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Choi, Chiu Yee. "A multivariate threshold stochastic volatility model /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?MATH%202005%20CHOI.
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Bongers, Martin B. "Multivariate volatility modelling in modern finance." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/4373.
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Includes abstract.Includes bibliographical references (leaves 100-101).
The aim of the study is to ascertain whether the information gained from the more complicated multivariate matrix decomposition models can be used to better forecast the covariance matrix and produce a Value at Risk estimate which more appropriately describes fat-tailed financial time-series.
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Marchese, Malvina. "Whittle estimation of multivariate exponential volatility models." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3173/.
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The aim of this thesis is to offer some insights into two topics of some interest for time-series econometric research. The first chapter derives the rates of convergence and the asymptotic normality of the pooled OLS estimators for linear regression panel models with mixed stationary and non-stationary regressors. This work is prompted by the consideration that many economic models of interest present a mixture of I(1) and I(0) regressors, for example models for analysis of demand system or for assessment of the relationship between growth and inequality. We present results for a model where the regressors and the regressand are cointegrated. We find that the OLS estimator is asymptotically normal with convergence rates T p n and p nT for respectively the non-stationary and the stationary regressors. Phillips and Moon (1990) show that in a cointegrated regression model with non-stationary regressors, the OLS estimator converges at a rate of T p n. We find that the presence of one stationary regressor in the model does not increases the rate of convergence. All the results are derived for sequential limits, with T going to infinity followed by n; and under quite restrictive regularity conditions. Chapters 3-5 focus on parametric multivariate exponential volatility models. It has long been recognized that the volatility of stock returns responds differently to good news and bad news. In particular, while negative shocks tend to increase future volatility, positive ones of the same size will increase it by less or even decrease it. This was in fact one of the chief motivations that led Nelson (1991) to introduce the univariate EGARCH model. More recently empirical studies have found that the asymmetry is a robust feature of multivariate stock returns series as well, and several multivariate volatility models have been developed to capture it. Another important property that characterizes the dynamic evolution of volatilities is that squared returns have significant autocorrelations that decay to zero at a slow rate, consistent with the notion of long memory, where the auto-covariances are not absolutely summable. Univariate long-memory volatility models have received a great deal of attention. However, the generalization to a multivariate long-memory volatility model has not been attempted in the literature. Chapter 3 offers a detailed literature review on multivariate volatility models. Chapter 4 and 5 introduce a new multivariate exponential volatility (MEV) model which captures long-range dependence in the volatilities, while retaining the martingale difference assumption and short-memory dependence in mean. Moreover the model captures cross-assets spillover effects, leverage and asymmetry. The strong consistency and the asymptotic normality of the Whittle estimator of the parameters in the Multivariate Exponential Volatility model is established under a variety of parameterization. The results cover both the case of exponentially and hyperbolically decaying coefficients, allowing for different degrees of persistence of shocks to the conditional variances. It is shown that the rate of convergence and the asymptotic normality of the Whittle estimates do not depend on the degree of persistence implied by the parameterization as the Whittle function automatically compensates for the possible lack of square integrability of the model spectral density.
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Eratalay, Mustafa Hakan. "Three essays on multivariate volatility modelling and estimation." Doctoral thesis, Universidad de Alicante, 2012. http://hdl.handle.net/10045/26482.
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Wang, Jian. "Real time estimation of multivariate stochastic volatility models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/16786/.
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This thesis firstly considers a modelling framework for multivariate volatility in financial time series. As most financial returns exhibit heavy tails and skewness, we are considering a model for the returns based on the skew-t distribution, while the volatility is assumed to follow a Wishart autoregressive process. We define a new type of Wishart autoregressive process and highlight some of its properties and some of its advantages. Particle filter based inference for this model is discussed and a novel approach of estimating static parameters is provided. Furthermore, an alternative methodology for estimating higher dimension data is developed. Secondly, inspired from the idea of Ulig's Wishart process, a new Wishart-Newton model is developed. The approach combines conjugate Bayesian inference while the hyper parameters are estimated by a Newton-Raphson method and here an online volatility estimate algorithm is proposed. The two proposed models are compared with the benchmarking GO-GARCH model in both function execution time and cumulative returns of different dimensional datasets.
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Platanioti, Kiriaki. "Inference for multivariate stochastic volatility and related models." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501781.
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Loddo, Antonello. "Bayesian analysis of multivariate stochastic volatility and dynamic models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4359.
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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (April 26, 2007) Vita. Includes bibliographical references.
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Vestweber, Johanna [Verfasser]. "Geometric ergodicity of multivariate stochastic volatility models / Johanna Vestweber." Ulm : Universität Ulm, 2018. http://d-nb.info/1151938378/34.
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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.
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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.Series: Research Report Series / Department of Statistics and Mathematics
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Gribisch, Bastian [Verfasser]. "Modeling and Forecasting of Multivariate Stock Market Volatility / Bastian Gribisch." Kiel : Universitätsbibliothek Kiel, 2013. http://d-nb.info/1031914897/34.
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Marius, Matei. "A Contribution to Multivariate Volatility Modeling with High Frequency Data." Doctoral thesis, Universitat Ramon Llull, 2012. http://hdl.handle.net/10803/81072.
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La tesi desenvolupa el tema de la predicció de la volatilitat financera en el context de l’ús de
dades d’alta freqüència, i se centra en una línia de recerca doble: proposar models alternatius que millorarien la predicció de la volatilitat i classificar els models de volatilitat ja existents com els que es proposen en aquesta tesi.
Els objectius es poden classificar en tres categories. El primer consisteix en la proposta d’un nou mètode de predicció de la volatilitat que segueix una línia de recerca desenvolupada recentment, la qual apunta al fet de mesurar la volatilitat intradia, com també la nocturna. Es proposa una categoria de models realized GARCH bivariants. El segon objectiu consisteix en la proposta d’una metodologia per predir la volatilitat diària multivariant amb models autoregressius que utilitzen estimacions de volatilitat diària (i nocturna, en el cas dels bivariants), a més d’informació d’alta freqüència, quan se’n disposava. S’aplica l’anàlisi de components principals (ACP) a un conjunt de models de tipus realized GARCH univariants i bivariants. El mètode representa una extensió d’un model ja existent (PC-GARCH) que estimava un model GARCH multivariant a partir de l’estimació de models GARCH univariants dels components principals de les variables inicials. El tercer objectiu de la tesi és classificar el rendiment dels models de predicció de la volatilitat ja existents o dels nous, a més de la precisió de les mesures intradia que s’utilitzaven en les estimacions dels models.
