本研究探討選擇權交易量之大小對於波動度預測之效率性所造成之對偶效果(dual effect)，驗證〝正常的高交易量〞與〝異常的高交易量〞對於波動度預測能力是否有不同的影響。本研究採用panel data之資料型態，以LIFFE上市的個股買權為對象，資料長度為三年左右。主要欲探討之假說為： 1.一般而言，交易量大的選擇權，其波動度估計誤差較交易量小的選擇權來得小。 2.相對於平日水準而言，某日交易量異常高的選擇權將有較大的波動度估計誤差。 本研究所使用的波動度預測模型為隱含波動度(ISD)，採用的是最接近到期月份及最接近價平的合約。實證以組合迴歸、固定效果模型、隨機效果模型分別估計之，加以比較。結果發現固定效果模型為較佳之解釋模型，然而結果顯示交易量的對偶效果並不明確影響波動度預測誤差，故推測有某種影響公司間差異的因素，即公司間之異質性，比相對交易量更容易影響波動度預測之誤差。另外，透過組間與組內效果之分析，發現不論是長期還是短期，由於公司間的異質性存在，使得相對交易量對於波動度預測誤差均無明顯影響。 / The purpose of this research is to study the dual effect on the efficiency of volatility forecasting which is caused by the volume of option market, with the intent to test whether〝normal high volume〞and〝abcdrmal high volume〞cause different results on the ability of volatility forecasting. The data used is in the form of panel data. It is drawn from LIFFE, and has a length of about three years. The hypotheses to be examined in this study are：1. High-average-volume options have smaller volatility forecasting errors than low-average-volume options; 2. Options have larger volatility forecasting errors on abcdrmally-high-volume days than on normal-volume days. In this research, volatility is forecasted by implied standard deviation (ISD) which is implied in the at-the-money and the nearest expiry month options. Pooled regression、fixed effect model、and random effect model methods were applied. The results show that the fixed effect model made the best analysis amongst the three models. However, the result does not support the hypotheses made above, which means that volume does not have much influence on volatility forecasting error. It is inferred that there exists some other factors which could cause the difference between firms, namely heterogeneity, and these factors have much more powerful influence over volatility forecasting error than volume. Finally, it was found that no matter for long run or short run, because of the existence of heterogeneity, relative volume doesn’t have obvious influence on volatility forecasting errors when analyzing the difference between the between-individual effect and the within-individual effect.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O objetivo principal desta dissertação é analisar os impactos da abertura comercial vista no Brasil no início da década de 1990 entre 1990 a 1996 na margem de lucro das indústrias de transformação passando por três padrões monetários diferentes (cruzeiro, cruzeiro real e real). A especificação e metodologia adotadas no trabalho são as de Goldar e Aggawal (2004), que fazem uma análise de dados em painel para efeitos fixos e randômicos para as indústrias de transformação indianas como um todo e, posteriormente, aplicando os mesmos testes separando os vinte e oito setores da indústria brasileira de transformação em setores de bens de capital e intermediários no primeiro grupo e bens de capital no segundo. Este trabalho ainda inclui esta metodologia aplicando, além das duas já citadas, o teste de mínimos quadrados ordinários (MQO) para permitir uma melhor análise com três testes diferentes antes e depois de separar os setores por meio de variáveis explicativas como barreiras à importação, concentração industrial, participação salarial, produtividade do trabalho, representatividade setorial e variação na taxa de crescimento da produção do setor entre os anos. Conclui-se que o aumento observado na margem de lucro foi impactado significativamente pelas variáveis expostas acima e estes resultados são importantes para que possamos auferir de que forma impactaram positivamente ou contribuíram negativamente na margem de lucro auferida pela indústria brasileira de transformação entre 1990 e 1996.

