none

Essa dissertação procura analisar se as variações dos retornos de carteiras setoriais formadas por ações do Índice teórico da Bolsa de Valores de São Paulo (Ibovespa), do primeiro quadrimestre de 2010, podem ser explicadas pelo CAPM condicional não-paramétrico proposto por Wang (2002) e também por quatro variáveis de informação disponíveis aos investidores: (i) percentual de variação do nível de produção industrial brasileira; (ii) percentual de variação do monetário agregado M4; (iii) percentual de variação da inflação representada pelo Índice de Preços ao Consumidor Amplo (IPCA); e (iv) percentual de variação da taxa de câmbio real-dólar, obtida pela cotação do dólar PTAX. O estudo compreendeu as ações listadas na Bolsa de Valores de São Paulo no período de janeiro de 2002 a dezembro de 2009. Utilizou-se a metodologia de teste desenvolvida por Wang (2002) e replicada para o contexto mexicano por Castillo-Spíndola (2006). Foram utilizados os excessos de retornos mensais para as ações, carteiras e prêmio de mercado. Ainda, para estimar a influência das variáveis de informação, foram calculados seus respectivos percentuais de variação mensal, para o período de janeiro de 2002 a novembro de 2009. A fim de validar a aplicação do CAPM condicional não-paramétrico para o mercado acionário brasileiro, foram estimados os diversos parâmetros do modelo e testada sua validade estatística para cada variável de informação avaliando-se o p-value. Os resultados observados indicam que o modelo condicional não-paramétrico é relevante na explicação dos retornos das carteiras da amostra considerada para duas das quatro variáveis testadas, M4 e dólar PTAX. / This dissertation seeks to analyze if the variations of returns from sector portfolios, formed by shares of the São Paulo Stock Exchange Index (Ibovespa), in the first four months of 2010, could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM), suggested by Wang (2002), and also by four variables of information available to the investors: (i) percentage variation of the Brazilian industrial production level; (ii) percentage variation of broad money supply M4; (iii) percentage variation of the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA); and (iv) percentage variation in the real-dollar exchange rate, obtained by PTAX dollar quotation. This study comprised the shares listed in São Paulo Stock Exchange throughout January 2002 to December 2009. The test methodology developed by Wang (2002) and retorted to the Mexican context by Castillo-Spíndola (2006) was used. The excess of monthly returns for the shares, portfolios, and market premium were used. Still, aiming to estimate the influence of information variables, their monthly percentage variations were calculated for the period from January 2002 to November 2009. In order to validate the nonparametric conditional CAPM application for the Brazilian stock market, the models several parameters were estimated and its statistic validity was tested for each information variable, evaluating the p-value. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios returns of the sample considered for two among the four tested variables, M4 and PTAX dollar.

Recent studies suggest that the conditional CAPM might hold, period-by-period, and that time-varying betas can explain the failures of the simple, unconditional CAPM. We argue, however, that significant departures from the unconditional CAPM would require implausibly large time-variation in betas and expected returns. Thus, the conditional CAPM is unlikely to explain asset-pricing anomalies like book-to-market and momentum. We test this conjecture empirically by directly estimating conditional alphas and betas from short-window regressions (avoiding the need to specify conditioning information). The tests show, consistent with our analytical results, that the conditional CAPM performs nearly as poorly as the unconditional CAP

