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.

Orientadores: Marcio Luiz de Andrade Netto, Jose Roberto Securato , Alessandra de Avila Montini / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T14:31:10Z (GMT). No. of bitstreams: 1 Freitas_AntonioAirtonCarneirode_D.pdf: 2395437 bytes, checksum: 02e1418421c18b7b627cbfe5f41ed90a (MD5) Previous issue date: 2007 / Resumo: A inferência sobre a previsibilidade de sistemas dinâmicos não lineares multivariados tem sido freqüentemente realizada a partir de testes que podem induzir à conclusões equivocadas. Isto porque em muitas pesquisas realizadas os testes utilizados são o de autocorrelação, o da razão de variância e do espectro, que só verificam a existência ou não da correlação serial de componentes lineares. Neste trabalho, também são utilizados testes para avaliar a correlação serial de componentes não lineares. Busca-se provar empiricamente se as classes de modelos ARMA-GARCH e neurais, bem como a combinação deles, tem qualidade de previsão superior ao modelo diferença Martingale em previsões na média condicional dos retornos da taxa de câmbio brasileira e da umidade em microclima. Um método de seleção de variáveis é proposto para melhorar os resultados obtidos com modelos de previsão multivariados não baseados em teoria. As não linearidades negligenciadas durante o ajuste dos modelos neurais são avaliadas por meio do teste de Blake and Kapetanios (2003). O teste de White (2000) é utilizado para comparar os modelos de previsão propostos em conjunto com o modelo benchmark. Foi constatado empiricamente que os dois processos analisados não são do tipo diferença Martingale / Abstract: The inference on predictability of nonlinear multivariate systems has been done with some possible misleading conclusions when the test statistics are insignificant because autocorrelation, variance ratio and spectrum tests check only serial uncorrelatedness (linear components). This work empirically explores the non linear components and if the ARMA-GARCH, neural network models, as well as their combination, outperform a Martingale model in the conditional mean out-of-sample forecasts. It is proposed a variable selection method to improve the results obtained with multivariate models without a priori knowledge. The neglected nonlinearities and data snooping bias were avoided applying respectively the Blake and Kapetanios (2003) and the White (2000) reality check tests. The empirical results indicate that the Brazilian exchange rates and the microclimate humidity are not Martingale differences / Doutorado / Engenharia de Computação / Doutor em Engenharia Elétrica

This thesis is mainly focused on pricing securities of selected capital protected funds. In its theoretical part, there are summarized approaches and principals that are generally used for derivatives pricing because capital protected funds' securities contain embedded options. Emphasis is put on risk-neutral pricing using Monte Carlo simulation at that point because complicated pay-off functions of these funds are hard to be evaluated analytically. There are also presented main approaches to constructions and portfolio management of these funds from their portfolio manager's viewpoint. Finally, there is made an overview of basic types of capital protected funds issued both in The Czech republic and Europe. Analytical part is focused on evaluation of selected capital protected funds. There is applied a standard approach that is based on a simulation of Geometric Brownian Motion with constant conditional variance and correlation in contrast with an advanced approach where the conditional variance and conditional correlation matrix are simulated as well. That is accomplished with GARCH-in-mean and DCC-GARCH models. Estimated prices are compared with real market prices and there is also performance of the standard models compared with performance of advanced ones.

This thesis has used the Fama and French five-factor model (FF5M) and proposed an alternative model. The proposed model is named the Fama and French five-factor heteroscedastic student's model (FF5HSM). The model utilises an ARMA model for the returns with the FF5M factors incorporated and a GARCH(1,1) model for the volatility. The FF5HSM uses returns data from the FF5M's portfolio construction for the Japanese stock market and the five risk factors. The portfolio's capture different levels of market capitalisation, and the factors capture market risk. The ARMA modelling is used to address the autocorrelation present in the data. To deal with the heteroscedasticity in daily returns of stocks, a GARCH(1,1) model has been used. The order of the GARCH-model has been concluded to be reasonable in academic literature for this type of data. Another finding in earlier research is that asset returns do not follow the assumption of normality that a regular regression model assumes. Therefore, the skewed student's t-distribution has been assumed for the error terms. The result of the data indicates that the FF5HSM has a better in-sample fit than the FF5M. The FF5HSM addresses heteroscedasticity and autocorrelation in the data and minimises them depending on the portfolio. Regardingforecasting, both the FF5HSM and the FF5M are accurate models depending on what portfolio the model is applied on.

