The thesis "Four Essays on the Econometric Modelling of Volatility and Durations" consists of four research papers in the area of financial econometrics on topics of the modelling of financial market volatility and the econometrics of ultra-high-frequency data. The aim of the thesis is to develop new econometric methods for modelling and hypothesis testing in these areas. The second chapter introduces a new model, the time-varying GARCH (TV-GARCH) model, in which volatility has a smooth time-varying structure of either additive or multiplicative type. To characterize smooth changes in the (un)conditional variance we assume that the parameters vary smoothly over time according to the logistic transition function. A data-based modelling technique is used for specifying the parametric structure of the TV-GARCH models. This is done by testing a sequence of hypotheses by Lagrange multiplier tests presented in the chapter. Misspecification tests are also provided for evaluating the adequacy of the estimated model. The third chapter addresses the issue of modelling deterministic changes in the unconditional variance over a long return series. The modelling strategy is illustrated with an application to the daily returns of the Dow Jones Industrial Average (DJIA) index from 1920 until 2003. The empirical results sustain the hypothesis that the assumption of constancy of the unconditional variance is not adequate over long return series and indicate that deterministic changes in the unconditional variance may be associated with macroeconomic factors. In the fourth chapter we propose an extension of the univariate multiplicative TV-GARCH model to the multivariate Conditional Correlation GARCH (CC-GARCH) framework. The variance equations are parameterized such that they combine the long-run and the short-run dynamic behaviour of the volatilities. In this framework, the long-run behaviour is described by the individual unconditional variances, and it is allowed to vary smoothly over time according to the logistic transition function. The effects of modelling the nonstationary variance component are examined empirically in several CC-GARCH models using pairs of seven daily stock return series from the S&P 500 index. The results show that the magnitude of such effect varies across different stock series and depends on the structure of the conditional correlation matrix. An important feature of financial durations is the evidence of a strong diurnal variation over the trading day. In the fifth chapter we propose a new parameterization for describing the diurnal pattern of trading activity. The parametric structure of the diurnal component allows the duration process to change smoothly over the time-of-day according to the logistic transition function. The empirical results suggest that the diurnal variation may not always have the inverted U-shaped pattern for the trade durations as documented in earlier studies.

Depuis plusieurs décennies, les prix des énergies sont sujets à une volatilité croissante pesant considérablement sur l’ensemble de l’économie. Comparée aux prix des autres matières premières (comme, par exemple, les métaux précieux, ou encore les produits agricoles), l’évolution des produits énergétiques est apparue exceptionnellement incertaine, tant à long terme qu’à court terme. Dans un contexte économique global, ce phénomène acquiert toute son importance tant les dommages sur l’économie réelle d’une forte variation des prix des matières premières peuvent être conséquents. Cette thèse s’intéresse donc aux causes profondes expliquant ces fluctuations. Plus spécifiquement, en unissant les différents champs de l’économie de l’énergie, de l’économétrie, de la finance et de la psychologie, elle s’attache à comprendre le phénomène de financiarisation des commodités et les relations étroites entre marchés financiers et marchés des matières premières. Cette réflexion s’articule en trois thèmes : d’une part la relation entre les prix des différentes énergies et leurs propriétés financières est analysée, d’autre part les aspects émotionnels et comportementaux des marchés sont étudiés, enfin les liens directs entre marchés boursiers et marchés de commodités sont abordés. / Since decades, energy prices are subject to increasing volatility affecting the whole economy. Compared to other commodity prices (for example precious metals and agro-industrial), energy price dynamics appear to be extremely uncertain both at short and long run. In a global economic context, this phenomenon is very important since intense variations of commodity prices can be tragic to real economy. This thesis focuses on the true nature of these movements. More formally, we investigate the commodity markets’ financialization, as well as the relationships between commodity and stock markets by unifying the fields of energy economics, econometrics, finance and psychology. This analysis is based on three themes: first energy prices relationships and their financial properties are analyzed, and then the behavioral and emotional specification of energy markets are studied, finally comovements between stock and commodity markets’ volatility are considered

