In this thesis I study the investment climate in the four rapidly growing emerging economies Brazil, Russia, India and China (BRIC). The first study, Chapter 2, uses a bivariate EGARCH methodology with time varying conditional correlation to study the global and regional integration of the BRICs and to identify the existence of diversification opportunities for international investors. The second study, Chapter 3, employs a restricted version of the model to explore the relationship between equity market returns and volatility of equity returns in the BRIC countries and global oil prices. Chapter 4 is an extension of Chapter 3, and focuses on the sustainability of Russia???s economic growth in view of its large dependence on oil income. A qualitative analysis of the oil industry in Russia, including an overview of the operations of the largest oil producing companies, government regulations, oil production and proven oil reserves, is conducted for the purpose of this study. The last study, Chapter 5, uses a panel data methodology to explore the determinants of changes in sovereign bond spreads for the BRICs as an asset class and for each of the BRIC countries individually. I conclude that the regional and global level of integration of the BRICs is relatively low, and portfolio investors can enjoy sound diversification benefits particularly by taking investment positions in the Indian and Chinese equity markets. Despite the aggressive economic growth of the BRICs and their increased oil consumption, the volatility of stock returns from the BRICs does not have a significant impact on global oil prices; however, oil prices do impact the volatility of equity returns in India and China, and particularly the level of returns and volatility of equity returns in Russia. Based on this and the qualitative analysis in Chapter 4, it is concluded that in the short to medium term Russia???s continued economic growth will depend on increased reinvestment in the oil industry and in the longer term the government should diversify its revenue sources and focus on development of other sectors within the economy. Lastly, it is concluded that sovereign risk in the BRICs is driven by different global and country-specific factors, hence risk should be observed on an individual country basis and not for the BRICs as an asset class.

With the acceleration of economic globalization, the immature Chinese mainland stock market is gradually associated with the stock markets of other countries. This paper predict the return rate of Chinese mainland stock market using several models from GARCH family, test the predictability by calculating Value-at-Risk, also capture the dynamic correlation between other fifive countries or region and mainland China by DCC-GARCH model. The results indicate that E-ARMA-GARCH model fifits the best due to the signifificant heteroscedasticity and leverage effect of Chinese mainland stock market. It has the strongest positive correlation with HongKong while the weakest correlation with the United States.

La présente thèse s'est inscrite dans une perspective d'améliorer les outils de mesure du risque de marché en proposant des solutions capables de reproduire certaines caractéristiques empiriques d'évolution des marchés financiers. A travers une étude empirique sur des données réelles, nous montrons que la réalité des marchés financiers possède certaines caractéristiques empiriques connues et résumées sous le nom "faits stylisés", qui rendent les mesures usuelles du risque de marché incapables de reproduire ces caractéristiques. Nous proposons des nouvelles méthodes de mesure de la Value-at-Risk (VaR), en fonction de la volatilité passée et des corrélations existant entre les actifs composant un portefeuille, dans le cadre de deux grandes approches de mesure du risque : une approche de mesure du risque global (ou risque univarié) et une approche de mesure du risque multiple (ou risque multivarié), tout en testant leur qualité prédictive au moyen des procédures de backtesting. Les résultats obtenus montrent une grande capacité des différentes mesures utilisées à capturer les faits stylisés caractérisant l'évolution des marchés financiers étudiés avec une nette surperformance des méthodes de mesure de la VaR estimées dans le cadre du risque multivarié par rapport à celles du risque univarié. / This thesis has object to improve the methods for estimating market risk by offering solutions capable to replicate some empirical properties of asset returns. Through an empirical study on real data, we show that the reality of financial markets has some empirical characteristics known and summarized as "stylized facts" that render the conventional market risk measurement unable to reproduce. We propose a Value-at-Risk (VaR) measures, based on modeling portfolio volatility and correlations between assets classes, using two risk measurement approaches: an univariate risk measurement approach and multivariate risk measurement approach, and testing their quality predictive using backtesting procedures. The results obtained show a great ability of different used risk measurement to capture the stylized facts characterizing financial markets, with a clear outperformance of the multivariate VaR measures than the univariate VaR measures.

