在全球企業自由化及國際化的過程，金融機構的併購行為也變成大眾關注的議題，本論文第一部份，以亞洲的金融業為樣本，並且考量金融風暴對金融業併購的影響，實證結果發現，本文的5個併購假說在大部份的情況下，都被支持，而且金融風暴發生，的確可能改變跨國購併決策和影響因素之間的關係。其中，訊息成本假說中的語言及宗教二個代理變數，在金融風暴之後，他們對跨國併購決策都有較大且較顯著的影響力。第二部份，我們先探討OECD國家併購行為是否存在共同波動特性，實證結果支持 OECD 國家的購併活動具有共同波動的特性；此外，由狀態存續機率估計值發現樣本國停留在特定購併狀態的持續期間相當長，表示只要這些國家一起處於購併高峰期，則此共同購併高峰期會持續一段期間。相反的，若沒有發生共同購併高峰期，則在下一個時點出現共同購併高峰期的可能性也不高。從另一個角度解釋，亦即 OECD 國家購併潮的共同波動行為具有「長幅擺盪」(long swing) 的特質。最後，我們進一步考量歐盟金融業的併購活動和國家經濟成長率及股價報酬率二個要素的關係。實證結果發現在併購高峰期之下，不論歐盟金融業是處於併購交易的主併方或是被併方，股價報酬率都對併購活動皆具有正向影響；然而，國家的經濟成長率則會隨著歐盟金融業所處的併購主體而改變。此外，歐盟金融業的併購的共同波浪潮行為也具有「長幅擺盪」的特質。

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-04-25T19:30:00Z No. of bitstreams: 1 Sabrina Monique Schenato Bredow_.pdf: 1816066 bytes, checksum: dfc2fea0a5369d22e5ec14fc8ea0cd5a (MD5) / Made available in DSpace on 2016-04-25T19:30:00Z (GMT). No. of bitstreams: 1 Sabrina Monique Schenato Bredow_.pdf: 1816066 bytes, checksum: dfc2fea0a5369d22e5ec14fc8ea0cd5a (MD5) Previous issue date: 2016-02-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho analisa a influência do recente ciclo de alta dos preços das commodities sobre a entrada de capital externo no Brasil. Para o alcance desse objetivo, foram utilizadas duas metodologias econométricas diferentes: Modelos de Mudanças de Regimes Markovianos e Modelo Vetorial de Correção de Erros (VAR/VEC). O primeiro modelo foi utilizado para delimitar o ciclo de alta dos preços das commodities e para verificar se este período é concomitante ao período de elevação da entrada de capital externo no Brasil. Os resultados apontam que o recente período de alta dos preços das commodities ocorre entre os anos de 2002 e 2014, que é o último ano da amostra utilizada nesta pesquisa. Ademais, os regimes de alta estimados para as exportações, Investimento Estrangeiro Direto (IED) e Investimento Estrangeiro em Carteira (IEC), que são os três principais agregados do Balanço de Pagamentos que representam o ingresso de capitais externos no país, ocorrem em períodos similares ao observado para a série dos preços das commodities. A partir destes resultados, a influência da alta dos preços das commodities sobre a entrada de capital externo no Brasil foi analisada através do emprego da metodologia VAR/VEC, para o período entre o ano de 2002 e 2014, a partir da estimação de três modelos diferentes, um para cada agregado do Balanço de Pagamentos brasileiro. Os resultados apontam que o ciclo de alta dos preços das commodities influenciou significativamente a entrada de dividas externas no Brasil, sendo que os efeitos mais expressivos ocorrem via comércio e entrada de capitais de curto prazo. / This study analyzes the influence of the recent cycle of high commodity prices on foreign capital inflows in Brazil. To achieve this goal, it was used two different econometric methodologies: Markov-Switching Model and Vector Error Correction Model (VAR/VEC). The first model was used to define the cycle of high commodity prices and to check if this period is concomitant to the raise period of foreign capital inflows in Brazil. The results show that the recent period of high commodity prices occurs between the years 2002 and 2014, which is the last year of the sample used in this research. Moreover, the estimated high regime for exports, Foreign Direct Investment and Foreign Portfolio Investment, which are the three main aggregates of the Balance of Payments representing the inflow of foreign capital in the country occur in similar periods to that observed for the series of commodity prices. From these results, the influence of higher commodity prices on foreign capital inflows in Brazil was analyzed through the use of VAR/VEC methodology for the period between 2002 and 2014, from the estimation of three different models, one for each aggregate of the Balance of Payments. The results show that the cycle of high commodity prices significantly influenced the foreign capital inflows in Brazil, with the most significant effects occur via trade and short-term capital inflows.