En relació amb els resultats, s’observa que els models EGARCHX, realized EGARCH i realized GARCH(2,2) obtenen una millor valoració, mentre que els models GARCH i no realized EGARCH obtenen uns resultats inferiors en gairebé totes les proves. Això permet concloure que el fet d’incorporar mesures de volatilitat intradia millora el problema de la modelització. Quant a la classificació dels models realized bivariants, s’observa que tant els models realized GARCH bivariant (en versions completes i parcials) com el model realized EGARCH bivariant obtenen millors resultats; els segueixen els models realized GARCH(2,2) bivariant, EGARCH bivariant I EGARCHX bivariant. En comparar les versions bivariants amb les univariants, amb l’objectiu d’investigar si l’ús de mesures de volatilitat nocturna a les equacions dels models millora l’estimació de la volatilitat, es mostra que els models bivariants superen els univariants. Els resultats proven que els models bivariants no són totalment inferiors als seus homòlegs univariants, sinó que resulten ser bones alternatives per utilitzar-los en la predicció, juntament amb els models univariants, per tal d’obtenir unes estimacions més fiables.La tesis desarrolla el tema de la predicción de la volatilidad financiera en el contexto del uso de datos de alta frecuencia, y se centra en una doble línea de investigación: la de proponer modelos alternativos que mejorarían la predicción de la volatilidad y la de clasificar modelos de volatilidad ya existentes como los propuestos en esta tesis. Los objetivos se pueden clasificar en tres categorías. El primero consiste en la propuesta de un nuevo método de predicción de la volatilidad que sigue una línea de investigación recientemente desarrollada, la cual apunta al hecho de medir la volatilidad intradía, así como la nocturna. Se propone una categoría de modelos realized GARCH bivariantes. El segundo objetivo consiste en proponer una metodología para predecir la volatilidad diaria multivariante con modelos autorregresivos que utilizaran estimaciones de volatilidad diaria (y nocturna, en el caso de los bivariantes), además de información de alta frecuencia, si la había disponible. Se aplica el análisis de componentes principales (ACP) a un conjunto de modelos de tipo realized GARCH univariantes y bivariantes. El método representa una extensión de un modelo ya existente (PCGARCH) que calculaba un modelo GARCH multivariante a partir de la estimación de modelos GARCH univariantes de los componentes principales de las variables iniciales. El tercer objetivo de la tesis es clasificar el rendimiento de los modelos de predicción de la volatilidad ya existentes o de los nuevos, así como la precisión de medidas intradía utilizadas en las estimaciones de los modelos. En relación con los resultados, se observa que los modelos EGARCHX, realized EGARCH y GARCH(2,2) obtienen una mejor valoración, mientras que los modelos GARCH y no realized EGARCH obtienen unos resultados inferiores en casi todas las pruebas. Esto permite concluir que el hecho de incorporar medidas de volatilidad intradía mejora el problema de la modelización. En cuanto a la clasificación de modelos realized bivariantes, se observa que tanto los modelos realized GARCH bivariante (en versiones completas y parciales) como realized EGARCH bivariante obtienen mejores resultados; les siguen los modelos realized GARCH(2,2) bivariante, EGARCH bivariante y EGARCHX bivariante. Al comparar las versiones bivariantes con las univariantes, con el objetivo de investigar si el uso de medidas de volatilidad nocturna en las ecuaciones de los modelos mejora la estimación de la volatilidad, se muestra que los modelos bivariantes superan los univariantes. Los resultados prueban que los modelos bivariantes no son totalmente inferiores a sus homólogos univariantes, sino que resultan ser buenas alternativas para utilizarlos en la predicción, junto con los modelos univariantes, para lograr unas estimaciones más fiables.
The thesis develops the topic of financial volatility forecasting in the context of the usage of high frequency data, and focuses on a twofold line of research: that of proposing alternative models that would enhance volatility forecasting and that of ranking existing or newly proposed volatility models. The objectives may be disseminated in three categories. The first scope constitutes of the proposal of a new method of volatility forecasting that follows a recently developed research line that pointed to using measures of intraday volatility and also of measures of night volatility, the need for new models being given by the question whether adding measures of night volatility improves day volatility estimations. As a result, a class of bivariate realized GARCH models was proposed. The second scope was to propose a methodology to forecast multivariate day volatility with autoregressive models that used day (and night for bivariate) volatility estimates, as well as high frequency information when that was available. For this, the Principal Component algorithm (PCA) was applied to a class of univariate and bivariate realized GARCH-type of models. The method represents an extension of one existing model (PC GARCH) that estimated a multivariate GARCH model by estimating univariate GARCH models of the principal components of the initial variables. The third goal of the thesis was to rank the performance of existing or newly proposed volatility forecasting models, as well as the accuracy of the intraday measures used in the realized models estimations. With regards to the univariate realized models’ rankings, it was found that EGARCHX, Realized EGARCH and Realized GARCH(2,2) models persistently ranked better, while the non-realized GARCH and EGARCH models performed poor in each stance almost. This allowed us to conclude that incorporating measures of intraday volatility enhances the modeling problem. With respect to the bivariate realized models’ ranking, it was found that Bivariate Realized GARCH (partial and complete versions) and Bivariate Realized EGARCH models performed the best, followed by the Bivariate Realized GARCH(2,2), Bivariate EGARCH and Bivariate EGARCHX models. When the bivariate versions were compared to the univariate ones in order to investigate whether using night volatility measurements in the models’ equations improves volatility estimation, it was found that the bivariate models surpassed the univariate ones when specific methodology, ranking criteria and stocks were used. The results were mixed, allowing us to conclude that the bivariate models did not prove totally inferior to their univariate counterparts, proving as good alternative options to be used in the forecasting exercise, together with the univariate models, for more reliable estimates. Finally, the PC realized models and PC bivariate realized models were estimated and their performances were ranked; improvements the PC methodology brought in high frequency multivariate modeling of stock returns were also discussed. PC models were found to be highly effective in estimating multivariate volatility of highly correlated stock assets and suggestions on how investors could use them for portfolio selection were made.
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Ng, Fo-chun, and 伍科俊. "Some topics in correlation stress testing and multivariate volatility modeling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206653.
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This thesis considers two important problems in finance, namely, correlation stress testing and multivariate volatility modeling.
