A evolução da economia brasileira durante os primeiros anos do século XXI é examinada através de um modelo microfundamentado de uma pequena economia aberta, permitindo mudanças no comportamento do Banco Central do Brasil, no parâmetro de rigidez nominal e na volatilidade dos choques estruturais. Mesmo os resultados não sendo conclusivos a respeito da presença de mudanças de regime durante o período analisado, encontramos evidências de troca de regime no âmbito da política monetária, passando em 2003 de um regime Dove para um regime Hawk, assim como evidências de choques externos mais voláteis durante períodos de incerteza. Na sequência, deixamos de lado a estimação empírica e derivamos regras de política monetária ótima para o caso brasileiro. É possível encontrar uma regra ótima capaz de estabilizar a inflação, o produto e a taxa de câmbio, mantendo uma taxa de juros estável. Por fim, o modelo trás uma discussão interessante sobre a dinâmica de determinadas variáveis macroeconômicas: uma moeda mais estável implica em uma taxa de juros mais volátil, e vice versa; um maior controle sobre a taxa de juros e/ou sobre a taxa de câmbio parece gerar uma maior instabilidade do produto e da inflação. / The evolution of the Brazilian economy during the first years of this century is examined through the lens of a micro-founded small open economy model that allows for changes in the behaviour of the Central Bank of Brazil, in the nominal price rigidity and in the volatility of structural shocks. Although the results are not conclusive about the presence of regime changes during the analysed sample, we find evidences in favour of shifts in the monetary policy stance, moving from a Dove to a Hawk regime in 2003, as well as evidences of more volatile external shocks during uncertainty periods. We further move away from the empirical estimation and derive optimal monetary policy rules for Brazil. It is possible to find an optimal rule that is successful in stabilizing inflation, output and exchange rates, whilst keeping interest rates stable. Finally, the model offers interesting insights about the standard deviation dynamics of macroeconomic variables: a more stable currency implies a more volatile interest rate and vice versa, and a higher control over interest rates and/or exchange rates seem to produce output and inflation instability.

O objetivo geral deste trabalho é testar se a informação contida em dados de microestrutura de mercado contribui para uma melhor explicação do comportamento dos preços dos títulos negociados na BMF&BOVESPA. O primeiro ensaio testa a hipótese de que o volume gera informação sobre o movimento dos preços quando os traders possuem informações diferentes sobre o comportamento dessa variável. Em uma abordagem de microestrutura de mercado, os traders não informados observam apenas as estatísticas diárias de preço e volume, e os traders informados conhecem o comportamento dos preços e volumes tick-by-tick. Aplicando o algoritmo do tick-test, os traders informados observam o volume de ordens de compras e vendas diárias. O teste empírico é feito utilizando modelos markov-switching, e os resultados revelam que os traders informados apresentam estimações melhores. No segundo ensaio, buscam-se evidências de que traders informados preferem atuar no mercado de opções pela possibilidade de alavancar seus ganhos. O objetivo é identificar se o volume no mercado de opções revela o comportamento dos preços das ações. Os resultados obtidos, utilizando opções de PETR4 e VALE5, corroboram a hipótese que o volume no mercado de opções não transmite informação sobre o comportamento do preço da ação. O terceiro ensaio utiliza dados tick-by-tick para estimar a probabilidade de informação privilegiada (PIN) para ações do IBRX. O PIN é uma proxy para informação privada e é incorporado ao método de Fama e French (1993) para separar os portfolios e explicar seus retornos. A combinação do PIN com as variáveis valor de mercado e índice book-to-market possibilita, para alguns portfolios, aumento do retorno e diminuição do risco. A significância das variáveis na explicação dos retornos é testada utilizando modelos de markov-switching. Os resultados permitem concluir que o PIN é um fator importante na explicação dos retornos dos portfolios. / The general objective is to test whether the information contained in data of microstructure market contributes to a better explanation of the behavior of stock prices negotiated in BMF&BOVESPA. The first essay tests the hypothesis that volume generates information on price movements when traders have different information on the behavior of this variable. In a model of market microstructure, non-informed traders observe only the daily statistics on price and volume, while informed traders know the behavior of tick-by-tick prices and volumes. Applying the tick-test algorithm, informed traders observe the volume of buy orders and daily sales. The empirical test is carried out by using markov-switching models, and the results reveal that informed traders show better estimates. In the second essay, we look for evidence that informed traders prefer to trade in options market due to its possibility of leveraging their revenue. The objective is to identify whether volume in the options market is revealing of the behavior of stock prices. The results obtained using options from PETR4 and VALE5 concur to the hypothesis that volume in options market does not transmit information on the behavior of stock price. The third essay makes use of tick-by-tick data to estimate the probability of privileged information (PIN) to IBRX stocks. PIN is a proxy to privilege information and is incorporated to Fama French (1993) method to separate portfolios and explain their returns. The combination of PIN with variables of market value and book-to-market index allows some portfolios to increase return and diminish risk. The significance of these variables in explaining returns is tested using markov-switching methods. The results demonstrate that PIN is an important factor in explaining portfolio returns.

