One of the financial risks an agent has to deal with is market risk. Market risk is caused by the uncertainty attached to asset values. There exit various measures trying to model market risk. The most widely accepted one is Value-at- Risk. However Value-at-Risk does not encourage portfolio diversification in general, whereas a consistent risk measure has to do so. In this work, risk measures satisfying these consistency conditions are examined within theoretical basis. Different types of coherent and convex risk measures are investigated. Moreover the extension of coherent risk measures to multiperiod settings is discussed.

Virtually all methods of learning dynamic models from data start from the same basic assumption: that the learning algorithm will be provided with a single or multiple sequences of data generated from the dynamic model. However, in quite a few modern time series modeling tasks, the collection of reliable time series data turns out to be a major challenge, due to either slow progression of the dynamic process of interest, or inaccessibility of repetitive measurements of the same dynamic process over time. In most of those situations, however, we observe that it is easier to collect a large amount of non-sequence samples, or random snapshots of the dynamic process of interest without time information. This thesis aims to exploit such non-sequence data in learning a few widely used dynamic models, including fully observable, linear and nonlinear models as well as Hidden Markov Models (HMMs). For fully observable models, we point out several issues on model identifiability when learning from non-sequence data, and develop EM-type learning algorithms based on maximizing approximate likelihood. We also consider the setting where a small amount of sequence data are available in addition to non-sequence data, and propose a novel penalized least square approach that uses non-sequence data to regularize the model. For HMMs, we draw inspiration from recent advances in spectral learning of latent variable models and propose spectral algorithms that provably recover the model parameters, under reasonable assumptions on the generative process of non-sequence data and the true model. To the best of our knowledge, this is the first formal guarantee on learning dynamic models from non-sequence data. We also consider the case where little sequence data are available, and propose learning algorithms that, as in the fully observable case, use non-sequence data to provide regularization, but does so in combination with spectral methods. Experiments on synthetic data and several real data sets, including gene expression and cell image time series, demonstrate the effectiveness of our proposed methods. In the last part of the thesis we return to the usual setting of learning from sequence data, and consider learning bi-clustered vector auto-regressive models, whose transition matrix is both sparse, revealing significant interactions among variables, and bi-clustered, identifying groups of variables that have similar interactions with other variables. Such structures may aid other learning tasks in the same domain that have abundant non-sequence data by providing better regularization in our proposed non-sequence methods.

This dissertation comprises of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation process. We examine patterns in the time-varying covariation among product-level inflation rates that aggregate up to industry-level inflation rates that in turn aggregate up to the overall inflation rate. The aggregate inflation volatility closely tracks the time path of this covariation, which is seen to be driven primarily by the variances of common shocks shared by all products, and by the covariances between idiosyncratic product-level shocks. We formulate a forecasting system that comprises of models for mean inflation rate and its variance, and exploit the index structure of the aggregate inflation rate using the HTS framework. Using a dynamic model selection approach to forecasting, we obtain forecasts that are between 9 and 155 % more accurate than a SARIMA-GARCH(1,1) for the aggregate inflation volatility. Chapter 3 is on improving forecasts using forecast combinations. The paper documents the software implementation of the open source R package for forecast combination that we coded and published on the official R package depository, CRAN. The GeomComb package is the only R package that covers a wide range of different popular forecast combination methods. We implement techniques from 3 broad categories: (a) simple non-parametric methods, (b) regression-based methods, and (c) geometric (eigenvector) methods, allowing for static or dynamic estimation of each approach. Using S3 classes/methods in R, the package provides a user-friendly environment for applied forecasting, implementing solutions for typical issues related to forecast combination (multicollinearity, missing values, etc.), criterion-based optimisation for several parametric methods, and post-fit functions to rationalise and visualise estimation results. The package has been listed in the official R Task Views for Time Series Analysis and for Official Statistics. The brief empirical application in the paper illustrates the package’s functionality by estimating forecast combination techniques for monthly UK electricity supply. Chapter 4 introduces HTS forecasting and forecast combination to a healthcare staffing context. A slowdown of healthcare budget growth in the UK that does not keep pace with growth of demand for hospital services made efficient cost planning increasingly crucial for hospitals, in particular for staff which accounts for more than half of hospitals’ expenses. This is facilitated by accurate forecasts of patient census and churn. Using a dataset of more than 3 million observations from a large UK hospital, we show how HTS forecasting can improve forecast accuracy by using information at different levels of the hospital hierarchy (aggregate, emergency/electives, divisions, specialties), compared to the naïve benchmark: the seasonal random walk model applied to the aggregate. We show that forecast combination can improve accuracy even more in some cases, and leads to lower forecast error variance (decreasing forecasting risk). We propose a comprehensive parametric approach to use forecasts in a nurse staffing model that has the aim of minimising cost while satisfying that the care requirements (e.g. nurse hours per patient day thresholds) are met.