Dans les études longitudinales en cancer, une problématique majeure est la description de l’évolution de la maladie d’un patient ou la prédiction de son état futur, à partir de mesures répétées d’un marqueur biologique. La modélisation conjointe permet de répondre à ces objectifs, mais elle a principalement été développée pour l’étude simultanée d’un marqueur longitudinal Gaussien et d’un unique temps d’événement. Afin de caractériser les transitions entre événements successifs qu’un patient peut connaître, nous étendons la méthodologie classique en introduisant un modèle conjoint pour un processus longitudinal Gaussien et un processus multi-états Markovien non homogène. Le modèle suppose que les temps de transition individuels sont indépendants conditionnellement aux covariables incluses. Nous proposons aussi un score test afin de tester cette hypothèse. Ces développements sont appliqués à deux cohortes d’hommes avec un cancer de la prostate localisé traité par radiothérapie. Le modèle permet de quantifier l’impact des dynamiques de l’antigène spécifique de la prostate, et d’autres facteurs pronostiques mesurés à la fin du traitement, sur chaque intensité de transition entre états cliniques prédéfinis. Cette thèse fournit ensuite des outils statistiques et des lignes directrices pour le calcul de prédictions dynamiques individuelles d’événements cliniques, dans le cadre de risques compétitifs. Enfin, un dernier travail amène une réflexion sur la modélisation conjointe de données longitudinales ordinales et de données de survie, avec une technique d’inférence innovante. Ainsi, ce travail introduit des méthodes statistiques adaptées à divers types de données longitudinales et d’histoire d’événements, qui permettent de répondre aux besoins des cliniciens. Des recommandations méthodologiques et des outils logiciels sont associés à chaque développement, pour une utilisation pratique par les communautés clinique et statistique. / In longitudinal studies in cancer, a major problem is the description of the patient’s disease evolution or the prediction of his future state, based on repeated measurements of a biological marker. Joint modelling enables to meet these objectives but it has mainlybeen developed for the simultaneous study of a Gaussian longitudinal marker and a single event time. In order to characterize the transitions between successive events that a patient may experience, we extend the classical methodology by introducing a joint model for a Gaussian longitudinal process and a non-homogeneous Markovian multi-state process. The model assumes that individual transition times are independent conditionally to included covariates. We also propose a score test to assess this assumption. These developments are applied on two cohorts of men with localized prostate cancer treated with radiotherapy. The model quantifies the impact of prostate specific antigen dynamics, and other prognostic factors measured at the end of treatment, on each transition intensity between predefined clinical states. This thesis then provides statistical tools and guidelines for the computation of individual dynamic predictions of clinical events in the context of competitive risks. Finally, a last work leads to a reflection on joint modelling of longitudinal ordinal data and survival data with an innovative inference technique. To conclude, this work introduces statistical methods adapted to various types of longitudinal data and event history data, which meet the needs of clinicians. Methodological recommendations and software tools are associated with each development, for practical use by the clinical and statistical communities.

In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.

Appartenir à une catégorie socio-économique moins élevée est généralement associé à une mortalité plus élevée pour de nombreuses causes de décès. De précédentes études ont déjà montré l’importance de la prise en compte des différentes dimensions des trajectoires socio-économiques au cours de la vie. L’analyse des trajectoires professionnelles constitue une étape importante pour mieux comprendre ces phénomènes. L’enjeu pour mesurer l’association entre les parcours de vie des trajectoires socio-économiques et la mortalité est de décomposer la part respective de ces facteurs dans l’explication du niveau de survie des individus. La complexité de l’interprétation de cette association réside dans la causalité bidirectionnelle qui la sous-tend: Les différentiels de mortalité sont-ils dus à des différentielsd’état de santé initial influençant conjointement la situation professionnelle et la mortalité, ou l’évolution professionnelle influence-t-elle directement l’état de santé puis la mortalité?Les méthodes usuelles ne tiennent pas compte de l’interdépendance des changements de situation professionnelle et de la bidirectionnalité de la causalité qui conduit à un biais important dans l’estimation du lien causale entre situation professionnelle et mortalité. Par conséquent, il est nécessaire de proposer des méthodes statistiques qui prennent en compte des mesures répétées (les professions) simultanément avec les variables de survie. Cette étude est motivée par la base de données Cosmop-DADS qui est un échantillon de la population salariée française.Le premier objectif de cette thèse était d’examiner l’ensemble des trajectoires professionnelles avec une classification professionnelle précise, au lieu d’utiliser un nombre limité d’états dans un parcours professionnel qui a été considéré précédemment. A cet effet, nous avons défini des variables dépendantes du temps afinde prendre en compte différentes dimensions des trajectoires professionnelles, à travers des modèles dits de "life-course", à savoir critical period, accumulation model et social mobility model, et nous avons mis en évidence l’association entre les trajectoires professionnelles et la mortalité par cause en utilisant ces variables dans un modèle de Cox.Le deuxième objectif a consisté à intégrer les épisodes professionnel comme un sous-modèle longitudinal dans le cadre des modèles conjoints pour réduire le biais issude l’inclusion des covariables dépendantes du temps endogènes dans le modèle de Cox. Nous avons proposé un modèle conjoint pour les données longitudinales nominaleset des données de risques concurrents dans une approche basée sur la vraisemblance. En outre, nous avons proposé une approche de type méta-analyse pour résoudre les problèmes liés au temps des calculs dans les modèles conjoints appliqués à l’analyse des grandes bases de données. Cette approche consiste à combiner les résultats issus d’analyses effectuées sur les échantillons stratifiés