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Redes Bayesianas: um método para avaliação de interdependência e contágio em séries temporais multivariadas; Bayesian Networks: a method for evaluation of interdependence and contagion in multivariate time series

Carvalho, João Vinícius de França
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 25/04/2011 Português
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O objetivo deste trabalho consiste em identificar a existência de contágio financeiro utilizando a metodologia de redes bayesianas. Além da rede bayesiana, a análise da interdependência de mercados internacionais em períodos de crises financeiras, ocorridas entre os anos 1996 e 2009, foi modelada com outras duas técnicas - modelos GARCH multivariados e de Cópulas, envolvendo países nos quais foi possível avaliar seus efeitos e que foram objetos de estudos similares na literatura. Com os períodos de crise bem definidos e metodologia calcada na teoria de grafos e na inferência bayesiana, executou-se uma análise sequencial, em que as realidades que precediam períodos de crise foram consideradas situações a priori para os eventos (verossimilhanças). Desta combinação resulta a nova realidade (a posteriori), que serve como priori para o período subsequente e assim por diante. Os resultados apontaram para grande interligação entre os mercados e diversas evidências de contágio em períodos de crise financeira, com causadores bem definidos e com grande respaldo na literatura. Ademais, os pares de países que apresentaram evidências de contágio financeiro pelas redes bayesianas em mais períodos de crises foram os mesmos que apresentaram os mais altos valores dos parâmetros estimados pelas cópulas e também aqueles cujos parâmetros foram mais fortemente significantes no modelo GARCH multivariado. Assim...

Avaliação da habilidade preditiva entre modelos Garch multivariados : uma análise baseada no critério Model Confidence Set

Borges, Bruna Kasprzak
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
Português
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Esta dissertação analisa a questão da seleção de modelos GARCH multivariados em termos da perfomance de previsão da matriz de covariância condicional. A aplicação empírica é realizada com 7 retornos de índices de ações envolvendo um conjunto de 34 especificações de modelos para os quais computamos as previsões da variância condicional um passo a frente para uma amostra com 60 observações para cada especificação dos modelos GARCH multivariados. A comparação entre os modelos é baseada no procedimento Model Confidence Set (MCS) avaliado através de duas funções perdas robustas a proxies de volatilidade imperfeitas. O MCS é um procedimento que permite comparar vários modelos simultaneamente em termos de sua habilidade preditiva e determinar um conjunto de modelos estatisticamente semelhantes em termos de previsão, dado um nível de confiança.; This paper considers the question of the selection of multivariate GARCH models in terms of covariance matrix forecasting. In the empirical application we consider 7 series of returns and compare a set of 34 model specifications based on one-step-ahead conditional variance forecasts over a sample with 60 observations. The comparison between models is performed with the Model Confidence Set (MCS) procedure evaluated using two loss functions that are robust against imperfect volatility proxies. The MCS is a procedure that allows both a multiple model comparison in terms of forecasting accuracy and the determination of a model set composed of statistically equivalent models...

Conditional correlation models of autoregressive conditional heteroskedasticity with nonstationary GARCH equations

Amado, Cristina; Teräsvirta, Timo
Fonte: Universidade do Minho. Núcleo de Investigação em Políticas Económicas Publicador: Universidade do Minho. Núcleo de Investigação em Políticas Económicas
Tipo: Trabalho em Andamento
Publicado em /05/2011 Português
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In this paper we investigate the effects of careful modelling the long-run dynamics of the volatilities of stock market returns on the conditional correlation structure. To this end we allow the individual unconditional variances in Conditional Correlation GARCH models to change smoothly over time by incorporating a nonstationary component in the variance equations. The modelling technique to determine the parametric structure of this time-varying component is based on a sequence of specification Lagrange multiplier-type tests derived in Amado and Teräsvirta (2011). The variance equations combine the long-run and the short-run dynamic behaviour of the volatilities. The structure of the conditional correlation matrix is assumed to be either time independent or to vary over time. We apply our model to pairs of seven daily stock returns belonging to the S&P 500 composite index and traded at the New York Stock Exchange. The results suggest that accounting for deterministic changes in the unconditional variances considerably improves the fit of the multivariate Conditional Correlation GARCH models to the data. The effect of careful specification of the variance equations on the estimated correlations is variable: in some cases rather small...