La simulation numérique est l’un des outils les plus puissants pour concevoir etoptimiser les systèmes industriels. Dans le domaine de la Dynamique des FluidesNumériques (CFD, "Computational Fluid Dynamics"), la simulation auxgrandes échelles (LES, "Large Eddy Simulation") est aujourd’hui largementutilisée pour calculer les écoulements turbulents réactifs, où les tourbillons degrande taille sont calculés explicitement, tandis que l’effet de ceux de petitetaille est modelisé. Des modèles de sous-mailles sont requis pour fermer leséquations de transport en LES, et dans le contexte de la simulation de la combustionturbulente, le plissement de la surface de flamme de sous-maille doitêtre modélisé.En général, augmenter le plissement de la surface de flamme de sous-maille favorisela combustion. L’amplitude de la promotion est donnée par une fonctiond’efficacité, qui est dérivée d’une hypothèse d’équilibre entre la production etla destruction de la surface de flamme. Dans les méthodes conventionnelles,le calcul de la fonction d’efficacité nécessite une constante qui dépend de lagéométrie de la chambre de combustion, de l’intensité de turbulence, de larichesse du mélange de air-carburant etc, et cette constante doit être fixée audébut de la simulation. Autrement dit, elle doit être déterminé empiriquement.Cette thèse développe un modèle de sous-maille pour la LES en combustionturbulente, qui est appelé le modèle dynamique de flammelette épaissie (DTF,"dynamic thickened flamelet model"), qui détermine la valeur de la constanteen fonction des conditions de l’écoulement sans utiliser des données empiriques.Ce modèle est tout d’abord testé sur une flamme laminaire unidimensionnellepour vérifier la convergence de la fonction d’efficacité vers l’unité (aucun plissementde la surface de flamme de sous-maille). Puis il est appliqué en combinaisonavec le modèle dynamique de Smagorinsky (Dynamic Smagorinskymodel) aux simulations multidimensionnelles d’une flamme en V, stabilisée enaval d’un dièdre. Les résultats de la simulation en trois dimensions sont alorscomparés avec les données expérimentales obtenues sur une expérience de mêmegéométrie. La comparaison montre la faisabilité de la formulation dynamique. / Numerical simulation is one of the most powerful tools to design and optimizeindustrial facilities. In the field of Computational Fluid Dynamics (CFD),Large Eddy Simulation (LES) is widely used to compute turbulent reactingflows, where larger turbulent motions are explicitly computed, while only theeffect of smaller ones is modeled. Subgrid models are required to close thetransport equations in LES, and in the context of the simulation of turbulentcombustion, the subgrid-scale wrinkling of the flame front must be modeled.In general, subgrid-scale flame wrinkling promotes the chemical reaction. Themagnitude of the promotion is given through an efficiency function derivedfrom an equilibrium assumption between production and destruction of flamesurface. In conventional methods, the calculation of the efficiency functionrequires a constant which depends on the geometry of the combustion chamber,turbulence intensity, the equivalence ratio of the fuel-air mixture, and so on;this constant must be prescribed at the beginning of the simulation. In otherwords, empirical knowledge is required.This thesis develops a subgrid-scale model for LES of turbulent combustion,called the dynamic thickened flamelet (DTF) model, which determines the valueof the constant from the flow conditions without any empirical input.The model is first tested in a one-dimensional laminar flame to verify the convergenceof the efficiency function to unity (no subgrid-scale flame front wrinkling).Then it is applied to multi-dimensional simulations of V-shape flamestabilized downstream of a triangular flame holder in combination with the dynamicSmagorinsky model. The results of the three-dimensional simulation arethen compared with the experimental data obtained through the experimentof the same geometry. The comparison proves the feasibility of the dynamicformulation.

Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-01T16:57:56Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) 2015, Desoneração da folha de pagamentos -final- Carlos Eduardo - final.pdf: 1472443 bytes, checksum: 40ab31bfa833ed1d099a25ceb24fc326 (MD5) / Made available in DSpace on 2016-03-01T16:57:56Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) 2015, Desoneração da folha de pagamentos -final- Carlos Eduardo - final.pdf: 1472443 bytes, checksum: 40ab31bfa833ed1d099a25ceb24fc326 (MD5) Previous issue date: 2015-06-13 / CAPES / Neste trabalho, apresentaremos políticas tributárias com o intuito de avaliar o impacto de longo prazo da desoneração da folha de pagamentos sobre consumo agregado, o estoque de capital, produto e o bem-estar entre gerações. Para isso, utilizaremos o modelo de gerações sobrepostas (Overlapping Generations Models) com vida finita, probabilidade de morte e previdência social. A inovação dessa pesquisa frente a literatura, foi a simulação com alíquota zero sobre a contribuição patronal, a incorporação da despesa da previdência e do fator previdenciário. Com essas modificações, temos um modelo próximo da economia brasileira. Os resultados apontaram que a desoneração da folha de pagamentos provocou aumentos no produto, capital e consumo de longo prazo, sem grandes sacrifícios para a economia no curto prazo. / In this work, we present tax policies in order to assess the impact of long-term relief payroll on aggregate consumption, the stock of capital, product and welfare between generations. For this, we use the model of overlapping generations with finite life, risk of death and social security. The innovation of this research literature forward was the simulation with zero tax rate on the employer's contribution, the incorporation of the social security expense and the social security factor. With these changes, we have a model close to the Brazilian economy. The results showed that the relief payroll caused increases in product, capital and long-term consumption without major sacrifice for the economy in the short term.

Orientador: Edevar Luvizotto Junior / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-11T21:41:10Z (GMT). No. of bitstreams: 1 Anjo_LuizFernandoResendedosSantos_D.pdf: 3588080 bytes, checksum: 122271523488dc8a40a0d509bd1b0cf8 (MD5) Previous issue date: 2008 / Resumo: Este texto descreve as etapas que objetivam a utilização da estrutura originalmenteproposta por Todini e Pilati no chamado método gradiente (MG), utilizado para análise em regime permanente em instalações a condutos forçados, na formulação de um modelo dinâmico inercial rígido (MDIR), para a análise de escoamentos transitórios lentos neste tipo de instalação. São apresentadas as bases teóricas para esta nova modelação, justificadas através do equacionamento geral do escoamento fluido em condutos forçados. Os resultados obtidos pelo MDIR são comparados com os resultados obtidos pelo programa EPANET que utiliza o método gradiente. Discussões a respeito da importância da incorporação do efeito de inércia são apresentadas através de um estudo de casos, no caso de modelação hidráulica, e para análises de qualidade decorrentes desta. / Abstract: This research describes the stages which aim the use of the original structure proposed by Todini and Pilati in the so called gradient method (GM), used in the analysis in steady state of pipe networks, in the formulation of an inertial rigid dynamic model (IRDM) to analyse slow transients in this type of installation. The theoretical bases are presented for this new method, justified by the general equation of fluids flow in pipes networks. The results obtained by the MDIR are compared to the results obtained in the EPANET program, which uses the gradient method. Discussion on the importance of the incorporation of the inertia effect are presented through a study case of hydraulic modelation and for quality analysis which derived from it. / Doutorado / Recursos Hidricos / Doutor em Engenharia Civil

We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. / Series: Department of Economics Working Paper Series

This master’s thesis describes equations of motion for dynamic model of nonholonomic constrained system, namely the trident robotic snakes. The model is studied in the form of Lagrange's equations and D’Alembert’s principle is applied. Actually this thesis is a continuation of the study going at VUT about the simulations of non-holonomic mechanisms, specifically robotic snakes. The kinematics model was well-examined in the work of of Byrtus, Roman and Vechetová, Jana. So here we provide equations of motion and address the motion planning problem regarding dynamics of the trident snake equipped with active joints through basic examples and propose a feedback linearization algorithm.

The thesis deals with modelling and control of wheeled mobile robots. The thesis introduces the mathematical models of diferentially driven mobile robot, robot with Ackerman steering and a skid steered robot. The thesis describes open-loop and feedback control. Feedback control is divided into point-to-point control, path following and trajectory tracking.

The thesis includes the basic principles of lead-acid, NiCd, NiMH and Li-Ion batteries. There is a description of their features and phenomena that processes in the batteries during charging and discharging. The most important part is devoted to dynamic models of these types of batteries, the parameters that are necessary for their modeling, and testing of absolute error models. Further, the model in Matlab was created which allows the simulation of the GRID-OFF photovoltaic system using the battery model.