Submitted by Marcos Souza Ferreira (mferreira@poli.ufrj.br) on 2016-07-27T13:57:40Z No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET.pdf: 405486 bytes, checksum: 54cd37d39ac7269f0a808b0e73addedb (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Marcos, boa tarde Por gentileza, verificar a numeração das páginas. Está correto, elas aparecerem a partir da Introdução, porém, não deve se iniciar pela página 1. Por exemplo, se a Introdução é na página 11, incluir a partir da página 11. Em seguida submeter novamente o arquivo. Att on 2016-07-28T15:38:39Z (GMT) / Submitted by Marcos Souza Ferreira (mferreira@poli.ufrj.br) on 2016-07-28T16:49:04Z No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2016-07-28T16:58:52Z (GMT) No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) / Made available in DSpace on 2016-07-28T17:28:22Z (GMT). No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) Previous issue date: 2016-06-28 / Considering the importance of the proper detection of bubbles in financial markets for policymakers and market agents, we used two techniques described in Diba and Grossman (1988b) and in Phillips, Shi, and Yu (2015) to detect periods of exuberance in the recent history of the Brazillian stock market. First, a simple cointegration test is applied. Secondly, we conducted several augmented, right-tailed Dickey-Fuller tests on rolling windows of data to determine the point in which there’s a structural break and the series loses its stationarity.

abstract: As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

This diploma thesis deals with the analysis of the birth rate and fertility rate in dependence on the economic conditions of the Czech Republic. The aim of this thesis is to analyze relations between selected socio-economic indicators and total fertility rate on the basis of available data and to evaluate changes related to birth and fertility in the Czech Republic, which occurred in the period 1993-2015. The thesis is divided into two main parts - theoretical and analytical. In the theoretical part are described the basic methods of calculating the characteristics for the analysis of the level of birthrate and fertility, as well as the development of selected socio-economic indicators. An important part of the first part of the diploma thesis is specification of used methods within time series. The second part is practically focused on cointegration analysis and subsequent assembly of single-row models from which error correction models were obtained by transformation. These can be used to describe short and long term relationships between time series. Explained variables are aggregate fertility in all assembled models, the explanatory variables are GDP, average gross monthly wage, final consumption expenditure for households, child allowances, parental allowance and household loans per capita.

No description available.

This thesis investigates through simulation why tests of unit root and stationarity occasionally result in different conclusions. The thesis focusses on the KPSS test and the ADF test and both review cases with and without a trend. The goal is to bring additional knowledge of whether one of the tests are more reliable in terms of size and power and when contradictory results occur. The result shows that both KPSS and ADF suffer from low power and size distortion and that the problems persists for the most common time series lengths. Problems particularly arise when the time series contains a trend or is a process with both an autoregressive and a moving average part. There is no clear evidence that one of the tests are superior to the other, it rather depends on sample size, parameter value and type of ARIMA process.

No description available.

Air pollution and meteorological conditions impact COVID-19 mortality rates. This research studied Quito, Ecuador, using Granger causality tests and regression models to investigate the relationship between pollutants, meteorological variables, human mobility, and excess deaths. Results suggested that Mobility as defined by Google Mobility Index, Facebook Isolation Index, in addition to Nitrogen Dioxide, and Sulphur Dioxide significantly impact excess deaths, while Carbon Monoxide and Relative Humidity have mixed results. Measures to reduce Carbon Monoxide emissions and increase humidity levels may mitigate the impact of air pollution on COVID-19 mortality rates. Further research is needed to investigate the impact of pollutants on COVID-19 transmission in other locations. Healthcare decision-makers must monitor and mitigate the impact of pollutants, promote healthy air quality policies, and encourage physical activity in safe environments. They must also consider meteorological conditions and implement measures such as increased ventilation and air conditioning to reduce exposure. Additionally, they must consider human mobility and reduce it to slow the spread of the diseases. Decisionmakers must monitor and track excess deaths during the pandemic to understand the impact of pollutants, meteorological conditions, and human mobility on human health. Public education is critical to raising awareness of air quality and its impact on health. Encouraging individuals to reduce their exposure to pollutants and meteorological conditions can play a critical role in mitigating the impact of air pollution on respiratory health during the pandemic.

No description available.

Investing in the stock market offers opportunities for wealth accumulation through variousstrategies. This thesis explores the pairs trading strategy with dual-class stocks differingonly in voting rights, aiming to reduce portfolio risk and outperform the market bench-mark. Using data from the Swedish Large Cap index (2003-2023), the study benchmarksthe strategy’s performance against the OMXSPI index, assessing total return, CAGR andthe Sharpe ratio for three different strategies. Depending on the predefined thresholds ofthe trading strategy, the study concludes that pairs trading can surpass a buy-and-holdapproach, showing the effectiveness of a market neutral trading strategy.

One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.