Flexible multivariate GARCH modeling with an application to international stock markets

Santa-Clara, Pedro
Fonte: MIT Press Publicador: MIT Press
Tipo: Artigo de Revista Científica
Publicado em /08/2003 Português
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This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the nev^ estimator with a number of existing ones.

Analyse von Finanzmarktdaten mittels multivariater GARCH-Modelle - Spill-Over-Effekte von Volatilitäten : EURO-Wechselkurs und Finanzmärkte in Europa; Multivariate GARCH-models and their application to financial markets - spill-over-effects of volatilities : the Euro and financial markets in Europe

Flad, Michael
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Masterarbeit
Português
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Die Bestimmung der Volatilität von Finanzmarktdaten ist heutzutage Kernpunkt empirischer Analysen im Bereich des Finance/Banking oder der monetären Makroökonomik. Dabei erweisen sich multivariate GARCH (MGARCH-) Modelle als besonders hilfreich, da mit ihnen wichtige empirische Eigenschaften von Finanzmarktdaten, wie z.B. Volatilitätscluster oder kontemporäre Korrelationen mehrerer Zeitreihen, leicht abzubilden sind. Es wird daher sowohl eine Übersicht über gängige und neuere MGARCH-Modelle als auch deren Schätzmethoden gegeben. Zusätzlich soll durch eine empirische Analyse herausgefunden werden, ob Spill-Over-Effekte zwischen Devisenmarkt, Geld- und Aktienmarkt in Europa existieren. Insbesondere kommen zwei bivariate MGARCH-Modelle (CCC-Modell nach Bollerslev und DCC-Modell nach Engel) mit zusätzlichen Erweiterungen zur Anwendung, um mögliche Volatilitätsbeziehen zwischen dem EUR/USD-Wechselkurs, dem kurzfristigen Zinssatz (EURIBOR) sowie dem EUROSTOXX zu testen. Zusätzlich wird untersucht, ob Politimplikationen hinsichtlich der Diskussion um flexible vs. fixe Wechselkursregime ökonometrisch abgeleitet werden können.; Modelling the volatility of financial market data is important for the empirical analysis of many issues in Finance/Banking and Macroeconomics. Especially multivariate GARCH (MGARCH-) models are crucial in describing prominent features of financial time series...

The persistence and asymmetry of time-varying correlations

Baur, Dirk
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: ResearchPaper
Português
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Existing multivariate GARCH models either impose strong restrictions on the parameters or do not guarantee a well-defined (positive definite) covariance matrix. We focus on the multivariate GARCH model of Baba, Engle, Kraft and Kroner (BE=) and show that the covariance and correlation is not adequately specified. This implies that any analysis of the persistence and the asymmetry of the correlation is difficult and potentially biased. We illustrate this by the use of Monte-Carlo simulations for different correlation processes and propose a new Bivariate Dynamic Correlation (BDC) model that parameterizes the conditional correlation directly and eliminates the shortcomings of the BEKK model. Empirical results for correlations of the German stock market index with three international stock market indices reveal that correlations exhibit different degrees of persistence and different asymmetric reactions than variances. In addition, we find that correlations do not necessarily increase with variantes implying a justification for international portfolio diversification.

Nonlinear Causality Testing with Stepwise Multivariate Filtering

BEKIROS, Stelios D.
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
Português
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This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total Forex trading worldwide. The data covers the period 3/20/1987-11/14/2007, including the Asian crisis, the dot-com bubble and the period just before the outbreak of the US subprime crisis. The objective of the paper is to test for the existence of both linear and nonlinear causal relationships among these currency markets. The modified Baek-Brock test for nonlinear non-causality is applied on the currency return time series as well as the linear Granger test. Further to the classical pairwise analysis causality testing is conducted in a multivariate formulation, to correct for the effects of the other variables. A new stepwise multivariate filtering approach is implemented. To check if any of the observed causality is strictly nonlinear, the nonlinear causal relationships of VAR/VECM filtered residuals are also examined. Finally, the hypothesis of nonlinear non-causality is investigated after controlling for conditional heteroskedasticity in the data using GARCH-BEKK...

