This work empirically examines six structural models of the term structure of credit risk spreads: Merton (1974), Longstaff & Schwartz (1995) (with and without stochastic interest rates), Leland & Toft (1996), Collin-Dufresne & Goldstein (2001), and a constant elasticity of variance model. The conventional approach to testing structural models has involved the use of observable data to proxy the latent capital structure process, which may introduce additional specification error. This study extends Jones, Mason & Rosenfeld (1983) and Eom, Helwege & Huang (2004) by using implicit estimation of key model parameters resulting in an improved level of model fit. Unlike prior studies, the models are fitted from the observed dynamic term structure of firm-specific credit spreads, thereby providing a pure test of model specification. The models are implemented by adapting the method of Duffee (1999) to structural credit models, thereby treating the capital structure process is truly latent, and simultaneously enforcing cross-sectional and time-series model constraints. Quasi-maximum likelihood parameter estimates of the capital structure process are obtained via the extended Kalman filter applied to actual market trade prices on 32 firms and 200 bonds for the period 1994 to 2000. / We find that including an allowance for time-variation in the market liquidity premium improves model specification. A simple extension of the Merton (1974) model is found to have the greatest prediction accuracy, although all models performed with similar prediction errors. At between 28.8 to 34.4 percent, the root mean squared error of the credit spread prediction is comparable with reduced-form models. Unlike Eom, Helwege & Huang (2004) we do not find a wide dispersion in model prediction errors, as evidenced by an across model average mean absolute percentage error of 22 percent. However, in support of prior studies we find an overall tendency for slight underprediction, with the mean percentage prediction error of between -6.2 and -8.7 percent. Underprediction is greatest with short remaining bond tenor and low rating. Credit spread prediction errors across all models are non-normal, and fatter tailed than expected, with autocorrelation evident in their time series. / More complex models did not outperform the extended Merton (1974) model; in particular stochastic interest-rate and early default accompanied by an exogenous write-down rate appear to add little to model accuracy. However, the inclusion of solvency ratio mean-reversion in the Collin-Dufresne & Goldstein (2001) model results in the most realistic latent solvency dynamics as measured by its implied levels of asset volatility, default boundary level, and mean-reversion rate. The extended Merton (1974) is found to imply asset volatility levels that are too high on average when compared to observed firm equity volatility. / We find that the extended Merton (1974) and the Collin-Dufresne & Goldstein (2001) models account for approximately 43 percent of the credit spread on average. For BB rated trades, the explained proportion rises to 55 to 60 percent. For investment grade trades, our results suggest that the amount of the credit spread that is default related is approximately double the previous estimate of Huang & Huang (2003). / Finally, we find evidence that the prediction errors are related to market-wide factors exogenous to the models. The percentage prediction errors are positively related to the VIX and change in GDP, and negatively related to the Refcorp-Treasury spread.

Accurate knowledge of water content in seasonal snow can be helpful for water resource management. In this study, a distributed temperature index snow model based on temperature and precipitation as forcing data, is used to estimate snow storage in the Jollie catchment approximately 20km east of the main divide of the central Southern Alps, New Zealand. The main objective is to apply a frequently used assimilation method, the ensemble Kalman square root filter, to assimilate remotely sensed snow covered area into the model and evaluate the impacts of this approach on simulations of snow water equivalent. A 250m resolution remotely sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS), specifically tuned to the study location was used. Temperature and precipitation were given on a 0.055 latitude/longitude grid. Precipitation was perturbed as input into the model, generating 100 ensemble members, which represented model error. Only observations of snow covered area that had less that 25% cloud cover classification were used in the assimilation precess. The error in the snow covered area observations was assumed to be 0.1 and grow linearly with cloud cover fraction up to 1 for a totally cloud covered pixel. As the model was not calibrated, two withholding experiments were conducted, in which observations withheld from the assimilation process were compared to the results. Two model states were updated in the assimilation, the total snow accumulation state variable and the total snow melt state variable. The results of this study indicate that the model underestimates snow storage at the end of winter and/or does not detect snow fall events during the ablation period. The assimilation method only affected simulated snow covered area and snow storage during the ablation period. That corresponded to higher correlation between modelled snow cover area and the updated state variables. Withholding experiments show good agreement between observations and simulated snow covered area. This study successfully applied the ensemble Kalman square root filter and showed its applicability for New Zealand conditions.