Correlation stress testing refers to the correlation matrix adjustment to evaluate potential impact of the changes in correlations under financial crises. Very often, some correlations are explicitly adjusted (core correlations), with the remainder left unspecified (peripheral correlations), although it would be more natural for both core correlations and peripheral correlations to vary. However, most existing methods ignored the potential change in peripheral correlations. Inspiring from this idea, two methods are proposed in which the stress impact on the core correlations is transmitted to the peripheral correlations through the dependence structure of the empirical correlations.
The first method is based on a Bayesian framework in which a prior for a population correlation matrix is proposed that gives flexibility in specifying the dependence structure of correlations. In order to increase the rate of convergence, the algorithm of posterior simulation is extended so that two correlations can be updated in one Gibbs sampler step. To achieve this, an algorithm is developed to find the region of two correlations keeping the correlation matrix positive definite given that all other correlations are held fixed.
The second method is a Black-Litterman approach applied to correlation matrices. A new correlation matrix is constructed by maximizing the posterior density. The proposed method can be viewed as a two-step procedure: first constructing a target matrix in a data-driven manner, and then regularizing the target matrix by minimizing a matrix norm that reasonably reflects the dependence structure of the empirical correlations.
Multivariate volatility modeling is important in finance since variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high frequency returns as the ex-post realization of the covariance matrix of low frequency returns. For the analysis of dynamics of RCOV matrices, the generalized conditional autoregressive Wishart model is proposed. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The proposed model is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments and stationarity are discussed. The forecasting performance of the proposed model is compared with the existing models.
Outliers exist in the series of realized volatility which is often decomposed into continuous and jump components. The vector multiplicative error model is a natural choice to jointly model these two non-negative components of the realized volatility, which is also a popular multivariate time series model for other non-negative volatility measures. Diagnostic checking of such models is considered by deriving the asymptotic distribution of residual autocorrelations. A multivariate portmanteau test is then devised. Simulation experiments are carried out to investigate the performance of the asymptotic result in finite samples.published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Heracleous, Maria S. "Volatility Modeling Using the Student's t Distribution." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/29126.
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Over the last twenty years or so the Dynamic Volatility literature has produced a wealth of univariate and multivariate GARCH type models. While the univariate models have been relatively successful in empirical studies, they suffer from a number ofweaknesses, such as unverifiable parameter restrictions, existence of moment conditions and the retention of Normality. These problems are naturally more acute in the multivariate GARCH type models, which in addition have the problem of overparameterization.
This dissertation uses the Student's t distribution and follows the Probabilistic Reduction (PR) methodology to modify and extend the univariate and multivariate volatility models viewed as alternative to the GARCH models. Its most important advantage is that it gives rise to internally consistent statistical models that do not require ad hoc parameter restrictions unlike the GARCH formulations.
Chapters 1 and 2 provide an overview of my dissertation and recent developments in the volatility literature. In Chapter 3 we provide an empirical illustration of the PR approach for modeling univariate volatility. Estimation results suggest that the Student's t AR model is a parsimonious and statistically adequate representation of exchange rate returns and Dow Jones returns data. Econometric modeling based on the Student's t distribution introduces an additional variable - the degree of freedom parameter. In Chapter 4 we focus on two questions relating to the `degree of freedom' parameter. A simulation study is used to examine:(i) the ability of the kurtosis coefficient to accurately capture the implied degrees of freedom, and (ii) the ability of Student's t GARCH model to estimate the true degree of freedom parameter accurately. Simulation results reveal that the kurtosis coefficient and the Student's t GARCH model (Bollerslev, 1987) provide biased and inconsistent estimators of the degree of freedom parameter.
Chapter 5 develops the Students' t Dynamic Linear Regression (DLR) }model which allows us to explain univariate volatility in terms of: (i) volatility in the past history of the series itself and (ii) volatility in other relevant exogenous variables. Empirical results of this chapter suggest that the Student's t DLR model provides a promising way to model volatility. The main advantage of this model is that it is defined in terms of observable random variables and their lags, and not the errors as is the case with the GARCH models. This makes the inclusion of relevant exogenous variables a natural part of the model set up.
In Chapter 6 we propose the Student's t VAR model which deals effectively with several key issues raised in the multivariate volatility literature. In particular, it ensures positive definiteness of the variance-covariance matrix without requiring any unrealistic coefficient restrictions and provides a parsimonious description of the conditional variance-covariance matrix by jointly modeling the conditional mean and variance functions.Ph. D.
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Stelzer, Robert Josef. "Multivariate continuous time stochastic volatility models driven by a Lévy process." kostenfrei, 2007. http://mediatum2.ub.tum.de/doc/624065/document.pdf.
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Le, Trung Thanh. "Essays on multivariate volatility models : an application to emerging financial markets." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3798/.
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This thesis is an empirical study of how multivariate models can be applied to analyze the dependence between emerging financial markets and the US financial market. This thesis comprises of 3 complete papers which will use this data set as follows. The first paper is an comparative research on estimations and evaluations of 54 individual volatility models which belong to 10 different model classes being the Riskmetrics models, the Constant model (CCC), the Orthogonal-GARCH model (O-GARCH), the Dynamic Conditional Correlation model (DCC), the Asymmetric DCC model (ADCC), the Consistent DCC model (CDCC) and the Student’s t-DCC model (TDCC). All of these models were estimated and then ranked by using both in-sample and out of sample performances. This research is to emphasize the importance of model selection in modeling the volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility models to analyze the volatilities and correlations of the emerging markets. Specifically, the pair-wise conditional correlations between each of the emerging markets and the US market, generated by the TDCC model, were used to perform empirical tests for the contagion of the 3 recent financial crises which are the Dotcom crisis in 2000, the Sub-prime in 2007-2008 and the Global financial crisis in 2008-2009. The use of the TDCC model which assumes a Student’s t-distribution is greatly meaningful for the empirical tests for contagion as it deals with the fat-tailed behaviours of the financial data. The third paper is the application of multivariate copula, which provides a connection between the univariate distributions and the multivariate distribution inside the DCC model, to analyze the emerging data. The flexibility of the copula model that separates the multivariate distribution assumption from those univariate series allows us to have an efficient examination of the dependence structure of emerging financial markets. Following success of the copula models in recent studies, our research, which is the first to use the copula model to analyze high-dimensional data, confirms a significant improvement of the copula from the standard DCC model.
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Noureldin, Diaa. "Essays on multivariate volatility and dependence models for financial time series." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:fdf82d35-a5e7-4295-b7bf-c7009cad7b56.
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This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields for the period 1986:3-2010:12, our in-sample analysis indicates model stability and we show statistically significant gains due to allowing for a time-varying dependence structure which permits joint extreme factor movements. Our out-of-sample analysis indicates the model's superior ability to forecast the conditional mean in terms of root mean square error reductions and directional forecast accuracy. The forecast gains are stronger during the recent financial crisis. We also conduct out-of-sample model evaluation based on conditional density forecasts. The second paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. The third paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting. The key idea is to rotate the returns and then fit them using a BEKK model for the conditional covariance with the identity matrix as the covariance target. The extension to DCC type models is given, enriching this class. We focus primarily on diagonal BEKK and DCC models, and a related parameterisation which imposes common persistence on all elements of the conditional covariance matrix. Inference for these models is computationally attractive, and the asymptotics is standard. The techniques are illustrated using recent data on the S&P 500 ETF and some DJIA stocks, including comparisons to the related orthogonal GARCH models.
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Radeschnig, David. "Modelling Implied Volatility of American-Asian Options : A Simple Multivariate Regression Approach." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28951.
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This report focus upon implied volatility for American styled Asian options, and a least squares approximation method as a way of estimating its magnitude. Asian option prices are calculated/approximated based on Quasi-Monte Carlo simulations and least squares regression, where a known volatility is being used as input. A regression tree then empirically builds a database of regression vectors for the implied volatility based on the simulated output of option prices. The mean squared errors between imputed and estimated volatilities are then compared using a five-folded cross-validation test as well as the non-parametric Kruskal-Wallis hypothesis test of equal distributions. The study results in a proposed semi-parametric model for estimating implied volatilities from options. The user must however be aware of that this model may suffer from bias in estimation, and should thereby be used with caution.
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Andersson, Markus. "Multivariate Financial Time Series and Volatility Models with Applications to Tactical Asset Allocation." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175326.
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The financial markets have a complex structure and the modelling techniques have recently been more and more complicated. So for a portfolio manager it is very important to find better and more sophisticated modelling techniques especially after the 2007-2008 banking crisis. The idea in this thesis is to find the connection between the components in macroeconomic environment and portfolios consisting of assets from OMX Stockholm 30 and use these relationships to perform Tactical Asset Allocation (TAA). The more specific aim of the project is to prove that dynamic modelling techniques outperform static models in portfolio theory.Den finansiella marknaden är av en väldigt komplex struktur och modelleringsteknikerna har under senare tid blivit allt mer komplicerade. För en portföljförvaltare är det av yttersta vikt att finna mer sofistikerade modelleringstekniker, speciellt efter finanskrisen 2007-2008. Idéen i den här uppsatsen är att finna ett samband mellan makroekonomiska faktorer och aktieportföljer innehållande tillgångar från OMX Stockholm 30 och använda dessa för att utföra Tactial Asset Allocation (TAA). Mer specifikt är målsättningen att visa att dynamiska modelleringstekniker har ett bättre utfall än mer statiska modeller i portföljteori.
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Feng, Gang [Verfasser], and Jens-Peter [Akademischer Betreuer] Kreiß. "Bootstrap Methods for Univariate and Multivariate Volatility / Gang Feng ; Betreuer: Jens-Peter Kreiß." Braunschweig : Technische Universität Braunschweig, 2015. http://d-nb.info/117581962X/34.
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Williams, Julian. "Multivariate financial econometrics : with applications to volatility modelling, option pricing and asset allocation." Thesis, University of Bath, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437728.
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Nicolas, José. "Politiques macroéconomiques et volatilité des marchés boursiers, analyse de modèles ICAPM multivariés sous hypothèses de covariances conditionnelles." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38166.pdf.
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Clark, Natalie. "Option Volume, Market Sentiment, and Future Performance and Volatility." Ohio University Honors Tutorial College / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1524761369518974.
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Zhou, Jin Shun. "Transmission of equity returns and volatility in Asia-Pacific markets : a multivariate GARCH analysis." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b1951112.
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Jiang, Dongchen. "A comparative study on large multivariate volatility matrix modeling for high-frequency financial data." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/587.
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Modeling and forecasting the volatilities of high-frequency data observed on the prices of financial assets are vibrant research areas in econometrics and statistics. However, most of the available methods are not directly applicable when the number of assets involved is large, due to the lack of accuracy in estimating high-dimensional matrices. This paper compared two methodologies of vast volatility matrix estimation for high-frequency data. One is to estimate the Average Realized Volatility Matrix and to regularize it by banding and thresholding. In this method, first we select grids as pre-sampling frequencies,construct a realized volatility matrix using previous tick method according to each pre-sampling frequency and then take the average of the constructed realized volatility matrices as the stage one estimator, which we call the ARVM estimator. Then we regularize the ARVM estimator to yield good consistent estimators of the large integrated volatility matrix. We consider two regularizations: thresholding and banding. The other is Dynamic Conditional Correlation(DCC) which can be estimated for two stage, where in the rst stage univariate GARCH models are estimated for each residual series, and in the second stage, the residuals are used to estimate the parameters of the dynamic correlation. Asymptotic theory for the two proposed methodologies shows that the estimator are consistent. In numerical studies, the proposed two methodologies are applied to simulated data set and real high-frequency prices from top 100 S&P 500 stocks according to the trading volume over a period of 3 months, 64 trading days in 2013. From the perfomances of estimators, the conclusion is that TARVM estimator performs better than DCC volatility matrix. And its largest eigenvalues are more stable than those of DCC model so that it is more approriable in eigen-based anaylsis.
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Kubilay, Mustafa Murat. "The Volatility Spillover Among A Country." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614244/index.pdf.
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The purpose of this study is to examine the volatility spillover among a country&rsquos foreign exchange, bond and stock markets and the volatility transmission from the global bond, stock and commodity markets to these local financial markets. The sample for the study includes data from both emerging and developed economies in the time period between 2004 and 2011. A multivariate GARCH methodology with the BEKK representation is applied for the local financial markets and global variables are included as exogenous variables into the model. The volatility integration of the financial markets of the emerging economies is stronger compared to the integration of the developed economies. Global variables have a spillover effect on the developed markets only after the global financial crisis, whereas they significantly affect the volatility in emerging markets for both the pre- and post-crisis period. North American countries in the sample, U.S. and Mexico, have low local volatility integration in the pre-crisis era and the integration rises in the post-crisis period. Moreover, they are more open to the internal and global short-term shocks in the post-crisis period. Germany and Turkey are the representatives of the EMEA (Europe, Middle East and Africa) region and they have high local market integration and are open to global shocks for both sub-periods. Far Eastern markets, Japan and Korea, also have high local market integration and their vulnerability to the global effects is large and getting larger for the post-crisis period. The most important limitation of this thesis is the difficulty of reaching sharp generalizations due to the small number of countries analyzed. This limitation can be addressed by the inclusion of a larger number of geographically dispersed countries. The most noteworthy originality of this study is the addition of the exogenous global variables for modeling volatility spillovers. Furthermore, comparison of results for emerging versus developed markets and the pre- versus post-crisis periods is another contribution of this study to the existing literature. The findings of this study can be used by investors interested in assessing the risks of investing internationally.
