This paper studies the dependence structure between the real estate and financial markets in the United States from roughly 1975 to 2010, including the stock, bond and foreign exchange markets. This analysis uses dynamic copulas, including the Gaussian, Gumbel and Clayton copula. The Gumbel and Clayton copulas are used to separately capture the tail dependence of data. The dependence between the property indices (HPI and NCREIF) and the three financial markets is analyzed using the parameters of the copula. The property indices are divided in two different ways: by different regions and by different types of real estate. Although we study the dependence between the real estate and the financial markets in the U.S., the main objective of this paper is to analyze the change in the dependence structure when financial disasters occur. This study indicates that the real estate and the stock markets were positively related during this time period, and this dependence drove extreme movement when financial crises occurred. This dependence differed depending on the type of financial crisis, such as the Internet bubble crisis or the financial crisis in 2008. The dependence between the real estate and bond markets was also positively related, and extreme movement also occurred during financial crises. As for the dependence between the real estate and foreign exchange markets, although the results shows that dependence decreased when financial crises occurred, this is because the value of U.S. dollars are opposite to those of the index, and the left tail dependence exists as previous result. When looking at different regions or types of property, the differences in dependence structure were not obvious, although they were positively related. Both right and left tail dependences existed for most regions and property types, although some regions or types showed either right or left tail dependences alone. Therefore, investors should focus on the relationship between different markets, not on the region or type of real estate.

The objectives of the thesis - which comprises six parts – can be summarized in i) implementing linear and nonlinear/nonparametric approaches toward detecting, measuring and analyzing the nature and directionality of causal relationships in financial markets, ii) elaborating on modern topics in financial investment analysis, iii) probing into the role of commodity futures in constructing optimal portfolios as well as iv) investigating growth dynamics via aggregated and disaggregated indices. The first paper named “Analyzing causal interactions between sectoral equity returns and commodity futures returns in the aftermath of the global financial crisis: The case of the US and EU equity returns”, aims to explore and compare the dependence and co-movement structure between commodity and various asset classes’ returns including the USA and EU stock markets via the use of linear and non-linear causality testing in a comparative context with the additional adjustment for cointegration and conditional heteroscedasticity. The findings provide important implications for optimal asset allocation and portfolio diversification with respect to various market conditions, namely both in “good” and “bad” (crisis) times. The second paper is entitled “On the time scale behaviour of Equity-Commodity links: Implications for Portfolio Management”, and has been published in the Journal of International Financial Markets, Institutions and Money (2016). The study is co-authored with Professors S. Bekiros, D.K. Nguyen, and B. Sjö. It develops a holistic framework for the investigation of the multi-horizon and intra-frequency causal directionalities of various asset classes, by means of multi-resolution analysis. The results verify the assumption that financial markets exhibit time-varying co-movement patterns, which are fundamentally important in a) generating profitable trading strategies according to different investor horizon expectations and b) decoding the financialization mechanism across various asset classes. The third paper entitled “Business Cycle (de) Synchronization in the aftermath of the Global Financial Crisis: Implications for the Euro Area”, was published at Studies in Nonlinear Dynamics and Econometrics (2015) and is co-authored with S. Bekiros, D.K Nguyen and B. Sjö. In this work, the scale-dependent time-varying (de)synchronization effects between the Eurozone and the broad Euro area business cycles are revealed, before and after the global financial crisis. The results, which point towards an increased observed comovement during the crisis period for the Euro area, could be catalytic for the introduction of a more efficient monetary policy by EU institutions and in particular by the European Central Bank. In the fourth paper, “Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach”, which was published in the International Review of Economics and Finance (2016) and co-authored with J.C. Reboredo, the financial and policy uncertainty is investigated in relation to the price dynamics of energy and metal commodity futures’ markets. This work lead to the analysis of the asymmetric interrelationships with respect to changes in the perceptions of various risk measures, covering various periods, i.e., “normal” vs. “turbulent” such as upward or downward market episodes. The fifth paper, co-authored with P. Andreasson, S. Bekiros and D.K. Nguyen, is entitled “The impact of speculation and economic uncertainty on commodity markets”, and is published in the International Review of Financial Analysis (2016). This paper attempts a novel methodological approach to measuring speculation in commodity markets, in particular whether market speculation drives agricultural commodity prices or viceversa. The assessment of the empirical analysis demonstrates that agricultural prices are not affected by speculation. Finally, the sixth paper “Energy and Output Dynamics in Bangladesh”, co-authored with B.P. Paul, was published in Energy Economics (2011) and explores the relationship between energy utilization and economic growth in Bangladesh. Specifically, it deals with the important issue of whether energy consumption can be reduced without affecting economic growth while at the same time implicitly may lead to poverty reduction. The findings substantiate the fact that a) energy usage has become more efficient in recent times, as well as indicate that b) fluctuations in energy consumption did not have a significant impact on economic output.