A evolução da economia brasileira durante os primeiros anos do século XXI é examinada através de um modelo microfundamentado de uma pequena economia aberta, permitindo mudanças no comportamento do Banco Central do Brasil, no parâmetro de rigidez nominal e na volatilidade dos choques estruturais. Mesmo os resultados não sendo conclusivos a respeito da presença de mudanças de regime durante o período analisado, encontramos evidências de troca de regime no âmbito da política monetária, passando em 2003 de um regime Dove para um regime Hawk, assim como evidências de choques externos mais voláteis durante períodos de incerteza. Na sequência, deixamos de lado a estimação empírica e derivamos regras de política monetária ótima para o caso brasileiro. É possível encontrar uma regra ótima capaz de estabilizar a inflação, o produto e a taxa de câmbio, mantendo uma taxa de juros estável. Por fim, o modelo trás uma discussão interessante sobre a dinâmica de determinadas variáveis macroeconômicas: uma moeda mais estável implica em uma taxa de juros mais volátil, e vice versa; um maior controle sobre a taxa de juros e/ou sobre a taxa de câmbio parece gerar uma maior instabilidade do produto e da inflação. / The evolution of the Brazilian economy during the first years of this century is examined through the lens of a micro-founded small open economy model that allows for changes in the behaviour of the Central Bank of Brazil, in the nominal price rigidity and in the volatility of structural shocks. Although the results are not conclusive about the presence of regime changes during the analysed sample, we find evidences in favour of shifts in the monetary policy stance, moving from a Dove to a Hawk regime in 2003, as well as evidences of more volatile external shocks during uncertainty periods. We further move away from the empirical estimation and derive optimal monetary policy rules for Brazil. It is possible to find an optimal rule that is successful in stabilizing inflation, output and exchange rates, whilst keeping interest rates stable. Finally, the model offers interesting insights about the standard deviation dynamics of macroeconomic variables: a more stable currency implies a more volatile interest rate and vice versa, and a higher control over interest rates and/or exchange rates seem to produce output and inflation instability.

The core idea of this thesis is to reduce the workload of manual inspection when the performance analysis of an updated software is required. The Central Process- ing Unit (CPU) utilization, which is one of the essential factors for evaluating the performance, is analyzed. The purpose of this work is to apply machine learning techniques that are suitable for detecting the state of the CPU utilization and any changes in the test environment that affects the CPU utilization. The detection re- lies on a Markov switching model to identify structural changes, which are assumed to follow an unobserved Markov chain, in the time series data. A historical behav- ior of the data can be described by a first-order autoregression. Then, the Markov switching model becomes a Markov switching autoregressive model. Another ap- proach based on a non-parametric analysis, a distribution-free method that requires fewer assumptions, called an E-divisive method, is proposed. This method uses a hi- erarchical clustering algorithm to detect multiple change point locations in the time series data. As the data used in this analysis does not contain any ground truth, the evaluation of the methods is analyzed by generating simulated datasets with known states. Besides, these simulated datasets are used for studying and compar- ing between the Markov switching autoregressive model and the E-divisive method. Results show that the former method is preferable because of its better performance in detecting changes. Some information about the state of the CPU utilization are also obtained from performing the Markov switching model. The E-divisive method is proved to have less power in detecting changes and has a higher rate of missed detections. The results from applying the Markov switching autoregressive model to the real data are presented with interpretations and discussions.

