Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

Neste trabalho, visamos mostrar as características usuais dos dados de alta frequência, bem como utilizar modelagem não paramétrica para estimar a variância/volatilidade para esses dados. Após uma revisão sobre microestrutura de mercado, sazonalidade intradiária, variação quadrática e saltos, utilizamos os dados da PETR4 para estimar a variância realizada e variação bipotente. Determinadas essas séries, testamos se há saltos nas mesmas. Em seguida, analisamos o impacto que a microestrutura de mercado e a sazonalidade intradiária causam na detecção dos saltos. Concluímos que, enquanto a presença de microestrutura aponta para um número de saltos menor que o esperado, a sazonalidade intradiária aponta para o lado contrário, ou seja, ela causa um viés para detectar mais saltos, dada a estrutura típica da curva de volatilidade ao longo do dia em formato de J invertido, causando mais saltos incorretamente detectados no período mais volátil do dia (que corresponde a abertura da bolsa de valores). / In this work, we aim to show the usual characteristics of high-frequency data and the estimation of variance/volatility for this kind of data using nonparametric models. After reviewing concepts about market microstructure, intraday seasonality, quadratic variation and jumps, we use PETR4 data to estimate realized variance and bipower variation. With these series determined, we test for jumps. Then, we analyze the impact that market microstructure and intraday seasonality causes in jump detection. We conclude that while microstructure noise indicates fewer jumps than the ideal amount, intraday seasonality goes in the opposite direction, i.e., it detects more jumps than it should, since the typical inverted-J-shaped intraday volatility pattern tends to incorrectly detect more jumps at the most volatile period (which is when stock markets start negotiations).

This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models. The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons. The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.

L’énergie éolienne, l’une des énergies renouvelables les plus compétitives, est considérée comme une solution qui remédie aux inconvénients de l’énergie fossile. Pour une meilleure gestion et exploitation de cette énergie, des prévisions de sa production s’avèrent nécessaires. Les méthodes de prévisions utilisées dans la littérature permettent uniquement une prévision de la moyenne annuelle de cette production. Certains travaux récents proposent l’utilisation du Théorème Central Limite (TCL), sous des hypothèses non classiques, pour l’estimation de la production annuelle moyenne de l’énergie éolienne ainsi que sa variance pour une seule turbine. Nous proposons dans cette thèse une extension de ces travaux à un parc éolien par relaxation de l’hypothèse de stationnarité la vitesse du vent et la production d’énergie, en supposant que ces dernières sont saisonnières. Sous cette hypothèse la qualité de la prévision annuelle s’améliore considérablement. Nous proposons aussi de prévoir la production d’énergie éolienne au cours des quatre saisons de l’année. L’utilisation du modèle fractal, nous permet de trouver une division ”naturelle” de la série de la vitesse du vent afin d’affiner l’estimation de la production éolienne en détectant les points de ruptures. Dans les deux derniers chapitres, nous donnons des outils statistiques de la détection des points de ruptures et d’estimation des modèles fractals. / The wind energy, one of the most competitive renewable energies, is considered as a solution which remedies the inconveniences of the fossil energy. For a better management and an exploitation of this energy, forecasts of its production turn out to be necessary. The methods of forecasts used in the literature allow only a forecast of the annual mean of this production. Certain recent works propose the use of the Central Limit Theorem (CLT), under not classic hypotheses, for the estimation of the mean annual production of the wind energy as well as its variance for a single turbine. We propose in this thesis, an extension of these works in a wind farm by relaxation of the hypothesis of stationarity the wind speed and the power production, supposing that the latter are seasonal. Under this hypothesis the quality of the annual forecast improves considerably. We also suggest planning the wind power production during four seasons of the year. The use of the fractal model, allows us to find a "natural" division of the series of the wind speed to refine the estimation of the wind production by detecting abrupt change points. Statistical tools of the change points detection and the estimation of fractal models are presented in the last two chapters.

This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. <p><p>The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.<p><p>The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.<p><p>The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished