Modeling Financial Volatility: Extreme Observations, Nonlinearities and Nonstationarities

This paper presents a selective survey of volatility topics, with emphasis on the measurement of volatility and a discussion of some of the most important time series models commonly employed in its modelling. In particular, the paper details the long memory characteristics of volatility, and discusses its possible origins and impact on option pricing. To conclude, the paper discusses statistical tools that discriminate between nonlinearity and nonstationarity.long memory; nonstationarity; nonlinearity; option pricing, volatility

Detection of abnormalities in ECG using Deep Learning

A significant part of healthcare is focused on the information that the physiological signals offer about the health state of an individual. The Electrocardiogram (ECG) cyclic behaviour gives insight on a subject’s emotional, behavioral and cardiovascular state. These signals often present abnormal events that affects their analysis. Two examples are the noise, that occurs during the acquisition, and symptomatic patterns, that are produced by pathologies. This thesis proposes a Deep Neural Networks framework that learns the normal behaviour of an ECG while detecting abnormal events, tested in two different settings: detection of different types of noise, and; symptomatic events caused by different pathologies. Two algorithms were developed for noise detection, using an autoencoder and Convolutional Neural Networks (CNN), reaching accuracies of 98,18% for the binary class model and 70,74% for the multi-class model, which is able to discern between base wandering, muscle artifact and electrode motion noise. As for the arrhythmia detection algorithm was developed using an autoencoder and Recurrent Neural Networks with Gated Recurrent Units (GRU) architecture. With an accuracy of 56,85% and an average sensitivity of 61.13%, compared to an average sensitivity of 75.22% for a 12 class model developed by Hannun et al. The model detects 7 classes: normal sinus rhythm, paced rhythm, ventricular bigeminy, sinus bradycardia, atrial fibrillation, atrial flutter and pre-excitation. It was concluded that the process of learning the machine learned features of the normal ECG signal, currently sacrifices the accuracy for higher generalization. It performs better at discriminating the presence of abnormal events in ECG than classifying different types of events. In the future, these algorithms could represent a huge contribution in signal acquisition for wearables and the study of pathologies visible in not only in ECG, but also EMG and respiratory signals, especially applied to active learning

Visual Aesthetics in Digital Games: A Comparative Analysis Between Photorealism and Stylized Graphicsgraphics

This dissertation starts from the assumption that every digital game has some kind of visual display. Based on that, it investigates photorealistic and stylized graphics, two popular visual styles in digital games, in order to comprehend the process of creating a prototype that incorporates those styles, as well as the technological artistic challenges of implementing each style in a solo development scenario, with the goal of assisting in the practice of designing this type of content. A literature review on digital game appearance and the development of both photorealistic and stylized styles was conducted to ground the development of a prototype. The result of the prototype creation is documented, so its findings can lead to the expansion of knowledge that can be used in practice and can inform practitioners and other designers

On the power of underdifferencing and over differencing tests against nearly nonstationary alternatives

The choice of the appropriate degree of integration is a very important question in ARIMA time series modeling. This choice is particularly difficult in the presence of either a nearly nonstationary autoregression or a fractionally integrated process. Via a Monte Carlo study we assess the size and power of MA, AR and spectral estimation tests in the presence of fractionally integrated, nearly nonstationary, and nearly noninvertible processes.info:eu-repo/semantics/publishedVersio

Competitive Advantages as a Complete Mediator Variable in Strategic Resources, Dynamic Capabilities and Performance Relations in the Car Sales Sector

Taking the resource-based view –RBV- and the dynamic capability view –DCV- as an orientation, the main aim of this study is to develop the mediator role that competitive advantages play in the relations between strategic resources, dynamic capabilities and performance. The study takes place in a dynamic and changing sector: the sale of new cars in Portugal. The results show that (a) achieving competitive advantages, which are decisive for business results, depends on the available strategic resources and the generating of dynamic capabilities, (b) in dynamic and changing sectors strategic resources are essential to generate dynamic capabilities, (c) firms must center their attention on, more than results, the generating of sustainable competitive advantages as these act as a mediator variable of the effect of strategic resources and dynamic capabilities on performance. The data scrutiny uses structural equation modeling (SEM) through PLS as the statistical instrument. The sample comprises 89 firms which sell new cars in Portugal

Constant mean curvature one surfaces in hyperbolic 3-space using the Bianchi-Calò method

In this note we present a method for constructing constant mean curvature on surfaces in hyperbolic 3-space in terms of holomorphic data first introduced in Bianchi's Lezioni di Geometria Differenziale of 1927, therefore predating by many years the modern approaches due to Bryant, Small and others. Besides its obvious historical interest, this note aims to complement Bianchi's analysis by deriving explicit formulae for CMC-1 surfaces and comparing the various approaches encountered in the literature. ___________________________________________________________________________________________________________________ RESUMONesta nota apresentaremos um método para construir superfícies de curvatura média constante um no 3-espaço hiperbólico, a partir de funções holomorfas. Tal método foi introduzido nas Lezioni di Geometria Differenziale de Bianchi em 1927, antecedendo, portanto, em muitos anos, os pontos de vista mais modernos de Bryant, Small e outros. Além do seu óbvio interesse histórico, o objetivo da nota é complementar a análise de Bianchi, obtendo fórmulas explícitas para as superfícies de curvatura média constante um, e comparar os vários pontos de vista encontrados na literatura

Data Labeling tools for Computer Vision: a Review

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceLarge volumes of labeled data are required to train Machine Learning models in order to solve today’s computer vision challenges. The recent exacerbated hype and investment in Data Labeling tools and services has led to many ad-hoc labeling tools. In this review, a detailed comparison between a selection of data labeling tools is framed to ensure the best software choice to holistically optimize the data labeling process in a Computer Vision problem. This analysis is built on multiple domains of features and functionalities related to Computer Vision, Natural Language Processing, Automation, and Quality Assurance, enabling its application to the most prevalent data labeling use cases across the scientific community and global market