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

Forecasting Random Walks under Drift Instability

This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a single estimation window

Forecasting and forecast narratives: the Bank of England inflation reports

We analyze the narratives that accompany the numerical forecasts in the Bank of England's Quarterly Inflation Reports, 1997 - 2018. We focus on whether the narratives contain useful information about the future course of key macro variables over and above the point predictions, in terms of whether the narratives can be used to enhance the accuracy of the numerical forecasts. We also consider whether the narratives are able to predict future changes in the numerical forecasts. We find that a measure of sentiment derived from the narratives can predict the errors in the numerical forecasts of output growth, but not of inflation. We find no evidence that past changes in sentiment predict subsequent changes in the point forecasts of output growth or of inflation, but the adjustments to the numerical output growth forecasts have a systematic element

Toll-Like Receptor 8 Agonist and Bacteria Trigger Potent Activation of Innate Immune Cells in Human Liver

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The study was supported by a Grant core funding from the Agency for Science Technology and Research (A*STAR, Singapore) and a Singapore Translational Research Investigator Award (NRMC/StaR/013/2012) to AB as well as NIHR Biomedical Centre, Oxford, WT 091663MA, NIAID1U19AI082630-01, Oxford Martin School funding and an NIHR Senior Investigator award to PK

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

The empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class ofvector autoregressive (VAR) processes. The linear VAR model is extendedto permit cointegration, a range of deterministic processes, equilibrium restrictions and restrictions on long-run responses to technology shocks. Wefind support for a number of the features implied by the real business cyclemodel. For example, restricting long run responses to identify technologyshocks has reasonable support and important implications for the short runresponses to these shocks. Further, there is evidence that savings and investment ratios form stable relationships, but technology shocks do not accountfor all stochastic trends in our system. There is uncertainty as to the mostappropriate model for our data, with thirteen models receiving similar support, and the model or model set used has signficant implications for theresults obtained

Innate Immune Response of Human Plasmacytoid Dendritic Cells to Poxvirus Infection Is Subverted by Vaccinia E3 via Its Z-DNA/RNA Binding Domain

Plasmacytoid dendritic cells (pDCs) play important roles in antiviral innate immunity by producing type I interferon (IFN). In this study, we assess the immune responses of primary human pDCs to two poxviruses, vaccinia and myxoma virus. Vaccinia, an orthopoxvirus, was used for immunization against smallpox, a contagious human disease with high mortality. Myxoma virus, a Leporipoxvirus, causes lethal disease in rabbits, but is non-pathogenic in humans. We report that myxoma virus infection of human pDCs induces IFN-α and TNF production, whereas vaccinia infection does not. Co-infection of pDCs with myxoma virus plus vaccinia blocks myxoma induction effects. We find that heat-inactivated vaccinia (Heat-VAC; by incubating the virus at 55°C for 1 h) gains the ability to induce IFN-α and TNF in primary human pDCs. Induction of IFN-α in pDCs by myxoma virus or Heat-VAC is blocked by chloroquine, which inhibits endosomal acidification required for TLR7/9 signaling, and by inhibitors of cellular kinases PI3K and Akt. Using purified pDCs from genetic knockout mice, we demonstrate that Heat-VAC-induced type I IFN production in pDCs requires the endosomal RNA sensor TLR7 and its adaptor MyD88, transcription factor IRF7 and the type I IFN feedback loop mediated by IFNAR1. These results indicate that (i) vaccinia virus, but not myxoma virus, expresses inhibitor(s) of the poxvirus sensing pathway(s) in pDCs; and (ii) Heat-VAC infection fails to produce inhibitor(s) but rather produces novel activator(s), likely viral RNA transcripts that are sensed by the TLR7/MyD88 pathway. Using vaccinia gene deletion mutants, we show that the Z-DNA/RNA binding domain at the N-terminus of the vaccinia immunomodulatory E3 protein is an antagonist of the innate immune response of human pDCs to poxvirus infection and TLR agonists. The myxoma virus ortholog of vaccinia E3 (M029) lacks the N-terminal Z-DNA/RNA binding domain, which might contribute to the immunostimulating properties of myxoma virus