Cette thèse porte sur la modélisation, la prévision et la couverture du risque de contagion financière. Après une présentation générale des fondements théoriques et des mécanismes de propagation relatifs à la contagion financière, nous introduisons une modélisation fondée sur les modèles de cointégration non linéaire et de causalité non linéaire dans lesquels, les variables et le terme d’erreur du modèle à correction d’erreur obéissent à la dynamique de processus auto-régressifs à changement de régime de type TAR et M-TAR pour capter l’effet de contagion. Une extension de cette modélisation au cadre de prévision probabiliste conditionnelle a été faite par la suite à travers les réseaux de croyance Bayésienne pour renforcer le pouvoir prédictif. Ensuite, nous montrons comment une institution financière peut couvrir son portefeuille contre ce type de risque par de nouvelles approches. Nous proposons pour cela, une stratégie de couverture purement statique dans une perspective règlementaire à l’aide de modèles génératifs de type Vines-copula, une stratégie de couverture semi-statique fondée sur la budgétisation des risques et une stratégie de couverture dynamique à partir des processus de diffusion à sauts mutualisés. Ces nouvelles modélisations sont testées empiriquement sur un ensemble d’indices boursiers. / This Ph.D thesis focuses on modeling, forecasting and hedging financial contagion. After an overview of the theoretical foundations and spread mechanism relating to financial contagion, we introduce modeling based on nonlinear cointegration and non-linear causality models in which the variables and the error term in the correction model error obey at the dynamics of autoregressive regime change process of type TAR and M-TAR to catch the contagion effect. An extension of this model to conditional probabilistic forecasting framework was done through Bayesian belief networks, to enhance the predictive power. Then we show how a financial institution can hedge its portfolio against this risk by new specifications. Therefore, we offer a purely static hedging strategy in a regulatory perspective using generative models Vines-copula, a semi-static hedging strategy based on risk budgeting and dynamic hedging strategy based on mutually exciting jumps diffusion process. These new models are tested empirically on set of market indices.

<p dir="ltr">In this thesis, we delve into the exploration of several nonparametric and semiparametric econometric models within the Bayesian framework, highlighting their applicability across a broad spectrum of microeconomic and macroeconomic issues. Positioned in the big data era, where data collection and storage expand at an unprecedented rate, the complexity of economic questions we aim to address is similarly escalating. This dual challenge ne- cessitates leveraging increasingly large datasets, thereby underscoring the critical need for designing flexible Bayesian priors and developing scalable, efficient algorithms tailored for high-dimensional datasets.</p><p dir="ltr">The initial two chapters, Chapter 2 and 3, are dedicated to crafting Bayesian priors suited for environments laden with a vast array of variables. These priors, alongside their corresponding algorithms, are optimized for computational efficiency, scalability to extensive datasets, and, ideally, distributability. We aim for these priors to accommodate varying levels of dataset sparsity. Chapter 2 assesses nonparametric additive models, employing a smoothing prior alongside a band matrix for each additive component. Utilizing the Bayesian backfitting algorithm significantly alleviates the computational load. In Chapter 3, we address multiple linear regression settings by adopting a flexible scale mixture of normal priors for coefficient parameters, thus allowing data-driven determination of the necessary amount of shrinkage. The use of a conjugate prior enables a closed-form solution for the posterior, markedly enhancing computational speed.</p><p dir="ltr">The subsequent chapters, Chapter 4 and 5, pivot towards time series dataset model- ing and Bayesian algorithms. A semiparametric modeling approach dissects the stochastic volatility in macro time series into persistent and transitory components, the latter addi- tional component addressing outliers. Utilizing a Dirichlet process mixture prior for the transitory part and a collapsed Gibbs sampling algorithm, we devise a method capable of efficiently processing over 10,000 observations and 200 variables. Chapter 4 introduces a simple univariate model, while Chapter 5 presents comprehensive Bayesian VARs. Our al- gorithms, more efficient and effective in managing outliers than existing ones, are adept at handling extensive macro datasets with hundreds of variables.</p>

This dissertation consists of three essays wherein tools of financial econometrics are used to study the three aspects of farmland valuation puzzle: short-term boom-bust cycles, overpricing of farmland, and inconclusive effects of direct government payments. Essay I addresses the causes of unexplained short-term boom-bust cycles in farmland values in a dynamic land pricing model (DLPM). The analysis finds that gross return rate of farmland asset decreases as the farmland asset level increases, and that the diminishing return function of farmland asset contributes to the boom-bust cycles in farmland values. Furthermore, it is mathematically proved that land values are potentially unstable under diminishing return functions. We also find that intertemporal elasticity of substitution, risk aversion, and transaction costs are important determinants of farmland asset values. Essay II examines the apparent overpricing of farmland by decomposing the forecast error variance of farmland prices into forward looking and backward looking components. The analysis finds that in the short run, the forward looking Capital Asset Pricing Model (CAPM) portion of the forecast errors are significantly