The aim of this thesis is to examine empirically the relationship between the exchange rate, the instruments of monetary policy and the measures of economic performance for Indonesia, Korea and Thailand during the pre- and post 1997 Asian financial crisis. The first core chapter (Chapter 2) assesses the possible linkages between the increase in domestic inflation and the exchange rate targeting policy adopted in these countries. Using the cointegration technique and a simple monetarist inflation model, Chapter 2 finds strong evidence that the exchange rate policy that generates a predominant domestic currency undervaluation has caused an increase in the domestic inflation rate for Indonesia and Korea. However, the exchange rate targeting policy that brings about a predominant baht overvaluation especially during the pre-crisis period has lowered Thailand’s inflation. Soon after the outbreak of 1997-crisis, instead of using the exchange rate as the nominal anchor, all three countries have implement their monetary policy around an inflation target following an inflation targeting framework. Owing to this significant structural break, the second core chapter (Chapter 3) uses a Markov-switching VAR framework to determine if the effects of monetary policy shocks have changed across different monetary policy regimes in these economies. Chapter 3 finds that regime switches occur in mid-1997 to 2000 for Indonesia, which coincides with the period after the onset of 1997-crisis and the economic recovery period; and in 1999 for Korea and Thailand, which coincides with the period when the inflation-targeting framework is adopted. From the regime-dependent impulse response functions, the responses of macroeconomic variables to monetary policy shocks have changed significantly across different regimes only for the case of Korea and Thailand. From the above discussions, Chapter 2 found that exchange rate targeting policy caused higher domestic inflation in Indonesia and Korea especially during the pre-crisis period; while Chapter 3 found that inflation targeting policy seemed to cause structural changes in Korea and Thailand. Therefore using a structural VAR framework, the third core chapter (Chapter 4) explores further the role of the exchange rate and inflation targeting policy on the economic performances of these economies during the pre- and post crisis periods. Chapter 4 finds that in the case of Indonesia and Korea, the foreign exchange market does create most of its own shocks during the pre-crisis period but not during the post crisis period. For Indonesia and Thailand, the soft US dollar peg policy during the pre-crisis period has caused additional distortions in the domestic economy. Moreover the role of the exchange rate as a shock absorber has increased during the post crisis period only for the case of Indonesia and Thailand. For all three economies, following the introduction of the inflation targeting policy, domestic short-term interest rates have been adjusted systematically to offset inflationary pressure following the real and nominal shocks. Moreover, in the case of Indonesia and Thailand, the unsystematic part of monetary policy plays a smaller role in explaining the variations in domestic economy during the post crisis period. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1320356 / Thesis (Ph.D.) -- University of Adelaide, School of Economics, 2008

Financial time series studies indicate that the lognormal assumption for the return of an underlying security is often violated in practice. This is due to the presence of time-varying volatility in the return series. The most common departures are due to a fat left-tail of the return distribution, volatility clustering or persistence, and asymmetry of the volatility. To account for these characteristics of time-varying volatility, many volatility models have been proposed and studied in the financial time series literature. Two main conditional-variance model specifications are the autoregressive conditional heteroscedasticity (ARCH) and the