O objetivo geral deste trabalho é testar se a informação contida em dados de microestrutura de mercado contribui para uma melhor explicação do comportamento dos preços dos títulos negociados na BMF&BOVESPA. O primeiro ensaio testa a hipótese de que o volume gera informação sobre o movimento dos preços quando os traders possuem informações diferentes sobre o comportamento dessa variável. Em uma abordagem de microestrutura de mercado, os traders não informados observam apenas as estatísticas diárias de preço e volume, e os traders informados conhecem o comportamento dos preços e volumes tick-by-tick. Aplicando o algoritmo do tick-test, os traders informados observam o volume de ordens de compras e vendas diárias. O teste empírico é feito utilizando modelos markov-switching, e os resultados revelam que os traders informados apresentam estimações melhores. No segundo ensaio, buscam-se evidências de que traders informados preferem atuar no mercado de opções pela possibilidade de alavancar seus ganhos. O objetivo é identificar se o volume no mercado de opções revela o comportamento dos preços das ações. Os resultados obtidos, utilizando opções de PETR4 e VALE5, corroboram a hipótese que o volume no mercado de opções não transmite informação sobre o comportamento do preço da ação. O terceiro ensaio utiliza dados tick-by-tick para estimar a probabilidade de informação privilegiada (PIN) para ações do IBRX. O PIN é uma proxy para informação privada e é incorporado ao método de Fama e French (1993) para separar os portfolios e explicar seus retornos. A combinação do PIN com as variáveis valor de mercado e índice book-to-market possibilita, para alguns portfolios, aumento do retorno e diminuição do risco. A significância das variáveis na explicação dos retornos é testada utilizando modelos de markov-switching. Os resultados permitem concluir que o PIN é um fator importante na explicação dos retornos dos portfolios. / The general objective is to test whether the information contained in data of microstructure market contributes to a better explanation of the behavior of stock prices negotiated in BMF&BOVESPA. The first essay tests the hypothesis that volume generates information on price movements when traders have different information on the behavior of this variable. In a model of market microstructure, non-informed traders observe only the daily statistics on price and volume, while informed traders know the behavior of tick-by-tick prices and volumes. Applying the tick-test algorithm, informed traders observe the volume of buy orders and daily sales. The empirical test is carried out by using markov-switching models, and the results reveal that informed traders show better estimates. In the second essay, we look for evidence that informed traders prefer to trade in options market due to its possibility of leveraging their revenue. The objective is to identify whether volume in the options market is revealing of the behavior of stock prices. The results obtained using options from PETR4 and VALE5 concur to the hypothesis that volume in options market does not transmit information on the behavior of stock price. The third essay makes use of tick-by-tick data to estimate the probability of privileged information (PIN) to IBRX stocks. PIN is a proxy to privilege information and is incorporated to Fama French (1993) method to separate portfolios and explain their returns. The combination of PIN with variables of market value and book-to-market index allows some portfolios to increase return and diminish risk. The significance of these variables in explaining returns is tested using markov-switching methods. The results demonstrate that PIN is an important factor in explaining portfolio returns.