higher in a boom or bust stage than in a stable stage. This shows that the farmland market absorbs economic information in a discriminative manner according to the stability of the market, and the market (and actors therein) responds to new information gradually as suggested by the theory. This helps to explain the overpricing of farmland, but this explanation works primarily in the short run. Finally, essay III investigates the duel effects of direct government payments and climate change on farmland values. This study uses a smooth coefficient semi-parametric panel data model. The analysis finds that land valuation is affected by climate change and government payments, both through discounted revenues and through effects on the risk aversion of land owners. This essay shows that including heterogeneous risk aversion is an efficient way to mitigate the impacts of misspecifications in a DLPM, and that precipitation is a good explanatory variable. In particular, precipitation affects land values in a bimodal manner, indicating that farmland prices could have multiple peaks in precipitation due to adaption through crop selection and technology alternation.

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Comovement is ubiquitous in financial markets. The evolution of asset characteristics, such as price, volatility or liquidity, exhibits a high degree of correlation across assets---a phenomenon that in this thesis will generically be denoted with the term comovement. The origins of such comovement are legion. In their investment decisions, economic agents are not only influenced by their idiosyncrasies---a large part of investment motivations are shared over a population. Demographics or the political situation can generate constraints that are similar for a large number of people. A country's geography can greatly influence the sectors in which it is most productive, which implies that many people are sometimes subject to the same risk factors. Moreover, it is well known that mimesis is part of human psychology, and that people mimic their peers even when taking personal decisions. For these reasons, and many more, financial markets have a very systematic character, and studying the nature and intensity of such comovement is important from a risk management point of view. <p>This thesis studies comovement in financial markets under three dimensions. First, I consider comovement in equity liquidity. The liquidity of an asset is the ease with which that asset can be bought or sold. Liquidity can be measured in various ways and the first chapter concludes that market movements of two different liquidity measures have the same origin. Second, I study the impact correlation comovement on the price of stocks. The correlations between stock returns and the market return evolve through time and are correlated themselves. The effect of this correlation comovement on asset prices is however ambiguous and there is not enough evidence to depict a clear image. Finally, I develop a model to investigate contagion dynamics in the secondary market for European sovereign bonds over the past two years. More particularly, I study whether changes in the bond price of one specific country have an impact the next day on the average bond price in Europe. The study concludes of that bonds of France, Ireland, Portugal, Spain and Italy have been most contagious, whereas the much more volatile Greek bonds have had little impact on the other European countries. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Cette thèse porte sur les relations entre le Système Communautaire d'Échange de Quotas d'Émission (SCEQE) et les marchés de l'énergie. Une attention particulière est donnée au changement de combustible, le principal moyen de réduire les émissions de CO2 à court-terme dans le SCEQE. Cela consiste à substituer des centrales gaz aux centrales charbon dans la production d'électricité en dehors des heures de pointes. Ainsi, les centrales charbon fonctionnent sur de plus courtes périodes, ce qui permet de réduire les émissions de CO2. Le Chapitre 1 décrit différentes approches expliquant les relations entre les marchés de l'énergie et du CO2. Une revue de littérature est ensuite présentée. Nous donnons une description détaillée du processus de changement de combustible. En particulier, l'influence de l'efficacité des centrales est analysée. Le Chapitre 2 fournit une étude théorique de l'impact des différences d'efficacité parmi les centrales gaz pour le changement de combustible. Le principal résultat montre que la sensibilité du prix du CO2 vis-à-vis du prix du gaz dépend du niveau des émissions de CO2.Le Chapitre 3 examine les interactions entre les prix de l'électricité, du charbon, du gaz et du CO2 dans une étude empirique. Les résultats montrent une qu'il existe une relation significative entre le gaz et le CO2 à l'équilibre de long-terme. Le Chapitre 4 étudie le processus de découverte des informations qui influencent laformation des prix du gaz et du CO2. La forte relation entre le gaz et le CO2 indique que leurs prix sont affectés par les mêmes informations. Nous montrons dans une étude empirique que le marché du CO2 domine le processus de découverte de ces informations. / This thesis investigates relationships between the European Union Emission Trading Scheme (EU ETS) and energy markets. A special focus is given to fuel switching, the main shortterm abatement measure within the EU ETS. This consists in substituting Combined Cycle Gas Turbines (CCGTs) for hard-coal plants in off-peak power generation. Thereby coal