Cette thèse de doctorat est composée de trois chapitres, un article et deux papiers et est principalement liée au domaine de l’économétrie financière empirique. Elle analyse la dépendance et le lien entre les marchés financiers et les marchés de matières premières, en particulier celui de l’énergie. Les distributions et corrélations des variables appartenant aux deux marchés sont étudiées afin de déterminer leurs effets les uns sur les autres et d’analyser leurs tendances pour donner un meilleur aperçu de leurs comportements vis-à-vis des crises et des événements brusques en économie. Ces variables sont représentées par certains indices financiers (SP500, Euro stoxx 50, Msci China) ainsi que par les principaux indices de matières premières (SP GSCI, Brent Oil,Gaz naturel, Metaux precieux). Nous choisissons de modéliser leur corrélation dans le temps et de prendre en compte la non-linéarité et l’instabilité qui peuvent les affecter. Pour cela, l’approche fonction copule a été employée pour modéliser d’une manière efficace leurs distributions. Dans le premier chapitre, nous examinons la dépendance et les co-mouvements entre les prix des émissions de dioxyde de carbone et les indices énergétiques comme le charbon, le gaz naturel, le Brent oil et l’indice énergétique global. Le deuxième chapitre analyse les interactions et relations entre le marché pétrolier et deux principaux marchés financiers en Europe et aux États-Unis représentés par l’Euro stoxx 50 et le SP500. Dans le dernier chapitre, on analyse la dépendance multivariée entre les indices de matière première de différents secteurs avec des indices financiers en utilisant le modèle de la copule Regular Vine. / This Ph.D. thesis is composed by three chapters and is mainly related to theempirical financial econometrics field. It analysis the dependence and correlationbetween the financial markets and the commodity markets specially energy.Variables from both markets are studied to determine their effects on each othersand to analyse their trends to giva a better insight to their co-movements.These variables are represented by some of the major equities (SP500, Eurostoxx 50, Msci China) as well as major commodities indices (SP GSCI commodity,Brent Oil, Natural Gas, Precious metals). We choose to model theircorrelation dynamically and take into account any non-linearity and stylisedfacts into the nature of their dependencies. For that, the copula approach wasused to model efficiently the correlated joint distributions of the studied variables.In the first paper, we examine the dependence and co-movements between theprices of the carbon dioxide emissions and energy commodities (coal, naturalgas, Brent oil and SP GSCI energy index). The dependence between thereturns was modeled by a particular class of dynamic copula, the StochasticAutoregressive Copula (SCAR). The second chapter analysis the interactions and co-movements between the oilmarket and two major stock markets in Europe and the US (the Euro stoxx 50and the SP500). Both the dynamic and the markov (regime switching) copulawere chosen to better understand the link between the two. In The last paper, I study the multivariate dependence between commoditiesfrom different sectors with some major equities using the Regular Vine copula model.

Markets have always changed in response to either exogenous or endogenous shocks. Many large events have occurred in financial and energy markets the last ten years. This dissertation examines market behavior and volatility in agricultural credit and fossil fuel markets under exogenous and endogenous changes in the last ten years. The efficiency of elements within the United States Farm Credit System, a major agricultural lender in the United States, and the dynamic correlation between coal, oil and natural gas prices, the three major fossil fuels, are examined. The Farm Credit system is a key lender in the U.S. agricultural sector, and its performance can influence the performance of the agricultural sector. However, its efficiency in providing credit to the agricultural sector has not been recently examined. The first essay of the dissertation provides assessments on the performance of elements within the Farm Credit System by measuring their relative efficiency using a stochastic frontier model. The second essay addresses the changes in relationship in coal, oil, and natural gas markets with respect to changes and turbulence in the last decade, which has also not been fully addressed in literature. The updated assessment on the relative performance of entities within the Farm Credit System provides information that the Farm Credit Administration and U.S. policy makers can use in their management of and policy toward the Farm Credit System. The measurement of the changes in fossil fuel markets’ relationships provides implications for energy investment, energy portfolio anagement, energy risk management, and energy security. It can also be used as a foundation for structuring forecasting models and other models related to energy markets. The dynamic correlations between coal, oil, and natural gas prices are examined using a dynamic conditional correlation multivariate autoregressive conditional heteroskedasticity (MGARCH DCC) model. The estimated results show that the FCS’s five banks and associations with large assets have more efficiently produced credit to the U.S. agricultural sector than smaller sized associations. Management compensation is found to be positively associated with the system’s efficiency. More capital investment and monitoring along with possible consolidation are implied for smaller sized associations to enhance efficiency. On average, the results show that the efficiency of the associations is increasing over time while the average efficiency of the five large banks is more stable. Overall, the associations exhibit a higher variation of efficiency than the five banks. In terms of energy markets the estimates from the MGARCH DCC model indicate significant and changing dynamic correlations and related volatility between the coal, oil, and natural gas prices. The coal price was found to experience more volatility and become more closely related to oil and natural gas prices in recent periods. The natural gas price was found to become more stable and drift away from its historical relationship with oil.

