5 obsahuje pět Tcf/Lef vazebných míst, která zprostředkují přímou regulaci Sp5 v důsledku kanonické Wnt signalizace. Dále jsme prokázali, že Sp5 funguje jako transkripční represor, mající stejnou DNA vazebnou specifitu jako Sp1, a tudíž potlačuje expresi Sp1 cílových genů, jako je p21. Došli jsme k závěru, že transkripční faktor Sp5...Department of Genetics and MicrobiologyKatedra genetiky a mikrobiologieFaculty of SciencePřírodovědecká fakult

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a "representative scenario" (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality

Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper

Statistical metrics for assessing the quality of wind power scenarios for stochastic unit commitment

In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day-ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics to assess whether the scenario set possesses desirable properties that are expected to lead to a lower cost in stochastic unit commitment. A new mass transportation distance rank histogram is developed for assessing the reliability of unequally likely scenarios. Energy scores, rank histograms and Brier scores are applied to alternative sets of scenarios that are generated by two very different methods. The mass transportation distance rank histogram is best able to distinguish between sets of scenarios that are more or less calibrated according to their bias, variability and autocorrelation