indépendants. Dans la même perspective de l’utilisation du modèle conjoint sur les grandes bases de données, nous avons proposé une procédure basée sur l’avantage computationnel de la régression de Poisson.Cette approche consiste à trouver les trajectoires typesà travers les méthodes de la classification, et d’appliquerle modèle conjoint sur ces trajectoires types. / Being in low socioeconomic position is associated with increased mortality risk from various causes of death. Previous studies have already shown the importance of considering different dimensions of socioeconomic trajectories across the life-course. Analyses of professional trajectories constitute a crucial step in order to better understand the association between socio-economic position and mortality. The main challenge in measuring this association is then to decompose the respectiveshare of these factors in explaining the survival level of individuals. The complexity lies in the bidirectional causality underlying the observed associations:Are mortality differentials due to differences in the initial health conditions that are jointly influencing employment status and mortality, or the professional trajectory influences directly health conditions and then mortality?Standard methods do not consider the interdependence of changes in occupational status and the bidirectional causal effect underlying the observed association and that leads to substantial bias in estimating the causal link between professional trajectory and mortality. Therefore, it is necessary to propose statistical methods that consider simultaneously repeated measurements (careers) and survivalvariables. This study was motivated by the Cosmop-DADS database, which is a sample of the French salaried population.The first aim of this dissertation was to consider the whole professional trajectories and an accurate occupational classification, instead of using limitednumber of stages during life course and a simple occupational classification that has been considered previously. For this purpose, we defined time-dependent variables to capture different life course dimensions, namely critical period, accumulation model and social mobility model, and we highlighted the association between professional trajectories and cause-specific mortality using the definedvariables in a Cox proportional hazards model.The second aim was to incorporate the employment episodes in a longitudinal sub-model within the joint model framework to reduce the bias resulting from the inclusion of internal time-dependent covariates in the Cox model. We proposed a joint model for longitudinal nominal outcomes and competing risks data in a likelihood-based approach. In addition, we proposed an approach mimicking meta-analysis to address the calculation problems in joint models and large datasets, by extracting independent stratified samples from the large dataset, applying the joint model on each sample and then combining the results. In the same objective, that is fitting joint model on large-scale data, we propose a procedure based on the appeal of the Poisson regression model. This approach consist of finding representativetrajectories by means of clustering methods and then applying the joint model on these representative trajectories.

Regional integration gained popularity and is prioritised globally, especially in developing economies, including those on the African continent. This is based on its potential to accelerate trade, stimulate economic growth, and increase access to basic necessities and to induce a sustainable increase in economic output and improved standards of living. Regional integration in the context of developing economies is entirely implicit. Modern literature observes it as a policy option for dealing with a wide variety of issues related to politics, economic factors, and societal welfare. The SACU, existing since 1910, made several trade agreements globally. The union aims at reducing inequalities, ensuring continuous improvement in the general welfare of the population, and sustainable economic growth. Research, though, indicates that the region persistently reflects poor socio-economic conditions. This is accompanied by limited development in infrastructure, lowly skilled and experienced workforce. Primary sector activities dominate their economies, such as mining and agriculture, high levels of inequalities and poverty. Regional integration was implemented differently in several countries globally, and Africa in particular. The research noted that literature on regional integration and its implications on socio-economic development lacks, especially in the context of SACU. A deficiency was also emphasised the universal measurement of regional integration, which is not standardised. Some research employed single variables as a proxy, whilst some composite indices were also compiled and implemented, suiting the diverse setups and environments. The development measurements, therefore, cannot universally be applied attributable to context-specific concerns, prevalent in regions or countries. This study developed the SACU Regional Integration Index (SRII) because the existing indices on regional integration are limited concerning applicability. Most of the indices established in the literature were developed for specific countries and regions with diverse characteristics from those of the SACU region. In addition to a detailed literature review and closing methodological divergencies, this study evaluated the effects of regional integration on socio-economic development in the SACU countries. The objectives of the study were first, to produce the SACU Regional Integration Index. Second, the study aimed at evaluating the effect of regional integration on various socio-economic development factors listed as economic growth, investments, and the Human Development Index (HDI), inequalities and poverty. Third, the study provided policy recommendations to the socio-economic problems encountered by the SACU countries; and lastly, to implement the proposed SRII as a way of providing policymakers with the actual impacts. The study employed the principal component analysis (PCA) to construct the SRII. The Ordinary Least Squares (LSDV), fixed effects and random effects were employed to ascertain the effect of regional integration on socio-economic development in the SACU countries. The constructed SACU index comprises four dimensions. These are trade integration; productive integration; infrastructure integration; and financial and macroeconomic policies integration. The index revealed that SACU countries are dominated by trade and productive