Essa dissertação procura analisar se as variações dos retornos de carteiras setoriais formadas por ações do Índice teórico da Bolsa de Valores de São Paulo (Ibovespa), do primeiro quadrimestre de 2010, podem ser explicadas pelo CAPM condicional não-paramétrico proposto por Wang (2002) e também por quatro variáveis de informação disponíveis aos investidores: (i) percentual de variação do nível de produção industrial brasileira; (ii) percentual de variação do monetário agregado M4; (iii) percentual de variação da inflação representada pelo Índice de Preços ao Consumidor Amplo (IPCA); e (iv) percentual de variação da taxa de câmbio real-dólar, obtida pela cotação do dólar PTAX. O estudo compreendeu as ações listadas na Bolsa de Valores de São Paulo no período de janeiro de 2002 a dezembro de 2009. Utilizou-se a metodologia de teste desenvolvida por Wang (2002) e replicada para o contexto mexicano por Castillo-Spíndola (2006). Foram utilizados os excessos de retornos mensais para as ações, carteiras e prêmio de mercado. Ainda, para estimar a influência das variáveis de informação, foram calculados seus respectivos percentuais de variação mensal, para o período de janeiro de 2002 a novembro de 2009. A fim de validar a aplicação do CAPM condicional não-paramétrico para o mercado acionário brasileiro, foram estimados os diversos parâmetros do modelo e testada sua validade estatística para cada variável de informação avaliando-se o p-value. Os resultados observados indicam que o modelo condicional não-paramétrico é relevante na explicação dos retornos das carteiras da amostra considerada para duas das quatro variáveis testadas, M4 e dólar PTAX. / This dissertation seeks to analyze if the variations of returns from sector portfolios, formed by shares of the São Paulo Stock Exchange Index (Ibovespa), in the first four months of 2010, could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM), suggested by Wang (2002), and also by four variables of information available to the investors: (i) percentage variation of the Brazilian industrial production level; (ii) percentage variation of broad money supply M4; (iii) percentage variation of the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA); and (iv) percentage variation in the real-dollar exchange rate, obtained by PTAX dollar quotation. This study comprised the shares listed in São Paulo Stock Exchange throughout January 2002 to December 2009. The test methodology developed by Wang (2002) and retorted to the Mexican context by Castillo-Spíndola (2006) was used. The excess of monthly returns for the shares, portfolios, and market premium were used. Still, aiming to estimate the influence of information variables, their monthly percentage variations were calculated for the period from January 2002 to November 2009. In order to validate the nonparametric conditional CAPM application for the Brazilian stock market, the models several parameters were estimated and its statistic validity was tested for each information variable, evaluating the p-value. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios returns of the sample considered for two among the four tested variables, M4 and PTAX dollar.

No description available.

This dissertation consists of three studies on the anticipation of mergers and acquisitions (M&amp;As) and its impact on takeover event studies.  Article I investigates whether the market can anticipate both takeovers and their payment forms prior to their announcement dates. This article also proposes a new time-series approach for detecting the ex-ante deal-anticipation and payment-form anticipation dates. The results indicate that the majority of deals and their payment forms are anticipated much earlier than has been documented in previous takeover studies. Moreover, controlling for the anticipation dates matters for explaining the choice of payment method in M&amp;As. Article II studies how assuming that M&amp;As are unpredictable during the estimation window affects the measurement of abnormal returns. The results show that a part of takeover synergy is indeed incorporated into the stock prices during the estimation window of previous studies, around the deal-anticipation dates. This article estimates the parameters of the expected return model from the pre-anticipation period to control the consequences of ex-ante anticipation on the estimates of abnormal returns. Using the anticipation-adjusted approach significantly improves the estimation of the event-window abnormal returns, and provides new insights into some well-documented takeover results. Article III examines how the abnormal returns are affected when a standard event study assumes that the parameters of the expected return model are stable. Using a sample of firm takeovers, the results indicate that the parameters are indeed unstable. This article introduces a time-varying market model to account for the dynamics of merging likelihood when it estimates the abnormal returns. The findings show that the stability assumption causes a standard event study to overestimate significantly the abnormal returns to the target and acquirer shareholders. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript.</p>