stochastic volatility (SV) models. The SV model, proposed by Taylor (1986), is a useful alternative to the ARCH family (Engle (1982)). It incorporates time-dependency of the volatility through a latent process, which is an autoregressive model of order 1 (AR(1)), and successfully accounts for the stylized facts of the return series implied by the characteristics of time-varying volatility. In this thesis, we review both ARCH and SV models but focus on the SV model and its variations. We consider two modified SV models. One is an autoregressive process with stochastic volatility errors (AR--SV) and the other is the Markov regime switching stochastic volatility (MSSV) model. The AR--SV model consists of two AR processes. The conditional mean process is an AR(p) model , and the conditional variance process is an AR(1) model. One notable advantage of the AR--SV model is that it better captures volatility persistence by considering the AR structure in the conditional mean process. The MSSV model consists of the SV model and a discrete Markov process. In this model, the volatility can switch from a low level to a high level at random points in time, and this feature better captures the volatility movement. We study the moment properties and the likelihood functions associated with these models. In spite of the simple structure of the SV models, it is not easy to estimate parameters by conventional estimation methods such as maximum likelihood estimation (MLE) or the Bayesian method because of the presence of the latent log-variance process. Of the various estimation methods proposed in the SV model literature, we consider the simulated maximum likelihood (SML) method with the efficient importance sampling (EIS) technique, one of the most efficient estimation methods for SV models. In particular, the EIS technique is applied in the SML to reduce the MC sampling error. It increases the accuracy of the estimates by determining an importance function with a conditional density function of the latent log variance at time t given the latent log variance and the return at time t-1. Initially we perform an empirical study to compare the estimation of the SV model using the SML method with EIS and the Markov chain Monte Carlo (MCMC) method with Gibbs sampling. We conclude that SML has a slight edge over MCMC. We then introduce the SML approach in the AR--SV models and study the performance of the estimation method through simulation studies and real-data analysis. In the analysis, we use the AIC and BIC criteria to determine the order of the AR process and perform model diagnostics for the goodness of fit. In addition, we introduce the MSSV models and extend the SML approach with EIS to estimate this new model. Simulation studies and empirical studies with several return series indicate that this model is reasonable when there is a possibility of volatility switching at random time points. Based on our analysis, the modified SV, AR--SV, and MSSV models capture the stylized facts of financial return series reasonably well, and the SML estimation method with the EIS technique works very well in the models and the cases considered.

本篇論文使用SWARCH模型探討布蘭特原油期貨市場的波動性。SWARCH模型將條件變異設定為可隨時間變動而改變，甚至移轉到不同的區間上。實證結果顯示SWARCH (3,3)模型具有最佳配適度與最準確的預測能力。樣本在不同區間下的平滑機率的估計值有助於補捉資料特性，而且當樣本落在高波動率區間上時會對應著重大事件的發生，如1990年波斯灣戰爭、1997年亞洲金融風暴與2001年的911恐怖攻擊。 / This paper investigates the volatility of the Brent crude oil futures markets using Markov-switching ARCH (SWARCH) model. The SWARCH model allows the conditional disturbances to change as time passes and even to switch in different regimes. The empirical evidence shows that the SWARCH (3,3) model performs the best goodness of fit and the best forecast performance between different fitting models. The estimation of smoothing probabilities of data under different regimes facilitates to capture the characteristics of data, and the high-volatility regime is associated with the extraordinary events, such as the 1990’s Persian Gulf War, the 1997’s Asia Financial Crisis, and the 2001’s 911 terrorist attack.

