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Causalidade Granger em medidas de risco; Granger Causality with Risk Measures

Murakami, Patricia Nagami
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 02/05/2011 Português
Relevância na Pesquisa
46.69%
Esse trabalho apresenta um estudo da causalidade de Granger em Risco bivariado aplicado a séries temporais financeiras. Os eventos de risco, no caso de séries financeiras, estão relacionados com a avaliação do Valor em Risco das posições em ativos. Para isso, os modelos CaViaR, que fazem parte do grupo de modelos de Regressão Quantílica, foram utilizado para identificação desses eventos. Foram expostos os conceitos principais envolvidos da modelagem, assim como as definições necessárias para entendê-las. Através da análise da causalide de Granger em risco entre duas séries, podemos investigar se uma delas é capaz de prever a ocorrência de um valor extremo da outra. Foi realizada a análise de causalidade de Granger usual somente para como comparativo.; Quantile Regression, Value at Risk, CAViaR Model, Granger Causality, Granger Causality in Risk

Estimação de medidas de risco utilizando modelos CAViaR e CARE; Risk measures estimation using CAViaR and CARE models.

Silva, Francyelle de Lima e
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 06/08/2010 Português
Relevância na Pesquisa
36.63%
Neste trabalho são definidos, discutidos e estimados o Valor em Risco e o Expected Shortfall. Estas são medidas de Risco Financeiro de Mercado muito utilizadas por empresas e investidores para o gerenciamento do risco, aos quais podem estar expostos. O objetivo foi apresentar e utilizar vários métodos e modelos para a estimação dessas medidas e estabelecer qual o modelo mais adequado dentro de determinados cenários.; In this work Value at Risk and Expected Shortfall are defined, discussed and estimated . These are measures heavily used in Financial Market Risk, in particular by companies and investors to manage risk, which they may be exposed. The aim is to present and use several methods and models for estimating those measures and to establish which model is most appropriate in certain scenarios.

Towards the validation of adaptive educational hypermedia using CAVIAr

Melia, Mark; Pahl, Claus
Fonte: IEEE Computer Society Publicador: IEEE Computer Society
Tipo: info:eu-repo/semantics/conferenceObject; all_ul_research; ul_published_reviewed
Português
Relevância na Pesquisa
36.52%
peer-reviewed; Migrating from static courseware to Adaptive Educational Hypermedia presents significant risk to the course creator. In this paper we alleviate some of this risk by outlining how the CAVIAr courseware validation framework can be used to validate some pedagogical aspects in Adaptive Educational Hypermedia. To allow for this we present a novel method for interoperability in Adaptive Educational Hypermedia using Model Driven Engineering methodologies. 1 Introduction

An adaptive Bayesian technique for tracking multiple objects

Kumar, P.; Brooks, M.; Van Den Hengel, A.
Fonte: Springer; Germany Publicador: Springer; Germany
Tipo: Conference paper
Publicado em //2007 Português
Relevância na Pesquisa
16.1%
Robust tracking of objects in video is a key challenge in computer vision with applications in automated surveillance, video indexing, human-computer-interaction, gesture recognition, traffic monitoring, etc. Many algorithms have been developed for tracking an object in controlled environments. However, they are susceptible to failure when the challenge is to track multiple objects that undergo appearance change to due to factors such as variation in illumination and object pose. In this paper we present a tracker based on Bayesian estimation, which is relatively robust to object appearance change, and can track multiple targets simultaneously in real time. The object model for computing the likelihood function is incrementally updated and uses background-foreground segmentation information to ameliorate the problem of drift associated with object model update schemes. We demonstrate the efficacy of the proposed method by tracking objects in image sequences from the CAVIAR dataset.; Pankaj Kumar, Michael J. Brooks and Anton van den Hengel; The original publication can be found at www.springerlink.com

Adaptive multiple object tracking using colour and segmentation cues

Kumar, P.; Brooks, M.; Dick, A.
Fonte: Springer; Germany Publicador: Springer; Germany
Tipo: Conference paper
Publicado em //2007 Português
Relevância na Pesquisa
16.1%
We consider the problem of reliably tracking multiple objects in video, such as people moving through a shopping mall or airport. In order to mitigate difficulties arising as a result of object occlusions, mergers and changes in appearance, we adopt an integrative approach in which multiple cues are exploited. Object tracking is formulated as a Bayesian parameter estimation problem. The object model used in computing the likelihood function is incrementally updated. Key to the approach is the use of a background subtraction process to deliver foreground segmentations. This enables the object colour model to be constructed using weights derived from a distance transform operating over foreground regions. Results from foreground segmentation are also used to gain improved localisation of the object within a particle filter framework. We demonstrate the effectiveness of the approach by tracking multiple objects through videos obtained from the CAVIAR dataset.; Pankaj Kumar, Michael J. Brooks and Anthony Dick; The original publication can be found at www.springerlink.com

Index-exciting CAViaR: A new empirical time-varying risk model

Huang, Dashan; Yu, Baimin; Lu, Zudi; Fabozzi, Frank J.; Focardi, Sergio; Fukushima, M.
Fonte: M I T Press Publicador: M I T Press
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
Relevância na Pesquisa
16.03%
Dashan Huang, Baimin Yu, Zudi Lu, Frank J. Fabozzi, Sergio Focardi, and Masao Fukushima

Rastreamento de objetos em vídeos e separação em classes; Tracking of objects in videos and separation in classes