Zugl.: Wuppertal, Univ., Diss., 2005

Techn. Universiẗat, Diss., 2001--Berlin.

Techn. Universiẗat, Diss., 2003--Darmstadt.

Made available in DSpace on 2014-06-11T19:23:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2005Bitstream added on 2014-06-13T19:29:38Z : No. of bitstreams: 1 aguiar_cr_me_prud.pdf: 7492218 bytes, checksum: 117035e4185d5091d476aa36427a4feb (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os receptores GPS de uma freqüência são utilizados na maioria dos trabalhos de posicionamento com GPS. Dentre as diversas aplicações, pode-se citar aquelas em que é de suma importância obter as coordenadas da antena do receptor em tempo real, tais como navegação aérea, marítima e terrestre, monitoramento da troposfera e ionosfera, monitoramento de deslocamento de estruturas e tubulações, entre outras. Porém, uma das maiores fontes de erro para estas aplicações é o efeito da refração ionosférica. A determinação deste efeito tem sido feita com observações coletadas com receptores GPS de dupla freqüência, e a partir da estimativa dos valores da refração ionosférica, pode-se aplicar a correção nas medidas obtidas com receptores de uma freqüência. No Departamento de Cartografia da FCT/UNESP foi desenvolvido o modelo da ionosfera (Mod_Ion), onde a ionosfera é analiticamente representada pela série do tipo Fourier. Este modelo está implementado, em linguagem de programação Fortran, para ser executado no modo pósprocessado. O foco de interesse atual pela comunidade mundial é o que diz respeito à correção desses efeitos em tempo real. Um algoritmo utilizado para calcular a correção ionosférica, ou obter o TEC, em tempo real, é o filtro de Kalman. No Mod_Ion_FK foram introduzidas duas melhorias: a função de modelagem da ionosfera do Mod_Ion foi alterada; e o filtro de Kalman foi implementado. Os resultados dos experimentos realizados mostraram que a função de modelagem série de Fourier com 19 coeficentes e o processo aleatório Gauss-Markov, foram mais eficazes na correção do efeito sistemático devido à ionosfera, chegando à proporcionar uma melhora na acurácia resultante, do posicionamento por ponto em tempo real, de 90,75%, no período diário de máxima atividade da ionosfera. / Single frequency GPS receivers have been widely used in most of the GPS projects. Among the several applications, one can mention those that require to obtain the receiver's antenna coordinates in real time, such as aerial, maritime and terrestrial navigation, ionosphere and troposphere monitoring, and structure displacement monitoring. However, one of the main drawbacks of the GPS accuracy for L1 users is the ionospheric refraction, which affects, mainly, the point positioning. The determination of this error has been carried out with double frequency GPS measurements, and from these estimate values the corrections can be applied in the single frequency GPS measurements. In the FCT/UNESP, a regional ionosphere model (Mod_Ion) was developed for computing the ionosphere systematic error, as well as TEC (Total Electron Contents). The Mod_Ion was implemented to run in a batch processing mode. The current focus for the worldwide community is concerned to the correction of these error in real time. One of the algorithms used to calculate the ionosphere correction, as well as the TEC, in real time, is based on Kalman filtering. In the Mod_Ion_FK version two improvements were introduced: the function for ionosphere modeling in the Mod_Ion was modified; and the Kalman filter was implemented. The results of the experiments showed that the modeling function with 19 coefficient Fourier series and the Gauss-Markov process, were the most effective in the ionosphere systematic effect's corrections, providing a improvement in the accuracy of point positioning, of 90,75%, in period of the highest ionosphere activity.