本篇論文使用馬可夫移轉變異數模型探討台指選擇權之買權的波動性。馬可夫移轉變異數模型將條件變異設定為可隨時間變動而改變，甚至移轉到不同區間上。樣本在不同區間下的平滑機率估計值有助於捕捉資料特性，實證結果顯示當樣本落在高波動率區間上時，會對應著重大事件的發生，例如2004年台灣319槍擊案、2006年全球股災、2008年金融海嘯等。當樣本落在低波動率區間上時，會對應著投資人傾向將台股指數的上漲或下跌視為超漲或超跌，而賦予台指選擇權之買權負的時間價值。 / This paper investigates the volatility of TAIEX Call Options using Markov-switching variance model. The Markov-switching variance model allows the conditional disturbances to change as time passes and even switch between different regimes. The estimation of smoothed probabilities under different regimes facilitates to capture the characteristics of data. The empirical result shows that the high volatility regime is related to extraordinary events, such as 319 shooting incident in 2004, the global stock market crash in 2006, and the Financial Crisis in 2008. When in low volatility regime, investors tend to treat rise or fall in TAIEX as overreactions and give TAIEX Call Options turning points of time values.

Financial time series studies indicate that the lognormal assumption for the return of an underlying security is often violated in practice. This is due to the presence of time-varying volatility in the return series. The most common departures are due to a fat left-tail of the return distribution, volatility clustering or persistence, and asymmetry of the volatility. To account for these characteristics of time-varying volatility, many volatility models have been proposed and studied in the financial time series literature. Two main conditional-variance model specifications are the autoregressive conditional heteroscedasticity (ARCH) and the stochastic volatility (SV) models. The SV model, proposed by Taylor (1986), is a useful alternative to the ARCH family (Engle (1982)). It incorporates time-dependency of the volatility through a latent process, which is an autoregressive model of order 1 (AR(1)), and successfully accounts for the stylized facts of the return series implied by the characteristics of time-varying volatility. In this thesis, we review both ARCH and SV models but focus on the SV model and its variations. We consider two modified SV models. One is an autoregressive process with stochastic volatility errors (AR--SV) and the other is the Markov regime switching stochastic volatility (MSSV) model. The AR--SV model consists of two AR processes. The conditional mean process is an AR(p) model , and the conditional variance process is an AR(1) model. One notable advantage of the AR--SV model is that it better captures volatility persistence by considering the AR structure in the conditional mean process. The MSSV model consists of the SV model and a discrete Markov process. In this model, the volatility can switch from a low level to a high level at random points in time, and this feature better captures the volatility movement. We study the moment properties and the likelihood functions associated with these models. In spite of the simple structure of the SV models, it is not easy to estimate parameters by conventional estimation methods such as maximum likelihood estimation (MLE) or the Bayesian method because of the presence of the latent log-variance process. Of the various estimation methods proposed in the SV model literature, we consider the simulated maximum likelihood (SML) method with the efficient importance sampling (EIS) technique, one of the most efficient estimation methods for SV models. In particular, the EIS technique is applied in the SML to reduce the MC sampling error. It increases the accuracy of the estimates by determining an importance function with a conditional density function of the latent log variance at time t given the latent log variance and the return at time t-1. Initially we perform an empirical study to compare the estimation of the SV model using the SML method with EIS and the Markov chain Monte Carlo (MCMC) method with Gibbs sampling. We conclude that SML has a slight edge over MCMC. We then introduce the SML approach in the AR--SV models and study the performance of the estimation method through simulation studies and real-data analysis. In the analysis, we use the AIC and BIC criteria to determine the order of the AR process and perform model diagnostics for the goodness of fit. In addition, we introduce the MSSV models and extend the SML approach with EIS to estimate this new model. Simulation studies and empirical studies with several return series indicate that this model is reasonable when there is a possibility of volatility switching at random time points. Based on our analysis, the modified SV, AR--SV, and MSSV models capture the stylized facts of financial return series reasonably well, and the SML estimation method with the EIS technique works very well in the models and the cases considered.