La complexité des marchés financiers et la recrudescence des crises particulièrement sévères contribuent à l'évolution et à la remise en cause de modèles économétriques dits standards dans l'explication et la prévision des dynamiques financières. L'alerte donnée conjointement par les responsables prudentiels et les chercheurs vise à encourager le développement de modèles plus complexes, non linéaires et largement inspirés d'autres disciplines. Nous soutenons dans cette thèse l'idée qu'une approche chaos-stochastique des chroniques financières est susceptible de conduire à de meilleurs résultats. La pertinence de cette association est évaluée pour le risque de marché dans deux cadres d'analyse distincts. Nous montrons tout l'intérêt d'une synthèse des modèles chaotiques et des spécifications GARCH avec ou sans changements de régimes markoviens (MRS) pour la modélisation et la prévision de la Value-at-Risk des indices boursiers de la zone euro. Il ressort de cette étude de meilleurs résultats des modèles chaos-stochastiques et dans le cas des spécifications MRS-GARCH, une meilleure adéquation du modèle chaotique de Lasota(1977) pour les indices de l'Europe du Sud, particulièrement plus volatiles que ceux de l'Europe du Nord pour lesquels nous recommandons le modèle de Mackey-Glass(1977). Cette combinaison permet, dans un cadre bivarié, de mieux appréhender les liens qui existent entre les différentes places boursières de la zone euro. Nous introduisons deux nouvelles spécifications qui intègrent les problématiques liées aux ruptures de corrélations : la première permet de distinguer, par une analyse en sous-périodes, les relations d'interdépendance par rapport aux phénomènes de contagion et la seconde propose, dans un cadre unifié, d'intégrer les ruptures de corrélations. Cette double analyse met en évidence le rôle moteur du couple d'indices franco-allemand, l'existence de deux sphères distinctes constituées d'une part par les indices de l'Europe du Nord et d'autre part par les pays de l'Europe du Sud et l'intensification de certaines relations entre indices suite à la crise des dettes souveraines. Nous constatons et insistons sur la pertinence d'un modèle chaotique en moyenne pour rendre compte d'une part de la volatilité attribuée, à tort, aux effets GARCH. / The complexity of financial markets and the resurgence of severe crises contribute to the skepticism and evolution of standard econometric models in the explanation and prediction of financial time series. The warning given jointly by prudential authorities and researchers aims to encourage the development of nonlinear and more complex models inspired by other disciplines. I argue in this thesis that a chaos-stochastic approach of financial dynamics is likely to lead to better results. The relevance of this association is evaluated for market risk in two distinct analytical frameworks. I show the improvements given by a synthesis of chaotic models and GARCH specifications with or without Markov Regime Switching (MRS) for modelling and predicting the Value-at-Risk of 7 mains index of Monetary and Economic Union. It appears, from this study, better results from chaos-stochastic models. In the case of the MRS-GARCH specifications, I find more adequacy of the chaotic model of Lasota (1977) for the indices of Southern Europe, which are especially more volatile than those of Northern Europe for which I recommend the model of Mackey-Glass (1977). This combination allows, in a bivariate framework, to provide information on the relationship between these different indices. I introduce two new specifications that integrate issues related to correlation breakdowns. The first distinguishes, by a sub-periods analysis, the relations of interdependence of contagious relationships. Meanwhile, the second provides, in a unified framework, an integration of correlations breakdowns. These two analyses imply It appears from this double analysis the leading role of the Franco-German duo, the existence of two distinct spheres formed in a part by the Northern European indices and in another part by countries of the Southern Europe, and the intensification of relations between some indices following the sovereign debt crisis. Finally, these results support the relevance of a chaotic model which may account for some volatilities that are, wrongly, attributed to GARCH effects.

In this study a structural model of the South African stock market, the Johannesburg Stock Exchange (JSE), was developed and estimated econometrically. The study has made three important contributions to the literature. Firstly, a structural model of the South African stock market has been developed, which quantifies the relationships between the stock market and macroeconomic variables while analyzing the impact of foreign markets and phenomena such as contagion, policy changes and structural economic changes on the JSE. This will improve the economic agents’ understanding of the functioning of the stock market and potentially assist in forecasting the stock market. Secondly, investors are generally assumed to be risk and/or loss averse. This study explains how this risk and/or loss aversion of investors can cause asymmetry in stock prices and the study evaluates different types of stock market asymmetry with advanced econometric techniques such as the threshold cointegration test of Siklos and Enders (2001) and a Markov switching regime model. The Markov switching regime model is used to model the South African business cycle and to construct an indicator for the state of the business cycle, which is in turn used to introduce cyclical asymmetry in the stock market model. The Markov switching regime model is in itself a substantial contribution to the literature since no Markov switching regime model has been estimated for the South African business cycle yet. Apart from being used to capture cyclical asymmetry in the stock market, the Markov switching regime business cycle model can also be used to identify turning points in the South African economy and to model economic growth. Finally, the forecasting performance of the stock market model developed in this study is compared to other stock market models. According to the results, this model is preferred to the other stock market models in terms of modelling and forecasting the level and direction of the JSE. This means that investors and policy markets can use this model to simulate the impact of changes in macroeconomic indicators on the future course of the stock market and use it to develop profitable trading rules. / Thesis (PhD (Econometrics))--University of Pretoria, 2005. / Economics / unrestricted