Submitted by Noeme Timbo (noeme.timbo@metodista.br) on 2018-02-28T19:05:22Z No. of bitstreams: 1 Ronaldi Carassini.pdf: 1393522 bytes, checksum: 668b3b6442930f4bc8cab980d41b9895 (MD5) / Made available in DSpace on 2018-02-28T19:05:22Z (GMT). No. of bitstreams: 1 Ronaldi Carassini.pdf: 1393522 bytes, checksum: 668b3b6442930f4bc8cab980d41b9895 (MD5) Previous issue date: 2017-08-09 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Asset pricing models, such as the Capital Asset Pricing Model (CAPM), are still widely discussed within the finance area, including in the scientific community as well. These models are used theoretically and practically in the area of investments to predict the risk and return of securities and portfolios, as well as in corporate finance, to analyze the viability of investments. Despite the discussions on the subject, there is still no unanimity on what rate of return should be taken at the time of the investment option. Considering these discussions about the ideal model, the objective of this work is to analyze if the application of the conditional CAPM model is valid to explain the returns of the Brazilian stock market. To answer this question, we will use the model developed by Jagannathan and Wang (1996), which introduced the possibility that betas and risk premium vary over time. For the application of this model in the Brazilian market, 40 stocks with the highest liquidity index of the Brazilian market were selected, divided into 5 (five) portfolios, each portfolio containing 8 shares, during the period from 2008 to 2016. The empirical results of this study suggest that the betas model and the risk premium varying over time can, with some adaptations, satisfactorily explain the cross-sectional variation of the portfolio returns analysed in this research. This study intends contribute to the area of finance and also, to the literature of asset pricing. / Os modelos de precificação de ativos, como é o caso do CAPM (Capital Asset Pricing Model), ainda são muito discutidos dentro da área de finanças, inclusive também, na comunidade científica. Estes modelos são utilizados de forma teórica e prática na área de investimentos para prever o risco e o retorno de títulos e de carteiras, bem como em finanças corporativas, para analisar a viabilidade dos investimentos. Apesar das discussões sobre o tema, ainda não existe uma unanimidade sobre qual taxa de retorno deva ser tomada na hora da opção pelo investimento. Considerando estas discussões acerca do modelo ideal, o objetivo deste trabalho é analisar se a aplicação do modelo CAPM Condicional é válida para explicar os retornos do mercado acionário brasileiro. Para responder a esta questão, utilizar-se-á o modelo desenvolvido por Jagannathan e Wang (1996), o qual introduziu a possibilidade de os betas e o prêmio de risco variarem ao longo do tempo. Para a aplicação deste modelo no mercado brasileiro, foram selecionadas 40 ações com maior índice de liquidez do mercado brasileiro, divididas em 5 (cinco) portfólios, contendo cada portfólio 8 ações, durante o período de 2008 à 2016. Os resultados empíricos deste estudo, sugerem que o modelo com os betas e o prêmio de risco variando ao longo do tempo, conseguem com algumas adaptações, explicar de forma satisfatória a variação cross-sectional dos retornos dos portfólios analisados nesta pesquisa. Com este estudo pretende-se contribuir para a área de finanças e também, para a literatura de precificação de ativos.