Este trabalho tem o intuito de investigar os efeitos de uma das principais barreiras atualmente impostas ao comércio internacional: o antidumping. Embora seja um instrumento legal de combate ao comércio desleal – o dumping –, a utilização do referido instrumento passou a ser mais intensa após a redução das barreiras tarifárias promovidas pelas sucessivas rodadas de negociações multilaterais e acordos preferenciais de comércio. Concomitantemente, a prática indiscriminada do antidumping passou a estar cada vez mais dissociada da existência do próprio dumping. Nesse sentido, este trabalho avalia os impactos do antidumping sob duas óticas. A primeira, ex-post, identifica os efeitos dos processos antidumping iniciados desde o início da década de 1990 pelos EUA sobre diversos produtos brasileiros, especialmente no que tange ao desempenho das importações daquele país. Para tanto, utiliza-se a metodologia de Mudança de Regime Markoviano, que permite avaliar as alterações ocorridas ao longo do tempo na série de importações, avaliando médias, variâncias e probabilidades de transição entre regimes. Os resultados encontrados indicam que as iniciações dos processos antidumping usualmente ocorrem após um longo período – entre dois e três anos – de regime de crescimento (2% a.m), passando para o regime de menor crescimento (entre -4% e -6% a.m.), entre a decisão preliminar e final do processo Em geral, pode-se dizer que os efeitos são negativos, embora não haja uma convergência para um estado absorvente de menor crescimento das importações na maioria dos casos analisados. Adicionalmente, realiza-se uma análise ex-ante, identificando possíveis impactos de uma reação antidumping por parte dos EUA sobre o Brasil, após a criação da Área de Livre Comércio das Américas (ALCA). As simulações supõem diferentes cenários, como a recomposição de tarifas (1) nos segmentos mais afetados em termos de crescimento de importações por parte dos EUA e (2) nos segmentos historicamente mais afetados pela prática antidumping por parte dos norte-americanos. A metodologia de equilíbrio geral, através do GTAP com modelo de concorrência perfeita, mostra que, em todos cenários, os benefícios totais de bem-estar são preservados. Conclui-se também que uma eventual “blitz” antidumping sobre setores que os EUA tradicionalmente aplicam o instrumento não deve afetar o Brasil tão fortemente quanto ações antidumping sobre setores cujas importações por parte dos norte-americanos cresceriam após a implementação da ALCA. / The objective of this thesis is to investigate one of the main barriers to international trade: antidumping. While a legal tool to defeat dumping, this instrument has been highly applied especially after the reduction of tariff barriers, an outcome of multilateral trade negotiations rounds and even preferential trade agreements during the last decades. Nevertheless, the indiscriminate practice of antidumping has become dissociate even from the existence of dumping itself. This study evaluates the antidumping impacts in two different instances. Firstly, it identifies the ex-post trading effects of the antidumping processes initiated since the early 90’s by USA over several Brazilian products. The methodology used is the Markov Switching Model that allows the evaluation of regime changes on USA imports from Brazil, assessing its mean, variance and transition probabilities. The results indicate that antidumping processes are initiated usually after a long time – about two or three years – of growth regime (2% monthly), changing to a lower growth regime (-2% to –6% monthly), between preliminary and final decisions. Generally, antidumping effects over Brazilian exports have been negative, even though there isn’t a convergence to an absorbent state of exports reduction in the majority of the products studied. Moreover, an ex ante evaluation is taken forward, identifying possible outcomes of an USA antidumping reaction after the Free Trade Area of the Americas (FTAA) has been created. The simulations suppose different scenarios like a surge in tariffs, offsetting the reduction carried over by the FTAA agreement. Two scenarios include the surge in tariffs (offsetting 5% and 25% of pre Alca tariffs) in sectors which imports have been raised after FTAA has been implemented; while the third one simulates a 50% tariff offset on sectors that USA usually applies antidumping measures. The general equilibrium methodology, through the application of the standard General Trade Applied Project (GTAP), demonstrates that in all scenarios the welfare benefits are persevered. The conclusion is that an antidumping blitz over sectors that traditionally are affected by USA measures shall not affect Brazil as strongly as antidumping actions over sector in which USA imports has risen after FTAA.