plants run for shorter periods, which allows power producers to reduce their CO2 emissions. In Chapter 1, we outline different approaches explaining relationships between carbon and energy markets. We also review the literature relating to these issues. Next, we further describe the fuel switching process and, in particular, we analyze the influence of energy and environmental efficiency of thermal power plants (coal and gas) on fuel switching. In Chapter 2, we provide a theoretical analysis that shows how differences in the efficiency of CCGTs can rule interactions between gas and carbon prices. The main result shows that the allowance price becomes more sensitive to the gas price when the level of CO2 emissions increases. In Chapter 3, we examine interactions between carbon, coal, gas and electricity prices in an empirical study. Among the main results, we find that there is a significant link between carbon and gas prices in the long-run equilibrium.In Chapter 4, we analyze the cross-market price discovery process between gas and CO2 markets. We identified in previous chapters that there is a robust significant link between gas and CO2 markets. They are linked commodities, and their prices are affected by the same information. In an empirical analysis, we find that the carbon market is the leader in cross-market price discovery process.

Le modèle GARCH à changement de régimes est le fondement de cette thèse. Ce modèle offre de riches dynamiques pour modéliser les données financières en combinant une structure GARCH avec des paramètres qui varient dans le temps. Cette flexibilité donne malheureusement lieu à un problème de path dependence, qui a empêché l'estimation du modèle par le maximum de vraisemblance depuis son introduction, il y a déjà près de 20 ans. La première moitié de cette thèse procure une solution à ce problème en développant deux méthodologies permettant de calculer l'estimateur du maximum de vraisemblance du modèle GARCH à changement de régimes. La première technique d'estimation proposée est basée sur l'algorithme Monte Carlo EM et sur l'échantillonnage préférentiel, tandis que la deuxième consiste en la généralisation des approximations du modèle introduites dans les deux dernières décennies, connues sous le nom de collapsing procedures. Cette généralisation permet d'établir un lien méthodologique entre ces approximations et le filtre particulaire. La découverte de cette relation est importante, car elle permet de justifier la validité de l'approche dite par collapsing pour estimer le modèle GARCH à changement de régimes. La deuxième moitié de cette thèse tire sa motivation de la crise financière de la fin des années 2000 pendant laquelle une mauvaise évaluation des risques au sein de plusieurs compagnies financières a entraîné de nombreux échecs institutionnels. À l'aide d'un large éventail de 78 modèles économétriques, dont plusieurs généralisations du modèle GARCH à changement de régimes, il est démontré que le risque de modèle joue un rôle très important dans l'évaluation et la gestion du risque d'investissement à long terme dans le cadre des fonds distincts. Bien que la littérature financière a dévoué beaucoup de recherche pour faire progresser les modèles économétriques dans le but d'améliorer la tarification et la couverture des produits financiers, les approches permettant de mesurer l'efficacité d'une stratégie de couverture dynamique ont peu évolué. Cette thèse offre une contribution méthodologique dans ce domaine en proposant un cadre statistique, basé sur la régression, permettant de mieux mesurer cette efficacité. / The Markov-switching GARCH model is the foundation of this thesis. This model offers rich dynamics to model financial data by allowing for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which has prevented maximum likelihood estimation of this model since its introduction, almost 20 years ago. The first half of this thesis provides a solution to this problem by developing two original estimation approaches allowing us to calculate the maximum likelihood estimator of the Markov-switching GARCH model. The first method is based on both the Monte Carlo expectation-maximization algorithm and importance sampling, while the second consists of a generalization of previously proposed approximations of the model, known as collapsing procedures. This generalization establishes a novel relationship in the econometric literature between particle filtering and collapsing procedures. The discovery of this relationship is important because it provides the missing link needed to justify the validity of the collapsing approach for estimating the Markov-switching GARCH model. The second half of this thesis is motivated by the events of the financial crisis of the late 2000s during which numerous institutional failures occurred because risk exposures were inappropriately measured. Using 78 different econometric models, including many generalizations of the Markov-switching GARCH model, it is shown that model risk plays an important role in the measurement and management of long-term investment risk in the context of variable annuities. Although the finance literature has devoted a lot of research into the development of advanced models for improving pricing and hedging performance, the approaches for measuring dynamic hedging effectiveness have evolved little. This thesis offers a methodological contribution in this area by proposing a statistical framework, based on regression analysis, for measuring the effectiveness of dynamic hedges for long-term investment guarantees.

