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.

The Rural Energy Enterprise Development (REED) initiative provides assistance to energy-related enterprises to prepare them for growth and to make eventual investments by mainstream financial partners less risky. This study assesses the non-financial returns of a number of REED-type enterprises and provides guidance for the selection and ongoing evaluation of these enterprises within the context of development interventions. This study concludes that desired development outcomes should provide the basis for programme objectives against which non-financial returns can be measured. However, qualitative information is also necessary, as the context within which an enterprise operates largely defines the importance of these returns. Further work to improve the understanding of this context is necessary prior to the development of a formalised monitoring programme. Limitations of the assessment process should be recorded and addressed through the ongoing review of the programme, other monitoring efforts and further research.

The Rural Energy Enterprise Development (REED) initiative provides assistance to energy-related enterprises to prepare them for growth and to make eventual investments by mainstream financial partners less risky. This study assesses the non-financial returns of a number of REED-type enterprises and provides guidance for the selection and ongoing evaluation of these enterprises within the context of development interventions. This study concludes that desired development outcomes should provide the basis for programme objectives against which non-financial returns can be measured. However, qualitative information is also necessary, as the context within which an enterprise operates largely defines the importance of these returns. Further work to improve the understanding of this context is necessary prior to the development of a formalised monitoring programme. Limitations of the assessment process should be recorded and addressed through the ongoing review of the programme, other monitoring efforts and further research.

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2018-11-21T11:51:02Z No. of bitstreams: 1 Augusto Felippe Caramico dos Santos.pdf: 1308243 bytes, checksum: 4a9f444e0b4ed4d812a0cacfb653b505 (MD5) / Made available in DSpace on 2018-11-21T11:51:02Z (GMT). No. of bitstreams: 1 Augusto Felippe Caramico dos Santos.pdf: 1308243 bytes, checksum: 4a9f444e0b4ed4d812a0cacfb653b505 (MD5) Previous issue date: 2018-10-11 / Fundação São Paulo - FUNDASP / This thesis aims to investigate if traders have better decision management under stress, obtaining greater financial results. The Iowa Gambling Task was used to verify the hypothesis of research and the influence of the somatic markers in decision making, integrated to the theories of Behavioral Finance and Emotional Finance. The results indicated trading floor operators has better quality of decision making, faster learning, greater adaptability and superior financial result compared to control group. Among the subgroups of operators, there was a significant difference in the learning process and financial result obtained, demonstrating better ability to recognize patterns and higher financial results of the floor traders when compared to the group of electronic desk traders / Esta tese tem como objetivo investigar se traders possuem melhor qualidade decisória sob estresse, obtendo maiores resultados financeiros. Foi utilizada a metodologia do Iowa Gambling Task como meio de verificar as hipóteses de pesquisa e a influência dos marcadores somáticos na tomada de decisão dos indivíduos, integrada às teorias das Finanças Comportamentais e das Finanças Emocionais. Os resultados indicaram que os operadores de pregão apresentaram melhor qualidade de tomada de decisão, aprendizado mais rápido, maior adaptabilidade e resultado financeiro superior ante o grupo de controle. Entre os subgrupos de operadores foi verificada diferença significativa no processo de aprendizagem e resultado financeiro obtido, demonstrando melhor capacidade no reconhecimento de padrões e maiores resultados financeiros dos operadores de mesa quando comparados ao grupo de operadores viva-voz

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

Ce mémoire s’intéresse aux modèles de Markov à variables latentes, une famille de modèles dans laquelle une chaîne de Markov latente régit le comportement d’un processus stochastique observable à travers duquel transparaît une version bruitée de la chaîne cachée. Pouvant être vus comme une généralisation naturelle des modèles de mélange, ces processus stochastiques bivariés ont entre autres démontré leur faculté à capter les dynamiques variables de maintes séries chronologiques et, plus spécifiquement en finance, à reproduire la plupart des faits stylisés des rendements financiers. Nous nous intéressons en particulier aux chaînes de Markov à temps discret et à espace d’états fini, avec l’objectif d’étudier l’apport de leurs reformulations hiérarchiques et de la relaxation de l’hypothèse d’homogénéité de la matrice de transition à la qualité de l’ajustement aux données et des prévisions, ainsi qu’à la reproduction des faits stylisés. Nous présentons à cet effet deux structures hiérarchiques, la première permettant une nouvelle interprétation des relations entre les états de la chaîne, et la seconde permettant de surcroît une plus grande parcimonie dans la paramétrisation de la matrice de transition. Nous nous intéressons de plus à trois extensions non-homogènes, dont deux dépendent de variables observables et une dépend d’une autre variable latente. Nous analysons pour ces modèles la qualité de l’ajustement aux données et des prévisions sur la série des log-rendements du S&P 500 et du taux de change Canada-États-Unis (CADUSD). Nous illustrons de plus la capacité des modèles à reproduire les faits stylisés, et présentons une interprétation des paramètres estimés pour les modèles hiérarchiques et non-homogènes. Les résultats obtenus semblent en général confirmer l’apport potentiel de structures hiérarchiques et des modèles non-homogènes. Ces résultats semblent en particulier suggérer que l’incorporation de dynamiques non-homogènes aux modèles hiérarchiques permette de reproduire plus fidèlement les faits stylisés—même la lente décroissance de l’autocorrélation des rendements centrés en valeur absolue et au carré—et d’améliorer la qualité des prévisions obtenues, tout en conservant la possibilité d’interpréter les paramètres estimés. / This master’s thesis is centered on the Hidden Markov Models, a family of models in which an unobserved Markov chain dictactes the behaviour of an observable stochastic process through which a noisy version of the latent chain is observed. These bivariate stochastic processes that can be seen as a natural generalization of mixture models have shown their ability to capture the varying dynamics of many time series and, more specifically in finance, to reproduce the stylized facts of financial returns. In particular, we are interested in discrete-time Markov chains with finite state spaces, with the objective of studying the contribution of their hierarchical formulations and the relaxation of the homogeneity hypothesis for the transition matrix to the quality of the fit and predictions, as well as the capacity to reproduce the stylized facts. We therefore present two hierarchical structures, the first allowing for new interpretations of the relationships between states of the chain, and the second allowing for a more parsimonious parameterization of the transition matrix. We also present three non-homogeneous models, two of which have transition probabilities dependent on observed explanatory variables, and the third in which the probabilities depend on another latent variable. We first analyze the goodness of fit and the predictive power of our models on the series of log returns of the S&P 500 and the exchange rate between canadian and american currencies (CADUSD). We also illustrate their capacity to reproduce the stylized facts, and present interpretations of the estimated parameters for the hierarchical and non-homogeneous models. In general, our results seem to confirm the contribution of hierarchical and non-homogeneous models to these measures of performance. In particular, these results seem to suggest that the incorporation of non-homogeneous dynamics to a hierarchical structure may allow for a more faithful reproduction of the stylized facts—even the slow decay of the autocorrelation functions of squared and absolute returns—and better predictive power, while still allowing for the interpretation of the estimated parameters.