This paper uses multivariate DCC-GARCH model to investigate the volatility of dynamic correlation between MSCI Taiwan stock index and the USA, China, Japan , Asia and global stock market. The existence of stock market volatility asymmetry, volatility spread of infection and clustering effects also are analysed, while in case of the U.S. sub-prime mortgage crisis and triggered the global financial tsunami. It discusses the Taiwan stock market fluctuations and structural changes in the international markets and the market dynamics related to change of influence and change. The main findings are (1)The volatility of continuity between the spread of infection and the clustering effect between the Taiwan stock market and international market fluctuations, (2) During the global financial tsunami, the correlation between changes in the international market and the market Correlation of different dynamic fluctuations and structural changes occurring in different time point also show the impact of changes of individual markets (3)The correlation between MSCI Taiwan stock index and the USA, China , Japan, indicates that the impact of change of stock the Japanese stock market on the MSCI Taiwan stock index is low, while China and the MSCI Taiwan stock index-related enhances, (4) market structure changes, the MSCI Taiwan stock index and the global dynamic fluctuations in the market is still a significant, The visible impact of the shock oscillation is wide and return to equilibrium of adjustment is still ongoing.

Focusing on Environmental, Social and Governance (ESG) responsible investments, this study examines the historical and forecasted volatility and dynamic correlations between Emerging Markets in Europe, Asia and Latin America. By complementing the previous studies that provide evidence for how high ESG-ratings can reduce volatility in stock prices, regardless of which market, we seek to find if this is true in Emerging Markets. We additionally incorporate an analysis of dynamic correlations between Emerging Markets to see potential diversification benefits, which can be crucial in risk management. Data selection is based on daily closing prices of six different Emerging Markets indices. Three indices capturing the traditional Emerging Markets and three more only consisting of firms with a high ESG-rating, considered to be ESG Leaders. The sampled period is between January 2020 to January 2024. Data was processed through the DCC- GARCH(1,1) model to measure historical and forecasted volatility and dynamic correlations. The model uses past information to predict future values, meaning that past volatility and correlations influence forecasted volatility and correlations. This allows for a nuanced understanding of how the volatility and correlations have evolved and how they are forecast to change between these Emerging Markets. Key findings suggest that Asia can work as the diversification benefactor, as it is the least volatile Emerging Market and the ESG Leaders in Asia are showing a lower dynamic correlation with the ESG Leaders in the other Emerging Markets. Further results indicate that Europe is the most volatile Emerging Market, including the ESG Leaders. Furthermore, ESG Leaders in Europe and Latin America were seen to have the best DCC-GARCH filtered daily returns, while also having the highest dynamic correlation. This means that a portfolio with these two assets tends to be more volatile as shocks in daily returns move in tandem.

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods.

Cette thèse se propose d’analyser la structure et la dynamique de dépendance de crédit des institutions financières aux Etats-Unis et en Europe durant la crise financière de 2008. Un premier chapitre présente une revue de la littérature des modèles multi-dimensionnels de crédit et des modèles économétriques de contagion financière. Ce chapitre a pour vocation de guider notre réflexion à la fois conceptuelle et méthodologique sur les hypothèses analytiques de la contagion ainsi que ses méthodes de mesure. Nous montrons que si la contagion est devenue une hypothèse centrale des modèles multivariés de risque de crédit, il n’en reste néanmoins que sa définition et sa quantification ne font pas l’objet de consensus dans la littérature. Un deuxième chapitre propose une analyse empirique des co-movements des rendements de CDS de banques et sociétés d’assurance américaines et européennes. La dissociation de leur structure de dépendance entre association linéaire et dépendances extrêmes nous permet de mettre en évidence des phénomènes d'interconnexions entre institutions financières apparues au courant de la crise et véhiculant ainsi sous l'effet de la contagion, un risque systémique croissant. Un dernier chapitre présente une interprétation économique des résultats obtenus dans notre deuxième chapitre. En particulier, nous cherchons à quantifier l'influence jouée par la contagion et les facteurs de risques communs sur la dynamique de dépendance extrême des institutions financières. Nous démontrons ainsi le rôle du risque de contrepartie, du risque de liquidité et du risque de défaut des institutions financières dans la transmission de la contagion sur le marché de CDS. / This thesis examines the credit dependence structure and dynamics of financial institutions in the U.S. and Europe amid the recent financial crisis. A first chapter presents a survey of multi-name models of credit risk and econometric models of financial contagion with the purpose of guiding both the analytical and conceptual assumptions and econometric modelling techniques we use in the subsequent chapters. We show that if contagion has become a central cornerstone of multi-name models of credit risk, there is nonetheless a lack of consensus on the way to both define and measure it. A second chapter presents the results of an empirical analysis of U.S. and European banks and insurance companies’ CDS return extreme co-movements. By uncovering financial institutions' linear as well as extremal dependence structures, we provide evidence that their credit dependence has strengthened during the crisis, thereby effectively conveying, in the face of extreme tail events, potential systemic risks. A third and last chapter provides an economic rationale of the results presented in our second chapter. In particular, we examine the impact of common risk factors and contagion on the dynamics of financial institutions' extremal credit dependence. We demonstrate the role of counterparty risk and liquidity risk, as well the repricing by market participants since July 2007 of their jump-to-default premia as additional channels driving financial institutions' increased dependence and amplifying contagion on the CDS market.