Greice Martins Freitas
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 11/06/2010 Português
Relevância na Pesquisa
16.42%
A crescente utilização de câmeras de vídeo para o monitoramento de ambientes, auxiliando no controle de entrada, saída e trânsito de indivíduos ou veículos tem aumentado a busca por sistemas visando a automatização do processo de monitoramento por vídeos. Como requisitos para estes sistemas identificam-se o tratamento da entrada e saída de objetos na cena, variações na forma e movimentação dos alvos seguidos, interações entre os alvos como encontros e separações, variações na iluminação da cena e o tratamento de ruídos presentes no vídeo. O presente trabalho analisa e avalia as principais etapas de um sistema de rastreamento de múltiplos objetos através de uma câmera de vídeo fixa e propõe um sistema de rastreamento baseado em sistemas encontrados na literatura. O sistema proposto é composto de três fases: identificação do foreground através de técnicas de subtração de fundo; associação de objetos quadro a quadro através de métricas de cor, área e posição do centróide - com o auxílio da aplicação do filtro de Kalman - e, finalmente, classificação dos objetos a cada quadro segundo um sistema de gerenciamento de objetos. Com o objetivo de verificar a eficiência do sistema de rastreamento proposto...

Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model

Ata-ur-Rehman; Naqvi, Syed Mohsen; Mihaylova, Lyudmila; Chambers, Jonathon
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/11/2015 Português
Relevância na Pesquisa
26.1%
This paper considers the problem of multiple human target tracking in a sequence of video data. A solution is proposed which is able to deal with the challenges of a varying number of targets, interactions and when every target gives rise to multiple measurements. The developed novel algorithm comprises variational Bayesian clustering combined with a social force model, integrated within a particle filter with an enhanced prediction step. It performs measurement-to-target association by automatically detecting the measurement relevance. The performance of the developed algorithm is evaluated over several sequences from publicly available data sets: AV16.3, CAVIAR and PETS2006, which demonstrates that the proposed algorithm successfully initializes and tracks a variable number of targets in the presence of complex occlusions. A comparison with state-of-the-art techniques due to Khan et al., Laet et al. and Czyz et al. shows improved tracking performance.; Comment: 19 pages, 14 figures

Comparison of Value-at-Risk models: the MCS package

Bernardi, Mauro; Catania, Leopoldo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/02/2015 Português
Relevância na Pesquisa
16.13%
This paper compares the Value--at--Risk (VaR) forecasts delivered by alternative model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The direct VaR estimate provided by the Conditional Autoregressive Value--at--Risk (CAViaR) models of Eengle and Manganelli (2004) are compared to those obtained by the popular Autoregressive Conditional Heteroskedasticity (ARCH) models of Engle (1982) and to the recently introduced Generalised Autoregressive Score (GAS) models of Creal et al. (2013) and Harvey (2013). The Hansen's procedure consists on a sequence of tests which permits to construct a set of "superior" models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. Our empirical results, suggest that, after the Global Financial Crisis (GFC) of 2007-2008, highly non-linear volatility models deliver better VaR forecasts for the European countries as opposed to other regions. The R package MCS is introduced for performing the model comparisons whose main features are discussed throughout the paper.; Comment: 25 pages. arXiv admin note: substantial text overlap with arXiv:1410.8504

MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos

Reddy, Vikas; Sanderson, Conrad; Sanin, Andres; Lovell, Brian C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/06/2014 Português
Relevância na Pesquisa
16.03%
Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. In this paper, we propose a method capable of robustly estimating the background and detecting regions of interest in such environments. In particular, we propose to extend the background initialisation component of a recent patch-based foreground detection algorithm with an elaborate technique based on Markov Random Fields, where the optimal labelling solution is computed using iterated conditional modes. Rather than relying purely on local temporal statistics, the proposed technique takes into account the spatial continuity of the entire background. Experiments with several tracking algorithms on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in object tracking accuracy, when compared to methods based on Gaussian mixture models and feature histograms.; Comment: arXiv admin note: substantial text overlap with arXiv:1303.2465

Histogram-PMHT for extended targets and target groups in images

Wieneke, M.; Davey, S.
Fonte: IEEE Publicador: IEEE
Tipo: Artigo de Revista Científica
Publicado em //2014 Português
Relevância na Pesquisa
16.03%
This article deals with the integration of random matrices into the Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT), a parametric track-before-detect method that locates targets in imagery by fitting a mixture of probability densities. The random matrices are used to describe the unknown physical extent of targets in the sensor image, a parameter that can change with time depending on the target orientation and sensor geometry. The track management model is extended to allow merging and splitting targets. The performance of the algorithm is quantified through simulations and using a benchmark people surveillance data set from the CAVIAR project.; Monika Wieneke, Sam Davey

Less is more: rarity trumps quality in luxury markets

Agnes Gault; Yves Meinard; Franck Courchamp
Fonte: Nature Preceedings Publicador: Nature Preceedings
Tipo: Manuscript
Português
Relevância na Pesquisa
16.52%
The international market for luxury goods has almost doubled since 1990, with a worldwide increase of 10% annually. This trade is fuelled by a great deal of legally and illegally exploited wildlife species, putting enormous pressure on many of them, with potentially irreversible consequences. The dramatic decline of sturgeon populations exploited for their caviar, is a good example: all 27 species are threatened and the most coveted are on the verge of extinction. We aim to identify the mechanism responsible for the continued overexploitation of sturgeon species, despite caviar's ever-increasing price and the imminent loss of these species. Here, we demonstrate consumer preference for rarity over intrinsic quality: customers tasting two caviar samples more often chose the one they thought was rare, although both were identical. In a game theory model, we demonstrate that the most rational behaviour is to rush to consume rare species, even though this precipitates their extinction. We conclude that the human predisposition to place exaggerated value on rarity probably drives the entire market for luxury goods from reptile skins to exotic woods. Our findings suggest that allowing low levels of legal trade will exacerbate the arbitrary value of rare species and thereby stimulate demand. Only a total ban on trade from the wild (with very strict controls) combined with strong support for farmed equivalents will protect rare species.