Nonparametric Identication and Structural Estimation of Auction Models

Thesis (Ph.D.)--University of Washington, 2016-06

Dynamic models of exchange rate dependence using option prices and historical returns

Models for the conditional joint distribution of the U.S. Dollar/Japanese Yen and Euro/Japanese Yen exchange rates, from November 2001 until June 2007, are evaluated and compared. The conditional dependency is allowed to vary across time, as a function of either historical returns or a combination of past return data and option-implied dependence estimates. Using prices of currency options that are available in the public domain, risk-neutral dependency expectations are extracted through a copula repre- sentation of the bivariate risk-neutral density. For this purpose, we employ either the one-parameter \Normal" or a two-parameter \Gumbel Mixture" specification. The latter provides forward-looking information regarding the overall degree of covariation, as well as, the level and direction of asymmetric dependence. Specifications that include option-based measures in their information set are found to outperform, in-sample and out-of-sample, models that rely solely on historical returns.

Dependence Structure of some Bivariate Distributions

Dependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply. ACM Computing Classification System (1998): G.3, J.2.

Anatomy and lessons of the global financial crisis. Is US consumption financed by Chinese savings?

Savings and investments in the American money market by emerging countries, primarily China, financed the excessive consumption of the United States in the early 2000s, which indirectly led to a global financial crisis. The crisis started from the real estate mortgage market. Such balance disrupting processes began on the American financial market which contradicted all previously known equilibrium theories of every school of economics. Economics has yet to come up with models or empirical theories for this new disequilibrium. This is why the outbreak of the crisis could not be prevented or at least predicted. The question is, to what extent can existing market theories, calculation methods and the latest financial products be held responsible for the new situation. This paper studies the influence of the efficient market and modern portfolio theory, as well as Li’s copula function on the American investment market. Naturally, the issues of moral risks and greed, credit ratings and shareholder control, limited liability and market regulations are aspects, which cannot be ignored. In summary, the author outlines the potential alternative measures that could be applied to prevent a new crisis, defines the new directions of economic research and draws the conclusion for the Hungarian economic policy.

Reducing uncertainties in estimation of wind effects on tall buildings using aerodynamic wind tunnel tests

Tall buildings are wind-sensitive structures and could experience high wind-induced effects. Aerodynamic boundary layer wind tunnel testing has been the most commonly used method for estimating wind effects on tall buildings. Design wind effects on tall buildings are estimated through analytical processing of the data obtained from aerodynamic wind tunnel tests. Even though it is widely agreed that the data obtained from wind tunnel testing is fairly reliable the post-test analytical procedures are still argued to have remarkable uncertainties. This research work attempted to assess the uncertainties occurring at different stages of the post-test analytical procedures in detail and suggest improved techniques for reducing the uncertainties. Results of the study showed that traditionally used simplifying approximations, particularly in the frequency domain approach, could cause significant uncertainties in estimating aerodynamic wind-induced responses. Based on identified shortcomings, a more accurate dual aerodynamic data analysis framework which works in the frequency and time domains was developed. The comprehensive analysis framework allows estimating modal, resultant and peak values of various wind-induced responses of a tall building more accurately. Estimating design wind effects on tall buildings also requires synthesizing the wind tunnel data with local climatological data of the study site. A novel copula based approach was developed for accurately synthesizing aerodynamic and climatological data up on investigating the causes of significant uncertainties in currently used synthesizing techniques. Improvement of the new approach over the existing techniques was also illustrated with a case study on a 50 story building. At last, a practical dynamic optimization approach was suggested for tuning structural properties of tall buildings towards attaining optimum performance against wind loads with less number of design iterations.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

Modèles de dépendance hiérarchique pour l’évaluation des passifs et la tarification en actuariat