Even though major advances in economic theory and modelling have in some cases furthered our understanding of how the economy works, the system as a whole has become more complex. If policymakers had perfect knowledge about the actual state of the economy, the various transmission mechanisms as well as the true underlying model, monetary intervention would be greatly simplified. In reality, however, the monetary authorities have to contend with considerable uncertainty in relation to the above-mentioned factors. This said, uncertainty has mostly been neglected in both the theoretical and empirical literature focusing on monetary policy analysis. Nonetheless, findings from a review of theoretical literature that does exist on this topic suggest that optimal central banks act more conservatively when faced with uncertainty. Similarly, empirical findings from the literature also favour conservatism. However, there is some evidence to suggest that this is not always the case. These results suggest that central banks do not always act optimally when faced with uncertainty. The limited number of industrial country cases examined prevents any generalised view from emerging. If anything, the literature findings suggest that central bank behaviour differs across countries. This thesis aims to contribute to the empirical literature by studying the effects of uncertainty on monetary policy in the developing country case of South Africa. In simplest terms, the thesis seeks to establish whether or not the South African Reserve Bank (SARB) responded optimally to uncertainty as suggested by theoretical models thereof. To this end, the thesis employs a theoretical model which resembles a structural rule-based approach. The optimal interest rate rule was derived given a set of structural equations relating to demand, the Phillips curve and the real exchange rate. To incorporate uncertainty, it is assumed that the coefficients are dependent on the variances of the exogenous variables, namely inflation, the output gap and the exchange rate. The uncertainty adjusted model allows us to investigate whether monetary policy is more aggressive or passive when uncertainty about the relevant exogenous variable increases. Inflation, output gap and exchange rate uncertainty estimates were derived through GARCH-model specifications related to the structural equations as defined in the theoretical model. The investigation considered both indirect and direct uncertainty effects with a sample period stretching from 1990 to 2011. The findings reported in this thesis provide strong evidence in support of the notion that uncertainty plays a significant role within the South African monetary policy landscape and contributes towards explaining the SARB’s actions. Furthermore, the results suggest that the SARB did in fact act optimally in responding more conservatively to target variable fluctuations on average. Also, the findings could potentially strengthen the case for inflation targeting as a monetary policy regime, as the results indicate a marked decline in the effects of uncertainty under inflation targeting than before. / Economics / D. Com. (Economics)

ESG investing has been a hot topic during several years and there have been numerousstudies examining the relationship between sustainable assets and non-sustainable assetsincluding green bonds, social bonds, environmental bonds, ESG-bonds and ESG indices;conventional bonds, S&P 500, common stocks and non-ESG indices. During negative marketshocks several ESG stocks and indices have been shown to outperform common stocks andindices. Green bonds demonstrated an asymmetric relationship to other assets providinginvestors with an opportunity for diversification. We’ve looked at the relationship andperformance of sustainable assets and non-sustainable assets by using Markowitz portfoliometrics and Engle Rs’ DCC-GARCH. Our findings propose green bonds and treasuries toprovide hedging and diversification opportunities during crises but demonstrate sustainablefixed income assets to underperform non-sustainable fixed income assets during the COVID19 market shock as opposed to previous studies.

This thesis investigates systematic liquidity risk and short-term stock price reaction to large one-day price changes. We study 642 constituents of the FTSALL share index over the period from 1st July 1992 to 29th June 2007. We show that the US evidence of a priced systematic liquidity risk of Pastor and Stambaugh (2003) and Liu (2006) is not country-specific. Particularly, systematic liquidity risk is priced in the London Stock Exchange when Amihud's (2002) illiquidity ratio is used as a liquidity proxy. Given the importance of systematic liquidity risk in the asset pricing literature, we are interested in testing whether the different levels of systematic liquidity risk across stocks can explain the anomaly following large one-day price changes. Specifically, we expect that the stocks with high sensitivity to the fluctuations in aggregate market liquidity to be more affected by price shocks. We find that most liquid stocks react efficiently to price shocks, while the reactions of the least liquid stocks support the uncertain information hypothesis. However, we show that time-varying risk is more important than systematic liquidity risk in explaining the price reaction of stocks in different liquidity portfolios. Indeed, the time varying risk explains nearly all of the documented overreaction and underreaction following large one-day price changes. Our evidence suggests that the observed anomalies following large one-day price shocks are caused by the pricing errors arising from the use of static asset pricing models. In particular, the conditional asset pricing model of Harris et al. (2007), which allow both risk and return to vary systematically over time, explain most of the observed anomalies. This evidence supports the Brown et al. (1988) findings that both risk and return increase in a systematic fashion following price shocks. / Yarmouk University, Jordan.

El siguiente proyecto de tesis pretende mostrar y verificar cómo las redes neuronales, en concreto, la red backpropagation son una alternativa para la predicción de la volatilidad condicional frente a los modelos econométricos clásicos de la familia GARCH. El estudio se realiza para diferentes índices bursátilies de diferentes tamaños y zonas geográficas, así como para datos tanto diarios como de alta frecuencia utilizando para la comparativa uno de los modelos más extendidos para el estudio de la volatildiad condicional en índices bursátiles como el EGARCH, dada la existencia comprobada de asimetrías en la volatildiad de dichos índices. La elección de la red neuronal backpropagation viene motivada por ser una de las redes neuronales más extendidas en su uso en finanzas por su capacidad de generalización método de aprendizaje basada en la relga delta generalizada. / Oliver Muncharaz, J. (2014). MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35803

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.

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