[pt] Os resultados empíricos apresentados na literatura sobre o CAPM em geral refletem as falhas teóricas do modelo em sua forma incondicional. Deste modo, duas linhas de pesquisas principais surgiram na tentativa de relaxar alguns dos pressupostos do modelo, dando origem aos chamados modelos de multifatores e modelos condicionais. Em síntese, os modelos condicionais são aqueles nos quais o valor esperado do retorno de um ativo é explicitado de forma condicional a um conjunto de informação disponível no período anterior, e a sensibilidade ao fator de risco, beta, bem como o intercepto da equação de regressão, alfa, são considerados parâmetros variantes no tempo. Este trabalho tem dois objetivos principais: (i) avaliar de forma comparativa o tratamento dos modelos CAPM condicionais na forma em espaço de estados estimados a partir do filtro de Kalman com os erros da equação de observação nas formas homocedástica e heterocedástica, com base no trabalho de Ortas, Salvador e Moneva (2014); (ii) avaliar como o uso de medidas condicionais obtidas a partir do modelo CAPM condicional sob a abordagem aqui descrita pode melhorar a prática atual de avaliação de performance dos fundos de investimentos a partir de uma amostra do mercado brasileiro. Os resultados obtidos indicam que a modelagem heterocedástica, do ponto de vista da qualidade de ajuste aos dados da amostra, é capaz de melhorar os resultados em relação ao modelo homocedástico e aos modelos incondicionais correspondentes, proporcionando, portanto, melhores práticas de avaliação de desempenho dos fundos. / [en] The empirical results presented in the literature on the CAPM generally reflect the theoretical flaws of the model in their unconditional form. Thus, two main lines of research have emerged in an attempt to relax some of the model assumptions in its original form, giving rise to so-called multi-factor models and conditional models. In summary, the conditional models are those in which the expected value of the return of an asset is explained conditionally to a set of information available in the previous period, and the sensitivity to the risk factor, beta, as well as the intercept of the equation regression, alpha, are assumed to be time varying parameter. This work has two main objectives: (i) assess comparatively the treatment of conditional CAPM models in state-space form and estimates from the Kalman filter with the residuals of the observation equation in homocedastic and heteroskedastic forms, based on the work of Ortas, Salvador and Moneva (2014); (ii) evaluate how the use of conditional measurements obtained from the conditional CAPM under the approach previously described can improve the current practice of performance evaluation of investment funds from a sample of the Brazilian market. The results obtained indicate that heteroskedastic modeling, from the point of view of the quality of fit for the sample data, is able to provide better results in relation to homocedastic model and corresponding unconditional models, providing better practices for performance evaluation of funds.

Les modèles d’équilibre à anticipations rationnelles (EAR) ont été considérablement développés ces 40 dernières années. Cependant, encore relativement peu d’avancées ont été réalisées quant à leurs applications empiriques, les signaux privés étant inobservables. Nous proposons une nouvelle méthodologie, fondée théoriquement, pour reconstituer ces signaux et ainsi parfaitement déduire toute l’information. Ce qui nous permet de construire le portefeuille optimal des agents informés et d’explorer ses propriétés à travers trois études. Dans un premier article, nous montrons que les ordres soumis au carnet d’ordres (l’offre) et le prix d’équilibre qui en résulte constituent une statistique suffisante pour l’ensemble d’information agrégé. Nous expliquons comment extraire l’information contenue dans ces deux données, en utilisant les volumes réalisés (connus avec délai) comme proxy pour l’offre, et construire ex post le portefeuille conditionnel à l’information privée. Nous comparons ses performances avec le portefeuille optimal des agents non-informés obtenu ex ante à partir des prix. Dans un second article, nous dérivons le portefeuille optimal des investisseurs informés en explorant une spécification différente du bruit. Constitué dans la première étude par une offre fournie de façon exogène par des noise traders, nous considérons à présent que les investisseurs informés et non-informés échangent entre eux. Ils sont initialement dotés d’une quantité aléatoire d’actifs risqués et échangent rationnellement sur le marché boursier pour se couvrir et spéculer sur leur information. Nous démontrons qu’il est alors nécessaire d’utiliser la partie des volumes relative à de l’information, déterminée à partir d’une mesure de la probabilité d’échanges informés, à la place des volumes totaux. A cause des contraintes et de la complexité de cette mesure, nous trouvons qu’utiliser les volumes totaux constitue le meilleur choix, du moins jusqu’à ce qu’une meilleure mesure soit trouvée. Enfin, dans une troisième étude, nous utilisons le portefeuille des agents informés pour tester le modèle d’évaluation des actifs financiers (MEDAF) conditionnel, à la place d’un indice boursier pondéré selon les capitalisations traditionnellement utilisé comme proxy pour le portefeuille de marché. Nous démontrons que conditionner à l’information privée permet d’estimer le vrai bêta, ainsi que la prime de risque du marché en isolant la prime de risque d’information qu’un indice boursier est incapable de distinguer. / Rational expectation equilibrium (REE) models were considerably developed over the past 40 years. However, still relatively little has been done on their empirical applications, private signals being unobservable. We propose a new methodology, theoretically premised, to reconstitute these signals and thus perfectly infer all the information. This allows us to build the optimal informed investors’ portfolio and explore its properties through three studies. In the first paper, we show, based on a REE model, that the orders entered into the order book (supply) and the resulting equilibrium price constitute a sufficient statistic for the aggregate information set. We explain how to extract the information contained in these two data, using realized volumes (known with delay) as proxy for the supply, and to construct ex post the portfolio conditional on private information. We compare its performance with the optimal uninformed agents’ portfolio obtained ex ante from prices. In a second paper, we derive the optimal informed investors’ portfolio by investigating a different specification for the noise. Constituted in the first study by a supply exogenously provided by noise traders, we now consider that informed and uninformed investors trade amongst themselves. They are initially endowed with a random quantity of risky assets and have both risk-sharing and informational motives to trade rationally on the stock market. We demonstrate that we must use information-related volumes, determined with a measure of the probability of informed trades, instead of total volumes. Due to the constraints and complexity of this measure, we found that using total volumes constitutes the best choice, at least until a better measure is found. Finally, in a third study, we use the informed agents’ portfolio to test the conditional capital asset pricing model (CAPM), instead of a value-weighted stock index traditionally used as proxy for the market portfolio. We show that conditioning on private information allows estimating the real beta, as well as the market risk premium by isolating the information risk premium that an index is unable to distinguish.