信用衍生性商品於近十年來已快速發展，為反映信用風險管理的迫切需求，本篇論文將以實證的方式探討信用衍生性市場。尤其著重在信用違約交換市場，因其佔信用衍生性市場的交易量高達45％。本篇論文分別討論以下二個議題：第一個議題乃在探討股票報酬率的峰態係數與信用違約交換報酬率的關係。第二個議題乃著重探討拉丁美洲國家的信用違約交換對阿根廷事件的反應。 / The development of credit derivatives in the past decade has brought about pronounced innovations in the markets. To reflect dramatic demand in managing credit risk, this thesis dedicates to the empirical world of credit derivatives markets. We especially focus on Credit Default Swaps (CDS) market due to its most widely trading in credit derivatives markets, capturing almost 45% of the market shares. This thesis encompasses two essays related to CDS. In the first essay, we attempt to extend empirical explanation of CDS premiums by considering the excess kurtosis of equity return distribution. As well, we show how copula functions can be applied to specify both the dependence structure and the tail relationship between CDS return and kurtosis of equity distribution. We contribute to the better specification of the dependence structure between the CDS return and the corresponding kurtosis, and provide an illustration of its implication which may be misled using conventional methods. In the second essay, we turn to focus on CDS in emerging markets. Thereby, further policy-oriented applications for governments can be extra induced. We empirically study the correlated default at sovereign level in Latin America region due to the eruption of Argentina debt crisis in 2001. A comprehensive understanding of correlated default at sovereign level is of critical importance in several respects. From the government and IMF point of views, the comovement in sovereign credit default swaps can serve as one of the leading indicators of financial crises. From the perspectives of mutual funds and banks, correlated movement which exists in sovereign CDS spreads is regarded as one of the measures of country risk premium. The findings and the associated methodology can provide useful insights not only to policymakers but also to whoever is interested in credit derivatives markets, particularly in emerging markets. From the methodology point of view, applying a copula method to identify the contagion corresponds to the arguments from Bae et. al. (2003) and Dungey and Tambakis (2003), the further challenge is to develop a model for capturing the nonlinear property.

In the new context of major public emergencies, this paper will mainly study the measurement and influencing factors of systemic risks in Chinese financial institutions based on three dimensions: overall situation, industries, and institutions. First, it uses the DTW-MST network model to describe the dependence structure between financial institutions and between industries. It explores important institutional nodes of risk dependence from a network perspective. Then, it uses the time-varying Copula-CoVaR model to measure financial institutions' and industries' risk spillover effect on the whole financial system and analyze the characteristics and differences of risk spillover. Last, it uses the panel regression model to study the influencing factors of the risk spillover effect of financial institutions and explore the sources of systemic risks. The results show that: (1) Industrial Bank (CIB), Changjiang Securities (CJSC), and China Pacific Insurance (CPIC) are the central nodes of the banking, securities, and insurance industries, respectively. (2) The risk spillover effect is characterized by a significant asymmetry and thick tail, and negative news has a greater impact on the risk spillover effect. (3) The value at risk (VaR) and volatility of financial institutions have a significant positive correlation with the risk spillover effect, while the size of financial institutions has a significant negative correlation with the risk spillover effect. / Business Administration/Finance

Understanding financial asset return correlation is a key facet in asset allocation and investor’s portfolio optimization strategy. For the last decades, several studies have investigated this relationship between stock and bond returns. But, fewer studies have dealt with multi-asset return dynamics. While initial literature attempted to understand the fundamental pattern of comovements, later studies model the economic state variables influencing such time-varying comovements of primarily stock and bond returns. Research widely acknowledges that return distributions of financial assets are non-normal. When the joint distributions of the asset returns follow a non-elliptical structure, linear correlation fails to provide sufficient information of their dependence structure. In particular two issues arise from this existing empirical evidence. The first is to propose a more reliable alternative density specification for a higher-dimensional case. The second is to formulate a measure of the variables’ dependence structure which is more instructive than linear correlation. In this work I use a time-varying conditional multivariate elliptical and non-elliptical copula to examine the return comovements of three different asset classes: financial assets, commodities and real estate in the US market. I establish the following stylized facts about asset return comovements. First, the static measures of asset return comovements overestimate the asset return comovements in the economic expansion phase, while underestimating it in the periods of economic contraction. Second, Student t-copulas outperform both elliptical and non-elliptical copula models, thus confirming the ii dominance of Student t-distribution. Third, findings show a significant increase in asset return comovements post August 2007 subprime crisis ... [cont.].