Made available in DSpace on 2014-06-11T19:26:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-06-29Bitstream added on 2014-06-13T20:27:33Z : No. of bitstreams: 1 bedutti_ad_me_sjrp.pdf: 1999751 bytes, checksum: 437383410c3f4cc28c116c28e9f7054a (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / O controle de plantas daninhas constitui um dos principais desafios no cultivo de área agrícolas. Quando presentes em quantidades descontroladas, estas plantas geram a diminuição na produtividade e ocasionam perdas significativas e indesejáveis. As perdas, aliadas ao alto custo de controle, motivam o desenvolvimento de ferramentas no auxílio a tomada de decisão, como mapas da distribuição de daninhas, visando o manejo localizado de herbicidas. Neste trabalho, considera-se a aplicação de um modelo espaço-temporal para a construção de mapas da distribuição de sementes de plantas daninhas em uma área agrícola de plantação de milho (Zea mays). Foram analisados dados reais, para as espécies Digitaria ciliaris, Euphorbia heterophilla L., Cenchrus echinatus L. e Bidens Pilosa L. e tamb´em dados simulados. O modelo envolve a combinação de estimação por krigagem e o filtro de Kalman. / The control of weeds is a major challenge in cultivation of agricultural areas. When present in uncontrolled quantities, these plants generate a decrease in productivity and cause significant and undesirable losses. The losses, combined with the high cost of control, motivate the development of tools to aid in taking decision, as maps of distribution of weed, to located handling of herbicides. In this work, was considered the application of a spatial-temporal model for construction of distribution maps of seed weeds in an agricultural area of corn plantation (Zea mays). Were analyzed real data, for the species Digitaria ciliaris, Euphorbia heterophilla L., Cenchrus echinatus L. and Bidens Pilosa L., and also simulated data. The model involves a combination of kriging estimation and Kalman filter.

This thesis outlines the dynamic system analysis of a 3D Ultrasonic neuro- navigation system for use in motion capture studies. The work entails the development and implementation of methods for achieving the same. The objective of the project is to come up with an accurate dynamic 3D ultrasonic neuro-navigation system which can deliver up to sub mm accuracy within the operating workspace for use in image guided neuro surgery. The major focus of the work is to come up with a second order Kalman filter which can take out the outliers occurring in a static system in real time, thereby making the system more robust and accurate. Once the filter achieves the requisites, it can be integrated into the current motion tracking software which allows for the real time tracking of transmitters, hence the points of interest.

The stochasticity of cellular processes and the small number of molecules in a cell make deterministic models inappropriate for modelling chemical reactions at the single cell level. The Chemical Master Equation (CME) is widely used to describe the evolution of biochemical reactions inside cells stochastically but is computationally expensive. The Linear Noise Approximation (LNA) is a popular method for approximating the CME in order to carry out inference and parameter estimation in stochastic models. Data from stochastic systems is often aggregated over time. One such example is in luminescence bioimaging, where a luciferase reporter gene allows us to quantify the activity of proteins inside a cell. The luminescence intensity emitted from the luciferase experiments is collected from single cells and is integrated over a time period (usually 15 to 30 minutes), which is then collected as a single data point. In this work we consider stochastic systems that we approximate using the Linear Noise Approximation (LNA). We demonstrate our method by learning the parameters of three different models from which aggregated data was simulated, an Ornstein-Uhlenbeck model, a Lotka-Voltera model and a gene transcription model. We have additionally compared our approach to the existing approach and find that our method is outperforming the existing one. Finally, we apply our method in microscopy data from a translation inhibition experiment.

[pt] Este trabalho versa sobre previsão de reservas do tipo IBNR levando-se em conta uma ordenação diferente do triângulo de runoff incremental. Esta se dá por linhas empilhadas, originando, assim, uma série temporal univariada repleta de valores faltantes, cuja soma desses valores constitui o IBNR a ser estimado. Duas abordagens de estimação, inteiramente baseadas na teoria dos modelos em Espaço de Estado e do filtro de Kalman, são desenvolvidas, implementadas com dados reais de empresas seguradoras, e comparadas entre si e a outros métodos de estimação já consagrados na literatura atuarial. A primeira abordagem pauta-se no cálculo da matriz de covariâncias condicionais das componentes do IBNR, e a segunda é um processo de obtenção do IBNR por acumulação. Os resultados obtidos revelam, para as abordagens propostas, os seguintes pontos sumários: (i) plena eficiência e viabilidade computacional; (ii) sistemático ganho em termos de acurácia do IBNR estimado; e (iii) abrangência no que diz respeito às possibilidades de modelagem estatística dos dados de IBNR. / [en] This work deals with prediction of IBNR reserves under a different ordering of the non-cumulative runoff triangle. This is accomplished by stacking the rows, which results in a univariate time series with several missing values, whose corresponding sum is in fact the IBNR. Two estimation approaches, entirely based on state space methods and Kalman filtering, are developed, implemented with real data, and compared with some well established estimation methods for IBNR. The first approach consists in obtaining the conditional covariance matrix of the IBNR components, and the second tackles the IBNR estimation under an accumulation process. Three remarks emerge from the empirical results: (i)computational feasibility and efficiency; (ii)precision improvement for IBNR estimation; and (iii)flexibility in which concerns the IBNR modelling framework.