O presente trabalho busca identificar bull e bear markets para o mercado financeiro brasileiro, especificamente para o índice Ibovespa, através das principais metodologias existentes na literatura: regras não paramétricas e modelos de mudança de regime markoviano. A primeira abordagem foi utilizada como benchmark para comparação com melhor modelo econométrico estimado pela segunda abordagem, visto que trata-se de um método ex-post de identificação. No tange aos modelos de mudança de regime markoviano, constatou-se que permitir regimes distintos também para a variância da série contribui para a identificação dos mesmos. Desta forma, o melhor modelo obtido fora o MSARMA(2,1)-2 para a série de retornos semanais do índice Ibovespa. O modelo foi capaz de identificar os principais eventos que impactaram a economia e o mercado financeiro brasileiro no período. Além disto, o modelo se mostrou útil para a tomada de decisão, visto que a estratégia de investimento, baseada na previsão um passo à frente do estado do mercado, foi capaz de preservar o capital do investidor, gerando um melhor desempenho do que na estratégia buy-and-hold de longo prazo. / This paper seeks to identify bull and bear markets in the brazilian stock market, specifically to the time series of the Ibovespa index, through the main methodologies present in literature: identification based on rules and Markov switching models. The first method was used as a benchmark to compare with the best regime switching model, since it is an ex-post method of identification. Modelling a Markov switching model with two regimes also for the variance of the process resulted in a better identification of the markets. Thus, the best Markov switching model estimated was theMSARMA(2,1)-2 to the time series of the Ibovespa weekly returns. The model was able to identify the main events that have impacted the brazilian economy and also the stock market in the period. Furthermore, the model proved its value in decision making, since in a investment strategy, based on the models one step ahead forecast about the regime of the market, it was able to preserve investor capital, generating a better performance than the buy-and-hold strategy.

O presente trabalho foi motivado pela forte demanda por modelos de dependência mais precisos e realistas para aplicações a dados financeiros multivariados. A recente crise financeira de 2007-2009 deixou claro quão importante é uma modelagem precisa da dependência para a avaliação correta do risco financeiro: percepções equivocadas sobre dependências extremas entre diferentes ativos foram um elemento importante da crise do subprime. O famoso teorema dc Sklar (1959) introduziu as cópulas como uma ferramenta para se modelar padrões de dependência mais sofisticados. Ele estabelece que qualquer função de distribuição conjunta ndimensional pode ser decomposta em suas n distribuições marginais e uma cópula, sendo que a última caracteriza completamente a dependência entre as variáveis. Enquanto existe uma variedade de famílias de cópulas bivariadas que podem descrever um amplo conjunto de dependências complexas, o conjunto de cópulas com dimensão mais elevada era bastante restrito até recentemente. Joe (1996) propôs uma construção de distribuições nmltivariadas baseada em pair-copulas (cópulas bivariadas), chamada pair-copula construction ou modelo de vine cópula, que reverteu esse problema. Nesta tese, desenvolvemos três ensaios que exploram a teoria de cópulas para obter modelos de dependência multivariados muito flexíveis para aplicações a dados financeiros. Patton (2006) estendeu o teorema de Sklar para o caso de distribuições condicionais e tornou o parâmetro de dependência da cópula variante no tempo. No primeiro ensaio, introduzimos um novo enfoque para modelar a dependência entre retornos financeiros internacionais ao longo do tempo, combinando cópulas; tempo-variantes e o modelo de mudança Markoviana. Aplicamos esses modelos de cópula e também os modelos propostos por Patton (2006), Jondeau e Rockinger (2006) e Silva Filho et al. (2012a) aos retornos dos índices FTSE 100, CAC 40 e DAX. Comparamos essas metodologias em termos das dinâmicas de dependência resultantes e das habilidades dos modelos em prever Valor em Risco (VaR). Interessantemente, todos os modelos identificam um longo período de alta dependência entre os retornos começando em 2007, quando a crise do subprime teve início oficialmente. Surpreendentemente, as cópulas elípticas mostram melhor desempenho na previsão dos quantis extremos dos retornos dos portfólios. No segundo ensaio, estendemos nosso estudo para o caso de n > 2 variáveis, usando