integration. Further analysis of the results indicated that collaboration on the financial and macroeconomic policies is lacking and the infrastructure dimension is lagging in the SACU region. Based on the second objective, the results indicate that regional integration is critical in improving trade openness and HDI, especially in Lesotho, Botswana, and Namibia. The effect of regional integration on real Gross Domestic Product (GDP) growth, inequalities, and poverty reduction was realised in the long run through the interaction of all variables under study. This supported the dynamic effects posited by the dynamic theory of regional integration. It was established that growth, though, in infrastructure is insignificant compared to other dimensions of regional integration. This explains why regional integration was unsupportive concerning stimulating investments in all the economies forming the SACU region. The third objective was to proffer policy recommendations. Several practical policy recommendations emerged from this study, based on the literature findings and review. These recommendations include implementing inclusive development programmes, promotion private sector participation in economic activities, and policies, to boost production capacity in the countries in this region. Based on the fourth objective, this study further recommends SACU as a region, to integrate into the global economy. This can be conducted by participating in global production networks for manufacturing and taking advantage of emerging economies. This would diversify their export markets and their sources of finance development. SACU countries should make regional integration and trade a part of their national and sectoral development plans, ensuring coherent trade and industrial policies. They should also improve their labour, education, social protection, and safety nets. With data availability, this research can be extended to incorporate quarterly data or more years of study. Time-series methods can be applied, such as the Autoregressive Distributive Lag (ARDL) method. This will increase the sample size and the number of observations, which can improve the outcome from the statistical and econometric analysis. Future studies may also evaluate the applicability of the index constructed in this study. / Economics / D. Phil. (Economics)

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<p dir="ltr">Seafood plays a pivotal role in global economies, livelihoods, and nutritional security. However, climate change and global pandemics pose significant threats to seafood harvests, production, supply chains, and marketing channels. The focus of my thesis is to understand the impact of external factors on our seafood resources and explore adaptive strategies in the face of uncertainties. We utilize economics techniques to study human-nature systems by zooming into social elements (government agencies, industry stakeholders, and fish farmers/fishermen) and aquatic resources. The three essays of my thesis delve into this inquiry from the perspectives of government, industry, and market, accordingly.</p><p dir="ltr">The first chapter in my thesis, <i>Climate Change and Snow Crab Harvest - Applying Random Effect Estimators with Instrumental Variable</i>, estimates the snow crab harvest function with unbalanced panel data of eastern Bering Sea snow crab, Canadian snow crab, Japanese snow crab, and Barents Sea snow crab. Specifically, we analyze the relationship between snow crab biomass, stock, and catch. To address the endogeneity of stock in the harvest function, climate change indicators are selected as instrumental variables. We identify that the Arctic Sea ice extent is effective in addressing the endogeneity and the random effects instrumental variable model with error components two stage least squares estimator performs the best to control heterogeneity. We find that a 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock-harvest elasticity and provide supporting evidence for government fishery agencies to prioritize stock enhancement in policy designs.</p><p dir="ltr">The second chapter, <i>Online Media Sentiment Analysis of Shrimp and Salmon in the United States</i>, employs online media analytics on shrimp and salmon in the US to provide insights into consumer perceptions and potential demand signals for seafood. Search hits and mentions are quantified for top sources, domains, and prevalent terms. In addition, sentiment drivers and sentiment values are identified and calculated using natural language processing tools. The results reveal that the occurrence of peak mentions does not necessarily coincide with the peak of net sentiment, and farmed seafood consistently exhibits lower net sentiments compared to their wild counterparts. Autoregressive modeling is conducted to predict the dynamics of seafood’s net sentiments. The regional analysis demonstrates that public attitudes toward both farmed shrimp and salmon in the East North Central region exhibit a more positive net sentiment, while the New England and Middle Atlantic regions tend to have a lower net sentiment for farmed shrimp and salmon, respectively. The fitted forecast model serves as a supplementary tool for industry stakeholders to quickly respond to future public perceptions. Regional statistics also help the seafood industry tailor business strategies to different regions.</p><p dir="ltr">In the third chapter, <i>Comparative Case Study of Small-Scale Fish Processing for Local Seafood Supply</i><i>,</i> we examine the feasibility of utilizing a shared-use commercial kitchen and on-farm kitchen to support small-scale local fish processing, which helps diversify marketing channels in the US Midwest and supply seafood to local food systems. A case study of each facility type is assessed for economic viability for fish farmers. The financial analysis suggests farmers interested in processing tilapia or rainbow trout from 2,500 lbs to 5,000 lbs per year utilize rental commercial kitchens. A minimum of 15% markup and processing of 10,000 lbs/year tilapia is required to make the on-farm kitchen option more viable. For farmers who process rainbow trout, 10,000 lbs/year with a 10% markup using an on-farm kitchen is a better choice. Factoring in the stochastic variability of raw product prices, rental rates, and set-up costs, we provide simulated ranges for economic metrics including profitability index, payback period, and net present values. The reports of estimated costs, revenues, and breakeven prices, provide fish farmers with suggested selling prices, kitchen choices, and production levels to achieve optimum profits under risks.</p>