Understanding financial asset return correlation is a key facet in asset allocation and investor’s portfolio optimization strategy. For the last decades, several studies have investigated this relationship between stock and bond returns. But, fewer studies have dealt with multi-asset return dynamics. While initial literature attempted to understand the fundamental pattern of comovements, later studies model the economic state variables influencing such time-varying comovements of primarily stock and bond returns. Research widely acknowledges that return distributions of financial assets are non-normal. When the joint distributions of the asset returns follow a non-elliptical structure, linear correlation fails to provide sufficient information of their dependence structure. In particular two issues arise from this existing empirical evidence. The first is to propose a more reliable alternative density specification for a higher-dimensional case. The second is to formulate a measure of the variables’ dependence structure which is more instructive than linear correlation. In this work I use a time-varying conditional multivariate elliptical and non-elliptical copula to examine the return comovements of three different asset classes: financial assets, commodities and real estate in the US market. I establish the following stylized facts about asset return comovements. First, the static measures of asset return comovements overestimate the asset return comovements in the economic expansion phase, while underestimating it in the periods of economic contraction. Second, Student t-copulas outperform both elliptical and non-elliptical copula models, thus confirming the ii dominance of Student t-distribution. Third, findings show a significant increase in asset return comovements post August 2007 subprime crisis ... [cont.].

The thesis aims to investigate some of the approaches to modelling portfolio returns with heavy-tailed risk factors. It first elaborates on the univariate time series models, and compares the benchmark model (GARCH with Student t innovations or its GJR extension) predictive performance with its two competitors, the EVT-GARCH model and the Markov-Switching Multifractal (MSM) model. The motivation of EVT extension of GARCH specification is to use a more proper distribution of the innovations, based on the empirical distribution function. The MSM is one of the best performing models in the multifractal literature, a markov-switching model which is unique by its parsimonious specification and variability. The performance of these models is assessed with Mincer-Zarnowitz regressions as well as by comparison of quality of VaR and expected shortfall predictions, and the empirical analysis shows that for the risk management purposes the EVT-GARCH dominates the benchmark as well as the MSM. The second part addresses the dependence structure modelling, using the Gauss and t-copula to model the portfolio returns and compares the result with the classic variance-covariance approach, concluding that copulas offer a more realistic estimates of future extreme quantiles.

This dissertation is a collection of three economics essays on different aspects of carbon emission trading markets. The first essay analyzes the dynamic optimal emission control strategies of two nations. With a potential to become the largest buyer under the Kyoto Protocol, the US is assumed to be a monopsony, whereas with a large number of tradable permits on hand Russia is assumed to be a monopoly. Optimal costs of emission control programs are estimated for both the countries under four different market scenarios: non-cooperative no trade, US monopsony, Russia monopoly, and cooperative trading. The US monopsony scenario is found to be the most Pareto cost efficient. The Pareto efficient outcome, however, would require the US to make side payments to Russia, which will even out the differences in the cost savings from cooperative behavior. The second essay analyzes the price dynamics of the Chicago Climate Exchange (CCX), a voluntary emissions trading market. By examining the volatility in market returns using AR-GARCH and Markov switching models, the study associates the market price fluctuations with two different political regimes of the US government. Further, the study also identifies a high volatility in the returns few months before the market collapse. Three possible regulatory and market-based forces are identified as probable causes of market volatility and its ultimate collapse. Organizers of other voluntary markets in the US and worldwide may closely watch for these regime switching forces in order to overcome emission market crashes. The third essay compares excess skewness and kurtosis in carbon prices between CCX and EU ETS (European Union Emission Trading Scheme) Phase I and II markets, by examining the tail behavior when market expectations exceed the threshold level. Dynamic extreme value theory is used to find out the mean price exceedence of the threshold levels and estimate the risk loss. The calculated risk measures suggest that CCX and EU ETS Phase I are extremely immature markets for a risk investor, whereas EU ETS Phase II is a more stable market that could develop as a mature carbon market in future years.