Dans cette thèse on s’intéresse à la modélisation de la dépendance entre les risques en assurance non-vie, plus particulièrement dans le cadre des méthodes de provisionnement et en tarification. On expose le contexte actuel et les enjeux liés à la modélisation de la dépendance et l’importance d’une telle approche avec l’avènement des nouvelles normes et exigences des organismes réglementaires quant à la solvabilité des compagnies d’assurances générales. Récemment, Shi et Frees (2011) suggère d’incorporer la dépendance entre deux lignes d’affaires à travers une copule bivariée qui capture la dépendance entre deux cellules équivalentes de deux triangles de développement. Nous proposons deux approches différentes pour généraliser ce modèle. La première est basée sur les copules archimédiennes hiérarchiques, et la deuxième sur les effets aléatoires et la famille de distributions bivariées Sarmanov. Nous nous intéressons dans un premier temps, au Chapitre 2, à un modèle utilisant la classe des copules archimédiennes hiérarchiques, plus précisément la famille des copules partiellement imbriquées, afin d’inclure la dépendance à l’intérieur et entre deux lignes d’affaires à travers les effets calendaires. Par la suite, on considère un modèle alternatif, issu d’une autre classe de la famille des copules archimédiennes hiérarchiques, celle des copules totalement imbriquées, afin de modéliser la dépendance entre plus de deux lignes d’affaires. Une approche avec agrégation des risques basée sur un modèle formé d’une arborescence de copules bivariées y est également explorée. Une particularité importante de l’approche décrite au Chapitre 3 est que l’inférence au niveau de la dépendance se fait à travers les rangs des résidus, afin de pallier un éventuel risque de mauvaise spécification des lois marginales et de la copule régissant la dépendance. Comme deuxième approche, on s’intéresse également à la modélisation de la dépendance à travers des effets aléatoires. Pour ce faire, on considère la famille de distributions bivariées Sarmanov qui permet une modélisation flexible à l’intérieur et entre les lignes d’affaires, à travers les effets d’années de calendrier, années d’accident et périodes de développement. Des expressions fermées de la distribution jointe, ainsi qu’une illustration empirique avec des triangles de développement sont présentées au Chapitre 4. Aussi, nous proposons un modèle avec effets aléatoires dynamiques, où l’on donne plus de poids aux années les plus récentes, et utilisons l’information de la ligne corrélée afin d’effectuer une meilleure prédiction du risque. Cette dernière approche sera étudiée au Chapitre 5, à travers une application numérique sur les nombres de réclamations, illustrant l’utilité d’un tel modèle dans le cadre de la tarification. On conclut cette thèse par un rappel sur les contributions scientifiques de cette thèse, tout en proposant des angles d’ouvertures et des possibilités d’extension de ces travaux.

Estimation du paramètre d'une copule archimedienne en présence de censure dépendante

Les méthodes classiques d’analyse de survie notamment la méthode non paramétrique de Kaplan et Meier (1958) supposent l’indépendance entre les variables d’intérêt et de censure. Mais, cette hypothèse d’indépendance n’étant pas toujours soutenable, plusieurs auteurs ont élaboré des méthodes pour prendre en compte la dépendance. La plupart de ces méthodes émettent des hypothèses sur cette dépendance. Dans ce mémoire, nous avons proposé une méthode d’estimation de la dépendance en présence de censure dépendante qui utilise le copula-graphic estimator pour les copules archimédiennes (Rivest etWells, 2001) et suppose la connaissance de la distribution de la variable de censure. Nous avons ensuite étudié la consistance de cet estimateur à travers des simulations avant de l’appliquer sur un jeu de données réelles.

Distribuição de funções de variáveis aleatórias dependentes e R-Vines cópulas

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2015.

Perifrasi verbali con εἰμί più participio nelle varianti del Pentateuco dei LXX

Nell’articolo sono raccolte e discusse le varianti con εἰμί più participio individuate nei testimoni della tradizione testuale del Pentateuco dei LXX; sono considerate anche le varianti con γίνομαι ma solo quelle concerneti il predicato nominale. La finalità è di osservare se vi siano tracce dell’affermazione della perifrasi nelle varianti, eventuali concentrazioni di frequenza insieme a sfumature d’uso, con particolare attenzione alla datazione dei testimoni dove appaiono. Un paragrafo è riservato anche alle varianti di costruzioni con predicato nominale (copula + aggettivo) rispetto a un verbo finito.

Reducing Uncertainties in Estimation of Wind Effects on Tall Buildings Using Aerodynamic Wind Tunnel Tests

Tall buildings are wind-sensitive structures and could experience high wind-induced effects. Aerodynamic boundary layer wind tunnel testing has been the most commonly used method for estimating wind effects on tall buildings. Design wind effects on tall buildings are estimated through analytical processing of the data obtained from aerodynamic wind tunnel tests. Even though it is widely agreed that the data obtained from wind tunnel testing is fairly reliable the post-test analytical procedures are still argued to have remarkable uncertainties. This research work attempted to assess the uncertainties occurring at different stages of the post-test analytical procedures in detail and suggest improved techniques for reducing the uncertainties. Results of the study showed that traditionally used simplifying approximations, particularly in the frequency domain approach, could cause significant uncertainties in estimating aerodynamic wind-induced responses. Based on identified shortcomings, a more accurate dual aerodynamic data analysis framework which works in the frequency and time domains was developed. The comprehensive analysis framework allows estimating modal, resultant and peak values of various wind-induced responses of a tall building more accurately. Estimating design wind effects on tall buildings also requires synthesizing the wind tunnel data with local climatological data of the study site. A novel copula based approach was developed for accurately synthesizing aerodynamic and climatological data up on investigating the causes of significant uncertainties in currently used synthesizing techniques. Improvement of the new approach over the existing techniques was also illustrated with a case study on a 50 story building. At last, a practical dynamic optimization approach was suggested for tuning structural properties of tall buildings towards attaining optimum performance against wind loads with less number of design iterations.