Realized Beta GARCH: A Multivariate GARCH model with realized measures of volatility and covolatility

HANSEN, Peter Reinhard; LUNDE, Asger; VOEV, Valeri
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
Português
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We introduce a multivariate GARCH model that incorporates realized measures of volatility and covolatility. The realized measures extract information about the current level of volatility and covolatility from high-frequency data, which is particularly useful for the modeling of return volatility during periods with rapid changes in volatility and covolatility. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than is usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises.

Nonlinear causality testing with stepwise multivariate filtering : evidence from stock and currency markets

BEKIROS, Stelios D.
Fonte: Elsevier Science Inc Publicador: Elsevier Science Inc
Tipo: Artigo de Revista Científica
Português
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We examine the spillovers of the US subprime crisis to Asian and European economies and in particular to what extent currency and stock markets have been affected by the crisis. Linear and non-linear dependencies are detected after pairwise and system-wise causality analysis. A new stepwise multivariate filtering approach is implemented after controlling for conditional heteroskedasticity in the raw data and in VAR/VECM residuals using multivariate GARCH models. Significant nonlinear causal linkages persisted even after the application of GARCH-BEKK, CCC-GARCH and DCC-GARCH modelling. This indicates that volatility effects might partly induce nonlinear causality. Perhaps new short-term asset-pricing models could be developed to explain this stylized fact. These results might also have important implications for hedging, trading strategies and financial market regulation.; This article is based on EUI ECO WP 2011/22

Contagion, decoupling and the spillover effects of the US financial crisis : evidence from the BRIC markets

BEKIROS, Stelios D.
Fonte: Elsevier Science Inc Publicador: Elsevier Science Inc
Tipo: Artigo de Revista Científica
Português
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Even though the global contagion effects of the financial crisis have been well documented, the transmission mechanism as well as the nature of the volatility spillovers among the US, the EU and the BRIC markets has not been systematically investigated. To examine the dynamic linear and nonlinear causal linkages a stepwise filtering methodology is introduced, for which vector autoregressions and various multivariate GARCH representations are adopted. The sample covers the after-Euro period and includes the financial crisis and the Eurozone debt crisis. The empirical results show that the BRICs have become more internationally integrated after the US financial crisis and contagion is further substantiated. Moreover, no consistent evidence in support of the "decoupling" view is found. Some nonlinear causal links persist after filtering during the examined period. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects, although tail dependency and higher-moments may be significant factors of the remaining interdependencies.

A multivariate generalized independent factor GARCH model with an application to financial stock returns

García-Ferrer, Antonio; González-Prieto, Ester; Peña, Daniel
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /12/2008 Português
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We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.

Comparing univariate and multivariate models to forecast portfolio value-at-risk

Santos, André A. P.; Nogales, Francisco J.; Ruiz, Esther
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /11/2009 Português
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This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR models. In this work we provide a more comprehensive look at the problem of portfolio VaR forecasting by using more appropriate statistical tests of comparative predictive ability. Moreover, we compare univariate vs. multivariate VaR models in the context of diversified portfolios containing a large number of assets and also provide evidence based on Monte Carlo experiments. We conclude that, if the sample size is moderately large, multivariate models outperform univariate counterparts on an out-of-sample basis.

The power log-GARCH model

Sucarrat, Genaro; Escribano, Álvaro
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em 09/06/2010 Português
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Exponential models of autoregressive conditional heteroscedasticity (ARCH) are attractive in empirical analysis because they guarantee the non-negativity of volatility, and because they enable richer autoregressive dynamics. However, the currently available models exhibit stability only for a limited number of conditional densities, and the available estimation and inference methods in the case where the conditional density is unknown hold only under very specific and restrictive assumptions. Here, we provide results and simple methods that readily enables consistent estimation and inference of univariate and multivariate power log-GARCH models under very general and non-restrictive assumptions when the power is fixed, via vector ARMA representations. Additionally, stability conditions are obtained under weak assumptions, and the power log-GARCH model can be viewed as nesting certain classes of stochastic volatility models, including the common ASV(1) specification. Finally, our simulations and empirical applications suggest the model class is very useful in practice.