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Sanhaji, Bilel. "Modélisation multivariée hétéroscédastique et transmission financière." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM2029/document.
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Cette thèse de doctorat composée de trois chapitres contribue au développement de tests statistiques et à analyser la transmission financière dans un cadre multivarié hétéroscédastique. Le premier chapitre propose deux tests du multiplicateur de Lagrange de constance des corrélations conditionnelles dans les modèles GARCH multivariés. Si l'hypothèse nulle repose sur des corrélations conditionnelles constantes, l'hypothèse alternative propose une première spécification basée sur des réseaux de neurones artificiels et une seconde représentée par une forme fonctionnelle inconnue qui est linéarisée à l'aide d'un développement de Taylor.Dans le deuxième chapitre, un nouveau modèle est introduit dans le but de tester la non-linéarité des (co)variances conditionnelles. Si l'hypothèse nulle repose sur une fonction linéaire des innovations retardées au carré et des (co)variances conditionnelles, l'hypothèse alternative se caractérise quant à elle par une fonction de transition non-linéaire : exponentielle ou logistique ; une configuration avec effets de levier est également proposée. Dans les deux premiers chapitres, les expériences de simulations et les illustrations empiriques montrent les bonnes performances de nos tests de mauvaise spécification.Le dernier chapitre étudie la transmission d'information en séance et hors séance de cotation en termes de rendements et de volatilités entre la Chine, l'Amérique et l'Europe. Le problème d'asynchronicité est considéré avec soin dans la modélisation bivariée avec la Chine comme référenceThis Ph.D. thesis composed by three chapters contributes to the development of test statistics and to analyse financial transmission in a multivariate heteroskedastic framework.The first chapter proposes two Lagrange multiplier tests of constancy of conditional correlations in multivariate GARCH models. Whether the null hypothesis is based on constant conditional correlations, the alternative hypothesis proposes a first specification based on artificial neural networks, and a second specification based on an unknown functional form linearised by a Taylor expansion.In the second chapter, a new model is introduced in order to test for nonlinearity in conditional (co)variances. Whether the null hypothesis is based on a linear function of the lagged squared innovations and the conditional (co)variances, the alternative hypothesis is characterised by a nonlinear exponential or logistic transition function; a configuration with leverage effects is also proposed.In the two first chapters, simulation experiments and empirical illustrations show the good performances of our misspecification tests.The last chapter studies daytime and overnight information transmission in terms of returns and volatilities between China, America and Europe. The asynchronicity issue is carefully considered in the bivariate modelling with China as benchmark
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Heiden, Moritz Daniel [Verfasser], and Yarema [Akademischer Betreuer] Okhrin. "Asymmetry and nonlinearity in forecasting multivariate stock market volatility / Moritz Daniel Heiden. Betreuer: Yarema Okhrin." Augsburg : Universität Augsburg, 2016. http://d-nb.info/1084583909/34.
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Malherbe, Chanel. "Fourier method for the measurement of univariate and multivariate volatility in the presence of high frequency data." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/4386.
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Zheng, Lingyu. "Estimation of the linkage matrix in O-GARCH model and GO-GARCH model." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/102486.
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StatisticsPh.D.
We propose new estimation methods for the factor loading matrix in modeling multivariate volatility processes. The key step of the methods is based on the weighted scatter estimators, which does not involve optimizing any objective function and was embedded with robust estimation properties. The method can therefore be easily applied to high-dimensional systems without running into computational problems. The estimation is proved to be consistent and the asymptotic distribution is derived. We compare the performance with other estimation methods and demonstrate its superiority when using both simulated data as well as real-world case studies.
Temple University--Theses
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Khalfaoui, Rabeh. "Wavelet analysis of financial time series." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM1083.
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Cette thèse traite la contribution des méthodes d'ondelettes sur la modélisation des séries temporelles économiques et financières et se compose de deux parties: une partie univariée et une partie multivariée. Dans la première partie (chapitres 2 et 3), nous adoptons le cas univarié. Premièrement, nous examinons la classe des processus longue mémoire non-stationnaires. Une étude de simulation a été effectuée afin de comparer la performance de certaines méthodes d'estimation semi-paramétrique du paramètre d'intégration fractionnaire. Nous examinons aussi la mémoire longue dans la volatilité en utilisant des modèles FIGARCH pour les données de l'énergie. Les résultats montrent que la méthode d'estimation Exact Local Whittle de Shimotsu et Phillips [2005] est la meilleure méthode de détection de longue mémoire et la volatilité du pétrole exhibe une forte évidence de phénomène de mémoire longue. Ensuite, nous analysons le risque de marché des séries de rendements univariées de marchés boursier, qui est mesurée par le risque systématique (bêta) à différents horizons temporels. Les résultats montrent que le Bêta n'est pas stable, en raison de multi-trading stratégies des investisseurs. Les résultats basés sur l'analyse montrent que le risque mesuré par la VaR est plus concentrée aux plus hautes fréquences. La deuxième partie (chapitres 4 et 5) traite l'estimation de la variance et la corrélation conditionnelle des séries temporelles multivariées. Nous considérons deux classes de séries temporelles: les séries temporelles stationnaires (rendements) et les séries temporelles non-stationnaires (séries en niveaux)This thesis deals with the contribution of wavelet methods on modeling economic and financial time series and consists of two parts: the univariate time series and multivariate time series. In the first part (chapters 2 and 3), we adopt univariate case. First, we examine the class of non-stationary long memory processes. A simulation study is carried out in order to compare the performance of some semi-parametric estimation methods for fractional differencing parameter. We also examine the long memory in volatility using FIGARCH models to model energy data. Results show that the Exact local Whittle estimation method of Shimotsu and Phillips [2005] is the better one and the oil volatility exhibit strong evidence of long memory. Next, we analyze the market risk of univariate stock market returns which is measured by systematic risk (beta) at different time horizons. Results show that beta is not stable, due to multi-trading strategies of investors. Results based on VaR analysis show that risk is more concentrated at higher frequency. The second part (chapters 4 and 5) deals with estimation of the conditional variance and correlation of multivariate time series. We consider two classes of time series: the stationary time series (returns) and the non-stationary time series (levels). We develop a novel approach, which combines wavelet multi-resolution analysis and multivariate GARCH models, i.e. the wavelet-based multivariate GARCH approach. However, to evaluate the volatility forecasts we compare the performance of several multivariate models using some criteria, such as loss functions, VaR estimation and hedging strategies
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Antonakakis, Nikolaos. "Exchange Return Co-movements and Volatility Spillovers Before and After the Introduction of Euro." Elsevier, 2012. http://dx.doi.org/10.1016/j.intfin.2012.05.009.