[pt] Diversos estudos envolvendo modelos de fatores para apreçamento de ativos contestam a validade do CAPM. Ao longo do tempo, para explicar as chamadas anomalias dos retornos das ações, os trabalhos se voltaram tanto para a busca de novos fatores de risco – os modelos multifatores – bem como para o tratamento dinâmico das sensibilidades relacionadas aos fatores de risco – os modelos condicionais de apreçamento de ativos. Os modelos condicionais, de um ou mais fatores, explicitam o valor esperado do retorno de um ativo de forma condicional a um conjunto de informação disponível no período anterior. As sensibilidades aos fatores de risco, os betas, são estimados como parâmetros dinâmicos a partir de diferentes abordagens na literatura. Nesta tese, o objetivo é o estudo de modelos condicionais na forma espaço-estado, em que os betas seguem processos estocásticos e são estimados a partir do filtro de Kalman, de forma a verificar o ganho na capacidade explicativa dos modelos. Dois estudos empíricos são realizados, um para o CAPM condicional no mercado brasileiro e outro para o modelo de três fatores condicional de Fama e French no mercado norte-americano. De modo geral, os resultados ao se considerar a variação temporal das sensibilidades aos fatores são melhores do que os obtidos a partir dos modelos incondicionais correspondentes, tanto no que se refere ao ajuste aos dados quanto à redução proporcionada nos erros de apreçamento. / [en] The validity of CAPM is contested by several studies based on factor models. During the last decades, aiming to explain the known financial anomalies of stock returns, two major lines of research emerged: the use of asset pricing models that allow for multiple sources of risk – the multifactor models – as well as the dynamic approach to model the sensitivities of returns in respect to the risk factors – the conditional models. The conditional models, based on one or more risk factors, explicit the expected return conditional to the information set available in the previous period. The factor sensitivities, or the betas, are estimated as dynamic parameters according to different approaches in the literature. The main objective in this thesis is to study conditional pricing models based on state-space approach. The betas dynamics are described as stochastic processes and estimated through the Kalman filter in order to verify the models ability to explain the returns and related financial anomalies, such as size and value effects. Two empirical applications are presented: one for Conditional CAPM in the Brazilian stock market and another for Conditional Fama and French (1993) three-factor model in the American stock market. In both cases, time-varying sensitivities treatment provides better model adjustment as well as smaller pricing errors compared to correspondent unconditional models.