o modelo de vine cópula para investigar a estrutura de dependência dos índices CAC 40, DAX, FTSE 100, S&P 500 e IBOVESPA, e, particularmente, checar a hipótese de dependência assimétrica nesse caso. Com base em nossos resultados empíricos, entretanto, essa hipótese não pode ser verificada. Talvez a dependência assimétrica com caudas inferiores mais fortes ocorra apenas temporariamente, o que sugere que a incorporação de variação temporal ao modelo de vine cópula pode melhorá-lo como ferramenta para modelar dados financeiros internacionais multivariados. Desta forma, no terceiro ensaio, introduzimos dinâmica no modelo de vine cópula permitindo que os parâmetros de dependência das pair-copulas em uma decomposição D-vine sejam potencialmente variantes no tempo, seguindo um processo ARMA(l,m) restrito como em Patton (2006). O modelo proposto é avaliado em simulações e também com respeito à acurácia das previsões de Valor em Risco (VaR) em períodos de crise. Os experimentos de Monte Cailo são bastante favoráveis à cópula D-vine dinâmica em comparação a uma cópula D-vine estática. Adicionalmente, a cópula D-vine dinâmica supera a cópula D-vine estática em termos de acurária preditiva para os nossos conjuntos de dados / This work was motivated by the strong demand for more precise and realistic dependence models for applications to multivariate financial data. The recent financial crisis of 2007-2009 has made it clear how important is a precise modeling of dependence for the accurate assessment of financial risk: misperceptions about extreme dependencies between different financial assets were an important element of the subprime crisis. The famous theorem by Sklar (1959) introduced the copulas as a tool to model more intricate patterns of dependence. It states that any n-dimensional joint distribution function can be decomposed into its n marginal distributions and a copula, where the latter completely characterizes the dependence among the variables. While there is a variety of bivariate copula families, which can match a wide range of complex dependencies, the set of higher-dimensional copulas was quite restricted until recently. Joe (1996) proposed a construction of multivariate distributions based on pair-copulas (bivariate copulas), called pair-copula construction or vine copula model, that has overcome this issue. In this thesis, we develop three papers that explore the copula theory in order to obtain very flexible multivariate dependence rnodels for applications to financial data. Patton (2006) extended Sklar's theorem to the conditional case and rendered the dependence parameter of the copula time-varying. In the first paper, we introduce a new approach to modeling dependence between International financial returns over time, combining time-varying copulas and the Markov switching model. We apply these copula models and also those proposed by Patton (2006), Jondeau and Rockinger (2006) and Silva Filho et al. (2012a) to the return data of FTSE 100, CAC 40 and DAX indexes. We compare these methodologies in terms of the resulting dynamics of dependence and the models' abilities to forecast Value-at-Risk (VaR). Interestingly, ali the models identify a long period of high dependence between the returns beginning in 2007, when the subprime crisis was evolving. Surprisingly, the elhptical copulas perform best in forecasting the extreme quantiles of the portfolios returns. In the second paper, we extend our study to the case of n > 2 variables, using the vine copula model to investigate the dependence structure of the broad stock market indexes CAC 40, DAX, FTSE 100, S&P 500 and IBOVESPA, and, particularly, check the asymmetric dependence hypothesis in this case. Based on our empirical results, however, this hypothesis cannot be verified. Perhaps, asymmetric dependence with stronger lower tails occurs only temporarily, what suggests that incorporating time variation into the vine copula rnodel can improve it as a tool to rnodel multivariate International financial data. So, in the third paper, we introduce dynamics into the vine copula model by allowing the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially timevarying, following a nonlinear restricted ARMA(l,m) process as in Patton (2006). The proposed model is evaluated in simulations and further assessed with respect to the accuracy of Value-at- Risk (VaR) forecasts in crisis periods. The Monte Cario experiments are quite favorable to the dynamic D-vine copula in comparison with a static D-vine copula. Moreover, the dynamic Dvine copula outperforms the static D-vine copula in terms of predictive accuracy for our data sets.