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This paper examines return co-movements and volatility spillovers between major exchange rates before and after the introduction of euro. Dynamic correlations and VAR-based spillover index results suggest significant return co-movements and volatility spillovers, however, their extend is, on average, lower in the post-euro period. Co-movements and spillovers are positively associated with extreme episodes and US dollar appreciations. The euro (Deutsche mark) is the dominant net transmitter of volatility, while the British pound the dominant net receiver of volatility in both periods. Nevertheless, cross-market volatility spillovers are bidirectional, and the highest spillovers occur between European markets. (author's abstract)
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Higgs, Helen. "Price and volatility relationships in the Australian electricity market." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16404/.
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This thesis presents a collection of papers that has been published, accepted or submitted for publication. They assess price, volatility and market relationships in the five regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales (NSW), Queensland (QLD), South Australia (SA), the Snowy Mountains Hydroelectric Scheme (SNO) and Victoria (VIC). The transmission networks that link regional systems via interconnectors across the eastern states have played an important role in the connection of the regional markets into an efficient national electricity market. During peak periods, the interconnectors become congested and the NEM separates into its regions, promoting price differences across the market and exacerbating reliability problems in regional utilities. This thesis is motivated in part by the fact that assessment of these prices and volatility within and between regional markets allows for better forecasts by electricity producers, transmitters and retailers and the efficient distribution of energy on a national level. The first two papers explore whether the lagged price and volatility information flows of the connected spot electricity markets can be used to forecast the pricing behaviour of individual markets. A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of price and volatility spillovers within (intra-relationship) and across (inter-relationship) the various spot markets. The results show evidence of the fact that prices in one market can be explained by their own price lagged one-period and are independent of lagged spot prices of any other markets when daily data is employed. This implies that the regional spot electricity markets are not fully integrated. However, there is also evidence of a large number of significant ownvolatility and cross-volatility spillovers in all five markets indicating that shocks in some markets will affect price volatility in others. Similar conclusions are obtained when the daily data are disaggregated into peak and off-peak periods, suggesting that the spot electricity markets are still rather isolated. These results inspired the research underlying the third paper of the thesis on modelling the dynamics of spot electricity prices in each regional market. A family of generalised autoregressive conditional heteroskedasticity (GARCH), RiskMetrics, normal Asymmetric Power ARCH (APARCH), Student APARCH and skewed Student APARCH is used to model the time-varying variance in prices with the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The important contribution in this paper lies in the use of two latter methodologies, namely, the Student APARCH and skewed Student APARCH which take account of the skewness and fat tailed characteristics of the electricity spot price series. The results indicate significant innovation spillovers (ARCH effects) and volatility spillovers (GARCH effects) in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information (that is, positive shocks or good news are associated with higher volatility than negative shocks or bad news). The fourth research paper attempts to capture salient feature of price hikes or spikes in wholesale electricity markets. The results show that electricity prices exhibit stronger mean-reversion after a price spike than the mean-reversion in the normal period, suggesting the electricity price quickly returns from some extreme position (such as a price spike) to equilibrium; this is, extreme price spikes are shortlived. Mean-reversion can be measured in a separate regime from the normal regime using Markov probability transition to identify the different regimes. The fifth and final paper investigates whether interstate/regional trade has enhanced the efficiency of each spot electricity market. Multiple variance ratio tests are used to determine if Australian spot electricity markets follow a random walk; that is, if they are informationally efficient. The results indicate that despite the presence of a national market only the Victorian market during the off-peak period is informationally (or market) efficient and follows a random walk. This thesis makes a significant contribution in estimating the volatility and the efficiency of the wholesale electricity prices by employing four advanced time series techniques that have not been previously explored in the Australian context. An understanding of the modelling and forecastability of electricity spot price volatility across and within the Australian spot markets is vital for generators, distributors and market regulators. Such an understanding influences the pricing of derivative contracts traded on the electricity markets and enables market participants to better manage their financial risks.
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Elezovic, Suad. "Modeling financial volatility : A functional approach with applications to Swedish limit order book data." Doctoral thesis, Umeå universitet, Statistik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-18757.
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This thesis is designed to offer an approach to modeling volatility in the Swedish limit order market. Realized quadratic variation is used as an estimator of the integrated variance, which is a measure of the variability of a stochastic process in continuous time. Moreover, a functional time series model for the realized quadratic variation is introduced. A two-step estimation procedure for such a model is then proposed. Some properties of the proposed two-step estimator are discussed and illustrated through an application to high-frequency financial data and simulated experiments. In Paper I, the concept of realized quadratic variation, obtained from the bid and ask curves, is presented. In particular, an application to the Swedish limit order book data is performed using signature plots to determine an optimal sampling frequency for the computations. The paper is the first study that introduces realized quadratic variation in a functional context. Paper II introduces functional time series models and apply them to the modeling of volatility in the Swedish limit order book. More precisely, a functional approach to the estimation of volatility dynamics of the spreads (differences between the bid and ask prices) is presented through a case study. For that purpose, a two-step procedure for the estimation of functional linear models is adapted to the estimation of a functional dynamic time series model. Paper III studies a two-step estimation procedure for the functional models introduced in Paper II. For that purpose, data is simulated using the Heston stochastic volatility model, thereby obtaining time series of realized quadratic variations as functions of relative quantities of shares. In the first step, a dynamic time series model is fitted to each time series. This results in a set of inefficient raw estimates of the coefficient functions. In the second step, the raw estimates are smoothed. The second step improves on the first step since it yields both smooth and more efficient estimates. In this simulation, the smooth estimates are shown to perform better in terms of mean squared error. Paper IV introduces an alternative to the two-step estimation procedure mentioned above. This is achieved by taking into account the correlation structure of the error terms obtained in the first step. The proposed estimator is based on seemingly unrelated regression representation. Then, a multivariate generalized least squares estimator is used in a first step and its smooth version in a second step. Some of the asymptotic properties of the resulting two-step procedure are discussed. The new procedure is illustrated with functional high-frequency financial data.