Understanding financial asset return correlation is a key facet in asset allocation and investor’s portfolio optimization strategy. For the last decades, several studies have investigated this relationship between stock and bond returns. But, fewer studies have dealt with multi-asset return dynamics. While initial literature attempted to understand the fundamental pattern of comovements, later studies model the economic state variables influencing such time-varying comovements of primarily stock and bond returns. Research widely acknowledges that return distributions of financial assets are non-normal. When the joint distributions of the asset returns follow a non-elliptical structure, linear correlation fails to provide sufficient information of their dependence structure. In particular two issues arise from this existing empirical evidence. The first is to propose a more reliable alternative density specification for a higher-dimensional case. The second is to formulate a measure of the variables’ dependence structure which is more instructive than linear correlation. In this work I use a time-varying conditional multivariate elliptical and non-elliptical copula to examine the return comovements of three different asset classes: financial assets, commodities and real estate in the US market. I establish the following stylized facts about asset return comovements. First, the static measures of asset return comovements overestimate the asset return comovements in the economic expansion phase, while underestimating it in the periods of economic contraction. Second, Student t-copulas outperform both elliptical and non-elliptical copula models, thus confirming the ii dominance of Student t-distribution. Third, findings show a significant increase in asset return comovements post August 2007 subprime crisis ... [cont.].

[pt] A preocupação com o aquecimento global e a poluição tem aumentado significativamente o interesse no desenvolvimento de fontes renováveis de energia. Este estudo tem como eixo principal a energia eólica, o uso dessa energia elimina resíduos indesejados e prejudiciais à saúde e ao meio ambiente causados por outras fontes de energia, como carvão e usinas nucleares. Este trabalho objetiva analisar a relação de dependência entre a velocidade do vento e a geração de energia eólica, esta é uma relação bastante complexa, por isso busca-se entender a natureza estocástica de ambas as variáveis. Como ferramenta metodológica foi utilizada a teoria da cópula. O estudo baseia-se na análise e modelagem da dependência entre dados de velocidade do vento e geração de energia eólica, para um banco de dados horário de um parque eólico do estado da Bahia, no período de janeiro a dezembro de 2017, após encontrar a cópula correspondente a estrutura de dependência para o ano completo e para cada mês individualmente, foram geradas simulações e apresentadas as probabilidades de ocorrência dos cenários em intervalos pré-definidos, os resultados obtidos foram significativos, testes estatísticos adequados foram realizados, evidenciando a qualidade do ajuste. / [en] Concern about global warming and pollution has significantly increased interest in developing renewable energy sources. This study has wind energy as its main axis, the use of this energy eliminates unwanted and harmful waste to health and the environment caused by other energy sources, such as coal and nuclear power plants. This work aims to analyze the dependence relationship between wind speed and wind energy generation, this is a very complex relationship, so we seek to understand the stochastic nature of both variables. As a methodological tool, the copula theory was used. The study is based on the analysis and modeling of the dependence between wind speed data and wind energy generation, for an hourly database of a wind farm in the state of Bahia, from January to December 2017, after finding the copula corresponding to the dependency structure for the entire year and for each month individually, simulations were generated and the probabilities of occurrence of the scenarios were presented at pre-defined intervals, the results obtained were significant, adequate statistical tests were performed, evidencing the quality of the fit .

