Thesis (Ph.D.)--University of Nebraska-Lincoln, 2009. / Title from title screen (site viewed October 13, 2009). PDF text: 118 p. ; 11 Mb. UMI publication number: AAT 3358959. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (B.A.)--Haverford College, Dept. of Economics, 2008. / Includes bibliographical references.

Standard and Poor's began publication of Credit Watch in November of 1981 as an early warning list for firms whose debt is under review for a possible rating change. This dissertation is composed of three essays which address various aspects of Credit Watch and the impact on shareholder wealth. The first essay uses a discriminant analysis model to classify the Credit Watch status of firms which engaged in mergers and acquisitions activity in 1991. The model correctly classifies 69.85% of the in-sample firms and 65.83% of the out of sample firms. The second essay examines whether the stock market reacts more strongly to trigger events which cause Credit Watch placements than to the actual placement. Significantly larger negative abnormal return are found around the trigger event than the placement. No evidence is found for the differential reaction evolving over time. The third essay examines firm specific and economy-wide factors which may be related to the strength of the abnormal stock return around the Credit Watch removal date. The removal return is found to be positively related to the number of trading days a firm remains on Credit Watch, negatively related to the number of updates regarding the firm released by Standard and Poor's while on the list, and positively related to the cumulative abnormal return measured between the placement and removal. This evidence suggests that the number of trading days a firm remains on Credit Watch is a proxy for information leakage to the market. The negative relationship between the removal return and the number of updates implies that the market reacts to a string of negative news of which the removal announcement is the final announcement. Finally, the positive relationship with the cumulative abnormal return between placement and removal suggests that much of the information content of the removal has been impounded into the stock price at the time of the removal.

In this study a two-part mixed probability density function was derived which described the relative changes in the Standard and Poor's 100 Stock Index over various intervals of time. The density function is a mixture of two different halves of normal distributions. Optimal values for the standard deviations for the two halves and the mean are given. Also, a general form of the function is given which uses linear regression models to estimate the standard deviations and the means. The density functions allow stock market participants trading index options and futures contracts on the S & P 100 Stock Index to determine probabilities of success or failure of trades involving price movements of certain magnitudes in given lengths of time.

Ce mémoire traite des diverses agences de notation et de l'importance qu'elles accordent aux fluctuations du nationalisme dans l'émission des cotes de crédit du Québec de 1970 à 2012. Plus spécifiquement, il a pour objectif de traiter de la situation des finances publiques du Québec, de démystifier le rôle de la notation financière et de déterminer si le nationalisme québécois est une variable spécifique prépondérante dans le processus d'évaluation des agences de notation. L'analyse statistique occupe une portion importante de la démonstration. Ce faisant, il est possible d'établir des modèles, voire des préférences méthodologiques, pour chacune des agences de notation à l'étude dans ce document. Les résultats de cette recherche démontrent que le nationalisme québécois n'est pas une variable spécifique prépondérante dans l'évaluation des agences de notation à l'endroit du Québec mais que ce sont plutôt les facteurs institutionnels et fiscaux qui vont primer lors de l'émission des cotes de crédit.

La prévision de la volatilité future constitue l'un des principaux enjeux actuels dans la finance contemporaine. De ce fait, une estimation précise de la volatilité, seul paramètre inobservable sur le marché, est cruciale pour la prise de décision en allocation d'actifs et en gestion des risques. Les modèles GARCH se basent sur les cours boursiers passés pour calculer ou estimer la volatilité. L'hypothèse qui se cache derrière cette approche est que l'on peut se servir du passé pour prédire l'avenir. Les modèles GARCH semblent toutefois peu adaptés à la prévision à long terme puisqu'ils présentent un retour à la moyenne. Nous avons alors utilisé le modèle EWMA qui présente l'avantage de ne pas retourner à la moyenne et nous avons estimé les paramètres des modèles étudiés pour reconstruire une volatilité historique de l'indice S&P500 par le biais de chaque modèle afin de les comparer avec le modèle de la volatilité réalisée. Les résultats de notre recherche montrent que la volatilité estimée par le modèle GARCH de Heston et Nandi (2000) n'est pas en mesure de reproduire la trajectoire suivie par la volatilité de l'indice S&P500 et ce modèle ne pourrait donc être employé pour faire des prévisions sur celle-ci. Nous avons trouvé également que le modèle EWMA semble significativement reproduire la même trajectoire que celle associée à la volatilité réalisée de l'indice et par conséquent on peut l'utiliser pour prévoir la volatilité future de l'indice S&P500. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Volatilité, S&P500, GARCH, EWMA, Heston et Nandi, VIX.