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Barthel, Nicole [Verfasser], Claudia [Akademischer Betreuer] Czado, Paul [Gutachter] Janssen, Harry [Gutachter] Joe, and Claudia [Gutachter] Czado. "Vine based models for multivariate volatility time-series and time-to-event data / Nicole Barthel ; Gutachter: Paul Janssen, Harry Joe, Claudia Czado ; Betreuer: Claudia Czado." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1187917419/34.
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Moura, Rodolfo Chiabai. "Spillovers and jumps in global markets: a comparative analysis." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-02082018-160351/.
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We analyze the relation between volatility spillovers and jumps in financial markets. For this, we compared the volatility spillover index proposed by Diebold and Yilmaz (2009) with a global volatility component, estimated through a multivariate stochastic volatility model with jumps in the mean and in the conditional volatility. This model allows a direct dating of events that alter the global volatility structure, based on a permanent/transitory decomposition in the structure of returns and volatilities, and also the estimation of market risk measures. We conclude that the multivariate stochastic volatility model solves some limitations in the spillover index and can be a useful tool in measuring and managing risk in global financial markets.Analisamos a relação existente entre spillovers e saltos na volatilidade nos mercados financeiros. Para isso, comparamos o índice de spillover de volatilidade proposto por Diebold and Yilmaz (2009), com um componente de volatilidade global, estimado através de um modelo multivariado de volatilidade estocástica com saltos na média e na volatilidade condicional. Este modelo permite uma datação direta dos eventos que alteram a estrutura de volatilidade global, baseando-se na decomposição das estruturas de retorno e volatilidade entre efeitos permanentes/transitórios, como também a estimação de medidas de risco de mercado. Concluímos que este modelo resolve algumas das limitações do índice de spillover além de fornecer um método prático para mensurar e administrar o risco nos mercados financeiros globais.
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DELLA, NOCE MATTEO. "Un modello VAR-GARCH multivariato per il mercato elettrico italiano." Doctoral thesis, Università Cattolica del Sacro Cuore, 2011. http://hdl.handle.net/10280/1108.
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E’ stato estesamente appurato che i mercati dell'elettricità mostrano mean-reversion e elevata volatilità dei prezzi. Questo lavoro utilizza un modello VAR-MGARCH al fine di cogliere queste caratteristiche presenti sul mercato dell'energia elettrica italiana (IPEX) e analizzare le interrelazioni esistenti tra le diverse regioni in cui il mercato è suddiviso. L’analisi è condotta sui prezzi giornalieri dal 1 ° gennaio 2006 al 31 dicembre 2008.
I coefficienti stimati dalle equazioni condizionali indicano che i mercati regionali sono abbastanza integrati e i prezzi regionali dell'energia elettrica possono essere adeguatamente previsti impiegando i prezzi passati di ciascun mercato zonale.
La volatilità e la cross-volatility sono significative per tutti i mercati, indicando la presenza di forti componenti ARCH e GARCH e la sostanziale inefficienza dei mercati. E’ inoltre evidente un’elevata persistenza della volatilità e della cross-volatility in tutti i mercati.
I risultati indicano inoltre che gli shock rilevati, sia nella volatilità, sia nei vari mercati, persistono nel tempo e che in ogni mercato la persistenza è più marcata quando è causata da innovazioni stimate sulle stesso mercato rispetto a shock stimati su altre aree. Questa persistenza descrive la tendenza delle variazioni dei prezzi a raggrupparsi nel tempo.It is commonly known that spot electricity markets show mean-reversion and high price volatility. This work employs a VAR-MGARCH model to capture these features in the Italian electricity market (IPEX) and analyze the interrelation existing among the different regions in which the market is divided. Daily spot prices from 1 January 2006 to 31 December 2008 are employed. The estimated coefficients from the conditional mean equations indicate that the regional markets are quite integrated and regional electricity prices could be usefully forecasted using lagged prices from either the same market or from the other areal markets. Volatility and cross-volatility spill-overs are significant for all markets, indicating the presence of strong ARCH and GARCH effects and market inefficiency. Strong persistence of volatility and cross-volatility are also evident in all local markets. The results also indicate that volatility innovations or shocks in all markets persist over time and that in every market this persistence is more marked for own-innovations or shocks than cross-innovations or shocks. This persistence captures the propensity of price changes of similar magnitude to cluster in time.
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Corrêa, Ana Carolina Costa. "Interdependência e assimetria de retornos e volatilidade dos ADRs da América Latina em relação aos mercados desenvolvidos durante a crise do subprime: um estudo multivariado." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/96/96132/tde-01112016-110215/.