Copulas functions are a flexible tool for modelling the dependence structure between variables. The joint and marginal distributions of Copulas are not constrained by the assumptions of normality. This study examines the dependence structure between the gold, platinum prices and the ZAR/U.S.D exchange rate using Copulas. The study found that marginal distributions of Copulas follows the ARMA (1, 1)-EGARCH (1, 1) and ARMA(1, 1)-APARCH (1, 1) models under different error terms including the normal, the student-t and the skew student-t error terms. It used the Normal, the Student-t, the Gumbel, the rotated Gumbel, the Clayton, the rotated Clayton, the Plackett, the Joe Clayton and the Normal time varying Copulas to analyse the dependence structure between returns prices of gold, platinum and ZAR/U.S.D exchange rate. The results showed evidence of a positive strong dependence between the returns prices of gold, platinum and returns on the Rand/U.S.D exchange rate for constant and time varying Copulas. The result also showed a co-movement of exchange rates and gold and platinum prices during a rise or declining prices of gold and platinum. The results imply that fluctuations in gold and platinum prices generate Rand/U.S.D exchange rate volatility. / Statistics / M. Sc. (Statistics)

Submitted by Eitan Chernizon (eitan.chernizon@sgcib.com) on 2013-02-15T17:46:24Z No. of bitstreams: 1 MODELAGEM DA DEPENDÊNCIA ENTRE FATORES DE CRÉDITO E MERCADO.pdf: 1474762 bytes, checksum: 19b13b065762c89e556619042eaf016d (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-02-18T12:58:52Z (GMT) No. of bitstreams: 1 MODELAGEM DA DEPENDÊNCIA ENTRE FATORES DE CRÉDITO E MERCADO.pdf: 1474762 bytes, checksum: 19b13b065762c89e556619042eaf016d (MD5) / Made available in DSpace on 2013-02-18T13:20:44Z (GMT). No. of bitstreams: 1 MODELAGEM DA DEPENDÊNCIA ENTRE FATORES DE CRÉDITO E MERCADO.pdf: 1474762 bytes, checksum: 19b13b065762c89e556619042eaf016d (MD5) Previous issue date: 2013-02-01 / Apesar das recentes turbulências nos mercados, a utilização de derivativos negociados fora de uma câmara de compensação tem apresentado rápido crescimento, constituindo um dos maiores componentes do mercado financeiro global. A correta inclusão da estrutura de dependência entre fatores de crédito e mercado é de suma importância no apreçamento do risco de crédito adjacente a exposições geradas por derivativos. Este é o apreçamento, envolvendo simulações de Monte Carlo, feito por uma instituição negociante para determinar a redução no valor do seu portfólio de derivativos devido a possibilidade de falência da contraparte. Este trabalho apresenta um modelo com abordagem paramétrica para lidar com a estrutura de dependência, intuitivo e de fácil implementação. Ao mesmo tempo, os números são contrastados com os resultados obtidos através de uma abordagem neutra ao risco para um portfólio replicante, sob o mesmo processo estocástico. O modelo é aplicado sobre um contrato a termo de câmbio, e diferentes cópulas e fatores de correlação são utilizados no processo estocástico. / Despite recent turmoils, the use of derivatives traded outside of a clearinghouse has shown rapid growth and is a major component of the global financial market. The correct inclusion of the dependence structure between market and credit factors is of high importance in the pricing of credit risk exposures generated by the adjacent derivatives. This pricing, involving Monte Carlo simulations, is done by a dealer to determine the reduction in the value of its derivatives portfolio because of the bankruptcy of the counterparty. This paper presents a model with parametric approach to deal with the dependence structure, intuitive and easily implemented. Meanwhile, the numbers are contrasted with results obtained using a risk neutral approach for a replicating portfolio under the same stochastic process. The model is applied on a forward exchange contract, and different copulas and correlation factors are used in the stochastic process.