L'énorme incertitude qui règne sur les marchés financiers se traduit par de la volatilité intégrée dans les prix des options qui augmentent lorsque les investisseurs prévoient des fluctuations importantes des prix des actions. Le recours à une formule d'évaluation d'options est alors nécessaire pour extraire la volatilité anticipée des prix d'options cotés. Dans ce cas, les hypothèses jointes d'efficience informationnelle du marché des options et de validité du modèle d'évaluation d'options retenu sont impliquées. Motivés par ces faits, nous avons adapté le modèle de Heston pour générer la volatilité de l'indice boursier S&P 500 à partir des prix des options écrites sur cet indice. Puis nous avons estimé les paramètres du modèle pour reconstruire une volatilité historique et la comparer avec la volatilité réalisée. Les résultats de notre recherche montrent que la différence entre la volatilité estimée par le modèle de Heston et la volatilité réalisée est peu significative, ce qui nous a permis de valider et d'appliquer ce modèle pour prévoir la volatilité future de l'indice S&P 500. Nous avons trouvé également que les résultats dépendent de la quantité des options sélectionnées et de la largeur de l'intervalle des prix d'options disponibles. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Volatilité, Options, S&P500, Heston, GARCH, VIX.

Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 75. Thesis director: Willem Thorbeck. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics. Title from PDF t.p. (viewed Aug. 27, 2008). Includes bibliographical references (p. 70-74). Also issued in print.

This thesis introduces three variable clustering methods designed in the context of diversified portfolio selection. The motivation is to cluster financial assets in order to identify a small set of assets to approximate the level of diversification of the whole universe of stocks. First, we develop a data-driven approach to variable clustering based on a correlation blockmodel, in which assets in the same cluster have the same correlations with all other assets. Under the correlation blockmodel, the assets in the same cluster are controlled by the same latent factor. In addition, each cluster forms an equivalent class among assets, in the sense that the portfolio consisting of one stock from each cluster will have the same correlation matrix, regardless of the specific stocks chosen. We devise an algorithm named ACC (Asset Clustering through Correlation) to detect the clusters, with theoretical analysis and practical guidance for tuning the parameter for the algorithm. Our second method studies a multi-factor block model, which is a generalization of the correlation blockmodel. Under this multi-factor block model, assets in the same cluster are governed by a set of multiple latent factors, instead of a single factor, as in the correlation blockmodel. Observations of the asset returns lie near a union of low-dimensional subspaces under this model. We propose a subspace clustering method that utilizes square-root LASSO nodewise regression to identify these subspaces and recover the corresponding clusters. Through theoretical analysis, we provide a practical and straightforward guidance for choosing the regularization parameters. Existing subspace clustering methods based on regularized nodewise regression often arbitrarily choose the form of the regularization. The parameter that controls the regularization is also often determined exogenously or by cross-validation.Our third method theoretically unifies the choices of the regularizer and its parameter by formulating a distributionally robust version of nodewise regression. In this new formulation, we optimize the worst-case square loss within a region of distributional uncertainty around the empirical distribution. We show that this formulation naturally leads to a spectral-norm regularized optimization problem. In addition, the parameter that controls the regularization is nothing but the radius of the uncertainty region and can be determined easily based on the degree of uncertainty in the data. We also propose an alternating direction method of multipliers (ADMM) algorithm for efficient implementation. Finally, we design and implement an empirical analysis framework to verify the performance of the three proposed clustering methods. This framework consists of four main steps: clustering, stock selection, asset allocation, and portfolio backtesting. The main idea is to select stocks from each cluster to construct a portfolio and then assess the clustering method by analyzing the portfolio's performance. Using this framework, we can easily compare new clustering methods with existing ones by creating portfolios with the same selection and allocation strategies. We apply this framework to the daily returns of the S&P 500 stock universe. Specifically, we compare portfolios constructed using different clustering methods and asset allocation strategies with the S&P 500 Index benchmark. Portfolios from our proposed clustering methods outperform the benchmark significantly. They also perform favorably compared to other existing clustering algorithms in terms of the risk-adjusted return.

Companies throughout the world use different methods for reporting their financial information to capital market investors and regulators. These different methods have caused financial reporting of statements to become less transparent, has increased adjustment errors and forecasting errors, and has reduced investor confidence. As a result, the International Accounting Standards Board created International Financial Reporting Standards (IFRS) to establish a global standard. Currently, 140 jurisdictions worldwide have implemented IFRS. The purpose of this study was to examine the effectiveness of IFRS on 248 Canadian companies and to analyze whether the 2011 implementation of IFRS affected corporate stock prices, key financial measurements of companies, and industry sectors. Arrow's social choice theory and general equilibrium analysis provided the theoretical framework for this quantitative investigation. Two 1-year time periods, 2009-2010 (the year before IFRS was implemented) and 2011-2012 (the year after IFRS was implemented), were analyzed using secondary data. A multiple regression model was used to examine the impact of IFRS implementation on price-to-earnings ratio, price-to-sales ratio, and price-to-cash flow ratio of the 248 Canadian companies. Findings indicate that IFRS led to an overall improvement in financial reporting by Canadian companies, which suggests IFRS's effectiveness. Mandating IFRS worldwide may facilitate comparisons of corporate financial information, reduce costs, reduce investor fatigue, improve adjustment errors and forecasting errors, and provide capital market participants the confidence to make valued investment decisions, leading to positive social change.