Outliers in multivariate Garch models

Grané, Aurea; Martín-Barragán, Belén; Veiga, Helena
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper
Publicado em /02/2014 Português
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Outliers of moderate magnitude cause large changes in financial time series of prices and returns and affect both the estimation of parameters and volatilities when fitting a GARCH-type model. The multivariate setting is still to be studied, but similar biases and impacts on correlation dynamics are believed to exist. The accurate estimation of the correlation structure is crucial in many applications, such as portfolio allocation and risk management. This paper ocuses on these issues by studding the impact of additive outliers (isolated, patches and volatility outliers) on the estimation of correlations when fitting well known multivariate GARCH models and by proposing a general detection algorithm based on wavelets that can be applied to a large class of multivariate volatility models. This procedure can be also interpreted as a model miss-specification test since it is based on residual diagnostics. The effectiveness of the new proposal is evaluated by an intensive Monte Carlo study before it is applied to daily stock market indices. The simulation studies show that correlations are highly affected by the presence of outliers and that the new method is both effective and reliable, since it detects very few false outliers.; Work partially supported by Financial Research Center/UNIDE...

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

DUFOUR, Jean-Marie; KHALAF, Lynda; BEAULIEU, Marie-Claude
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 219916 bytes; application/pdf
Português
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In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates...

BayesDccGarch - An Implementation of Multivariate GARCH DCC Models

Fioruci, Jose A.; Ehlers, Ricardo S.; Louzada, Francisco
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/12/2014 Português
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Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in practice due to the lack of reliable software. This paper describes the {\tt R} package {\bf BayesDccGarch} which was developed to implement recently proposed inference procedures to estimate and compare multivariate GARCH models allowing for asymmetric and heavy tailed distributions.

Stationarity and Geometric Ergodicity of BEKK Multivariate GARCH Models

Boussama, Farid; Fuchs, Florian; Stelzer, Robert
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2011 Português
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Conditions for the existence of strictly stationary multivariate GARCH processes in the so-called BEKK parametrisation, which is the most general form of multivariate GARCH processes typically used in applications, and for their geometric ergodicity are obtained. The conditions are that the driving noise is absolutely continuous with respect to the Lebesgue measure and zero is in the interior of its support and that a certain matrix built from the GARCH coefficients has spectral radius smaller than one. To establish the results semi-polynomial Markov chains are defined and analysed using algebraic geometry.; Comment: version to appear in Stochastic Processes and their Applications, 2011; http://www.sciencedirect.com/science/article/pii/S0304414911001372

A closed-form estimator for the multivariate GARCH(1,1) model

Sbrana, Giacomo; Poloni, Federico
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/03/2013 Português
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We provide a closed-form estimator based on the VARMA representation for the unrestricted multivariate GARCH(1,1). We show that all parameters can be derived using basic linear algebra tools. We show that the estimator is consistent and asymptotically normal distributed. Our results allow also to derive a closed form for the parameters in the context of temporal aggregation of multivariate GARCH(1,1) by solving the equations as in Hafner [2008].

Bayesian Inference Methods for Univariate and Multivariate GARCH Models: a Survey

Virbickaitė, Audronė; Ausín, M. Concepción; Galeano, Pedro
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/02/2014 Português
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This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non-parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non-parametric approach.; Comment: 28 pages, 3 figures

A Closer Look at ADC Multivariate GARCH

Haftel, Jared
Fonte: Universidade Duke Publicador: Universidade Duke
Formato: 207528 bytes; application/pdf
Português
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In the past thirty years, academia and the marketplace have devoted signi cant e ort and resources toward gaining a better understanding of how volatility changes over time in the nancial markets and how changes in one market a ect changes in another. All of these attempts involve modeling the covariance matrix of two or more asset returns using the period-earlier covariance matrix. In this paper, we outline the volatility modeling process for an Antisymmetric Dynamic Covariance (ADC) multivariate Generalized Autoregressive Conditional Heteroskedacity (GARCH) model, explain the math involved, and attempt to estimate the parameters of the model using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm. We nd several barriers to estimating parameters using BFGS and suggest using alternative algorithms to estimate the ADC multivariate GARCH in the future.