Made available in DSpace on 2014-08-20T14:36:59Z (GMT). No. of bitstreams: 1 Dissertacao_Leandro_Sanzi_Aquino.pdf: 2633860 bytes, checksum: eeb09c0678ebe75556f513e8a4e089b7 (MD5) Previous issue date: 2010-02-25 / Soil land leveling is a technique used in low land areas and has the objective to improve agricultural use to facilitate the management of water both for irrigation and drainage operations, for the establishment of agricultural practices and crop harvest. However, it causes changes in the physical environment where the plant grows, and many studies have sought to identify the effect of this practice in the structure of soil spatial variability and in the relationship between the hydric-physical and chemical soil attributes. Thus, the objective of this study was to identify and characterize the structure of spatial variability of soil hydric-physical and chemical attributes of a low land soil, before and after land leveling, and to study the relationship between these soil attributes through an autoregressive state space model. In an experimental area of 0.81 ha belongs to Embrapa Clima Temperado situated in Capão do Leão county, state of Rio Grande do Sul, Brazil, was established a regular grid of 100 points spaced 10 m apart in both directions. At each point, soil disturbed and undisturbed samples were collected at the depth of 0-0.20 m to determine, before and after land leveling, the following soil attributes: clay, silt and sand contents, soil macroporosity, soil microporosity and soil total porosity, soil bulk density and soil water content at field capacity and permanent wilting point, soil organic carbon and cation exchange capacity. All data sets were organized into a spreadsheet in the form of a spatial transect consisting of 100 points and they were ordered following the gradient slope area resulting from the soil land leveling. Autocorrelograms and crosscorrelograms were built to evaluate the structure of spatial correlation of all soil attributes having served as a subsidy for the selection of variables in each autoregressive state-space model. The results show that the soil land leveling changed the structure of soil spatial dependence of all variables and between them as well. The soil cation exchange capacity and soil microporosity variables were the variables that made up the largest number of state space models, before and after soil land leveling. The contribution of the each variable at position i-1 to estimate its value at position increased to the sand content, silt content, soil bulk density, soil microporosity, soil macroporosity, soil water content at permanent wilting point, soil organic carbon and cation exchange capacity variables and decreased to soil water content at field capacity variable after land leveling. Soil land leveling improved the state space model performance for soil organic carbon content, sand content, soil bulk density, soil total porosity and soil water content at field capacity and permanent wilting point variables. The worst state space model performances, after soil land leveling, were found taking silt content, soil microporosity and cation exchange capacity variables as response variables. The best state space model performance, before land leveling, was obtained taking the soil total porosity as response variable. / A sistematização do solo é uma técnica utilizada em regiões planas, com características de várzea, e tem por objetivo aperfeiçoar o uso agrícola facilitando o manejo da água tanto de irrigação como de drenagem, as operações de implantação da lavoura, de tratos culturais e de colheita. No entanto, a sistematização do solo provoca alterações no ambiente físico onde a planta se desenvolve, sendo que muitos estudos têm buscado identificar o efeito dessa prática na estrutura de variabilidade espacial e no relacionamento entre os atributos físico-hídricos e químicos do solo. Dessa forma, o objetivo deste trabalho foi identificar e caracterizar a estrutura de variabilidade espacial dos atributos físico-hídricos e químicos de um solo de várzea, antes e depois da sistematização, assim como estudar o relacionamento entre esses atributos por meio de um modelo autoregressivo de espaço de estados. Em uma área experimental de 0,81 ha pertencente a Embrapa Clima Temperado, Capão do Leão-RS, foi estabelecida uma malha regular de 100 pontos, espaçados de 10 m entre si em ambas as direções. Em cada ponto foram coletadas amostras de solo deformadas e com estrutura preservada na profundidade de 0-0,20 m para a determinação, antes e depois da sistematização, dos teores de argila, silte e areia, macroporosidade, microporosidade e porosidade total, densidade do solo, conteúdo de água retido na capacidade de campo e ponto de murcha permanente, carbono orgânico e capacidade de troca de cátions. Os dados foram organizados em uma planilha de cálculo na forma de uma transeção espacial composta de 100 pontos e foram ordenados seguindo o gradiente de declividade da área resultante do processo de sistematização do solo. Para avaliar a estrutura de correlação espacial foram construídos autocorrelogramas e crosscorrelogramas que serviram de subsídio para a seleção de variáveis em cada um dos modelos autoregressivos de espaço de estados. Os resultados mostram que a sistematização do solo alterou a estrutura de dependência espacial tanto da variável como entre as variáveis deste estudo. A capacidade de troca de cátions e a microporosidade do solo foram as variáveis que compuseram o maior número de modelos de espaço de estados, antes e depois da sistematização. A contribuição da variável na posição i-1 na estimativa na posição i, por meio do modelo autoregressivo de espaço de estados, aumentou com a sistematização para as variáveis teor de areia, teor de silte, densidade do solo, microporosidade, macroporosidade, conteúdo de água no solo retido no ponto de murcha permanente, carbono orgânico e da capacidade de troca de cátions; e diminuiu para a variável conteúdo de água no solo retido na capacidade de campo.A sistematização do solo melhorou a estimativa, por meio dos modelos de espaço de estados, das variáveis carbono orgânico, teor de areia, densidade do solo, macroporosidade e do conteúdo de água no solo retido na capacidade de campo e no ponto de murcha permanente, sendo o modelo da variável porosidade total, antes da sistematização, que apresentou o melhor desempenho. Já os piores desempenhos dos modelos, depois da sistematização do solo, foram encontrados quando utilizadas as variáveis teor de silte, microporosidade e capacidade de troca de cátions como resposta.