Le modèle GARCH à changement de régimes est le fondement de cette thèse. Ce modèle offre de riches dynamiques pour modéliser les données financières en combinant une structure GARCH avec des paramètres qui varient dans le temps. Cette flexibilité donne malheureusement lieu à un problème de path dependence, qui a empêché l'estimation du modèle par le maximum de vraisemblance depuis son introduction, il y a déjà près de 20 ans. La première moitié de cette thèse procure une solution à ce problème en développant deux méthodologies permettant de calculer l'estimateur du maximum de vraisemblance du modèle GARCH à changement de régimes. La première technique d'estimation proposée est basée sur l'algorithme Monte Carlo EM et sur l'échantillonnage préférentiel, tandis que la deuxième consiste en la généralisation des approximations du modèle introduites dans les deux dernières décennies, connues sous le nom de collapsing procedures. Cette généralisation permet d'établir un lien méthodologique entre ces approximations et le filtre particulaire. La découverte de cette relation est importante, car elle permet de justifier la validité de l'approche dite par collapsing pour estimer le modèle GARCH à changement de régimes. La deuxième moitié de cette thèse tire sa motivation de la crise financière de la fin des années 2000 pendant laquelle une mauvaise évaluation des risques au sein de plusieurs compagnies financières a entraîné de nombreux échecs institutionnels. À l'aide d'un large éventail de 78 modèles économétriques, dont plusieurs généralisations du modèle GARCH à changement de régimes, il est démontré que le risque de modèle joue un rôle très important dans l'évaluation et la gestion du risque d'investissement à long terme dans le cadre des fonds distincts. Bien que la littérature financière a dévoué beaucoup de recherche pour faire progresser les modèles économétriques dans le but d'améliorer la tarification et la couverture des produits financiers, les approches permettant de mesurer l'efficacité d'une stratégie de couverture dynamique ont peu évolué. Cette thèse offre une contribution méthodologique dans ce domaine en proposant un cadre statistique, basé sur la régression, permettant de mieux mesurer cette efficacité. / The Markov-switching GARCH model is the foundation of this thesis. This model offers rich dynamics to model financial data by allowing for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which has prevented maximum likelihood estimation of this model since its introduction, almost 20 years ago. The first half of this thesis provides a solution to this problem by developing two original estimation approaches allowing us to calculate the maximum likelihood estimator of the Markov-switching GARCH model. The first method is based on both the Monte Carlo expectation-maximization algorithm and importance sampling, while the second consists of a generalization of previously proposed approximations of the model, known as collapsing procedures. This generalization establishes a novel relationship in the econometric literature between particle filtering and collapsing procedures. The discovery of this relationship is important because it provides the missing link needed to justify the validity of the collapsing approach for estimating the Markov-switching GARCH model. The second half of this thesis is motivated by the events of the financial crisis of the late 2000s during which numerous institutional failures occurred because risk exposures were inappropriately measured. Using 78 different econometric models, including many generalizations of the Markov-switching GARCH model, it is shown that model risk plays an important role in the measurement and management of long-term investment risk in the context of variable annuities. Although the finance literature has devoted a lot of research into the development of advanced models for improving pricing and hedging performance, the approaches for measuring dynamic hedging effectiveness have evolved little. This thesis offers a methodological contribution in this area by proposing a statistical framework, based on regression analysis, for measuring the effectiveness of dynamic hedges for long-term investment guarantees.