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A crescente globalização financeira e integração desses mercados resultaram em relações cada vez mais próximas entre os países, sejam eles desenvolvidos ou emergentes. Esses fenômenos, somados às crises financeiras recentes, provocaram maior interesse nos eventos de transmissão de volatilidade e de fluxos de informações entre os mercados financeiros. Dentre elas, destaca-se a crise financeira internacional de 2008, conhecida como \"crise do subprime\", considerada a maior e mais importante desde a Grande Depressão de 1929. Neste contexto, o mercado de recibos americanos de depósito (ADRs) apresentou uma importância crescente nas últimas décadas, especialmente para companhias sediadas em países emergentes, como os da América Latina. Essa região, particularmente, exibiu uma grande expansão neste mercado. De maneira geral, as empresas de países emergentes emissoras de ADRs possuem características mais similares às companhias sediadas nos mercados desenvolvidos, comparadas às demais de seu país de origem. Por isso, como objetivo geral deste estudo, buscou-se detectar e mensurar o fenômeno da interdependência, englobando os transbordamentos (spillovers) de retornos e de volatilidade e suas assimetrias, entre os principais mercados de capitais da América Latina - Brasil, Argentina, Chile e México - e dos países desenvolvidos - Estados Unidos, Japão, Reino Unido e França - no âmbito da última crise financeira internacional. Esse fenômeno foi investigado considerando tanto seus índices acionários de mercado, como os índices de ADRs criados neste estudo, um para cada mercado da América Latina. Estes foram compostos pelas cotações de seus respectivos ADRs níveis 2 ou 3, sendo que a metodologia desenvolvida para sua criação foi uma das contribuições deste trabalho. A partir das séries temporais de retornos diários logarítmicos dos índices dos oito países no período de junho de 2008 a maio de 2015, foi empregada uma metodologia abrangente. Foram aplicadas três abordagens univariadas para modelagem das volatilidades dos mercados (GARCH, EGARCH e TARCH) e dois modelos multivariados assimétricos VAR-MGARCH, com representação Diagonal VECH, para identificação dos transbordamentos de retornos e volatilidade, bem como a análise de suas correlações condicionais. Além disso, foram estimados dois modelos autorregressivos multivariados (VAR) para análise das relações conjuntas dos mercados, e a análise das Funções de Resposta a Impulso (IRF) e dos efeitos sobre a variância por meio de sua decomposição. Os resultados indicaram que as séries de retornos dos mercados de ADRs de empresas latino-americanas não apresentam comportamento mais similar, no tocante à volatilidade, ao dos principais mercados de capitais desenvolvidos. No entanto, há evidências de que os índices de ADRs possuem maior interdependência com os principais mercados de capitais desenvolvidos, por apresentarem relações mais próximas com esses, comparados aos mercados acionários latino-americanos analisados. Essa conclusão corrobora as hipóteses elaboradas sobre esse tema a partir da teoria de segmentação de mercado e das próprias características dessas companhias. Outro resultado relevante foi que os mercados emergentes da América Latina são mais suscetíveis a efeitos locais e regionais que globais, confirmando o benefício do uso dos ativos financeiros desses países para diversificação de carteiras internacionais, mesmo durante uma crise financeira internacional, como a do subprime.The growing financial globalization and integration of this markets resulted in increasingly close links between the countries, both developed and emerging ones. These phenomena, added to the recent financial crises, provoked greater interest in the events of volatility and information flows transmission between the financial markets. Among them, stands out the international financial crisis of 2008, known as the \"subprime crisis\", considered the largest and most important since the Great Depression of 1929. In this context, the American Depositary Receipts (ADRs) market showed an increasing importance in recent decades, especially for companies based in emerging markets, such as the Latin America. This region, particularly, exhibited a large expansion in this market. In general, companies in emerging countries issuers of ADRs have more similar characteristics to companies based in developed markets, compared to the rest of their country of origin. Therefore, the general objective of this study was to detect and measure the interdependence phenomenon, encompassing returns and volatility spillovers and their asymmetries, among the major capital markets in Latin America - Brazil, Argentina, Chile and Mexico - and developed countries - United States, Japan, UK and France - within the last international financial crisis. This phenomenon was investigated considering both their stock market indices and the ADRs indices created in this study, one for each Latin America country. They were compound of the quotes from their respective ADRs levels 2 or 3, and the methodology developed for their creation was one of the contributions of this assignment. Using the time series of daily logarithmic returns of the eight countries indices in the period from June 2008 to May 2015, it was applied an embracing methodology. It was estimated three univariate approaches to modeling the markets volatility (GARCH, EGARCH and TARCH) and two asymmetric multivariate models VAR-MGARCH, with Diagonal VECH representation, for identification of the returns and volatility spillovers, as well as analysis of their conditional correlations. In addition, two multivariate autoregressive models (VAR) were estimated for analysis of joint relations of markets, and analysis of Impulse Response Functions (IRF) and the effects on the variance through its decomposition. The results indicated that the returns series from Latin American ADR markets doesn\"t have behavior more similar, with regard to volatility, to the major developed capital markets. However, there is evidence that the ADR indices present greater interdependence with the major developed capital markets, because they have closer relationships with these, compared to the Latin American equity markets analyzed. This finding supports the hypothesis elaborated on this subject from the market segmentation theory and the characteristics of these companies. Another important result was that the emerging markets of Latin America are more susceptible to local and regional effects than global ones, confirming the benefit of the use of the financial assets of these countries for diversification of international portfolios, even during an international financial crisis, such as the subprime.
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Silvennoinen, Annastiina. "Essays on autoregressive conditional heteroskedasticity." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (EFI), 2006. http://www2.hhs.se/EFI/summary/711.htm.
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Mazibas, Murat. "Dynamic portfolio construction and portfolio risk measurement." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3297.
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The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.
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Rotta, Pedro Nielsen. "Análise de contágio a partir do modelo de correlação condicional constante com mudança de regime Markoviana." reponame:Repositório Institucional do FGV, 2012. http://hdl.handle.net/10438/10402.
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Nas últimas décadas, a análise dos padrões de propagação internacional de eventos financeiros se tornou o tema de grande parte dos estudos acadêmicos focados em modelos de volatilidade multivariados. Diante deste contexto, objetivo central do presente estudo é avaliar o fenômeno de contágio financeiro entre retornos de índices de Bolsas de Valores de diferentes países a partir de uma abordagem econométrica, apresentada originalmente em Pelletier (2006), sobre a denominação de Regime Switching Dynamic Correlation (RSDC). Tal metodologia envolve a combinação do Modelo de Correlação Condicional Constante (CCC) proposto por Bollerslev (1990) com o Modelo de Mudança de Regime de Markov sugerido por Hamilton e Susmel (1994). Foi feita uma modificação no modelo original RSDC, a introdução do modelo GJR-GARCH formulado em Glosten, Jagannathan e Runkle (1993), na equação das variâncias condicionais individuais das séries para permitir capturar os efeitos assimétricos na volatilidade. A base de dados foi construída com as séries diárias de fechamento dos índices das Bolsas de Valores dos Estados Unidos (SP500), Reino Unido (FTSE100), Brasil (IBOVESPA) e Coréia do Sul (KOSPI) para o período de 02/01/2003 até 20/09/2012. Ao longo do trabalho a metodologia utilizada foi confrontada com outras mais difundidos na literatura, e o modelo RSDC com dois regimes foi definido como o mais apropriado para a amostra selecionada. O conjunto de resultados encontrados fornecem evidências a favor da existência de contágio financeiro entre os mercados dos quatro países considerando a definição de contágio financeiro do Banco Mundial denominada de 'muito restritiva'. Tal conclusão deve ser avaliada com cautela considerando a extensa diversidade de definições de contágio existentes na literatura.
Over the last decades, the analysis of the transmissions of international financial events has become the subject of many academic studies focused on multivariate volatility models volatility. The goal of this study is to evaluate the financial contagion between stock market returns. The econometric approach employed was originally presented by Pelletier (2006), named Regime Switching Dynamic Correlation (RSDC). This methodology involves the combination of Constant Conditional Correlation Model (CCC) proposed by Bollerslev (1990) with Markov Regime Switching Model suggested by Hamilton and Susmel (1994). A modification was made in the original model RSDC, the introduction of the GJR-GARCH Glosten model formulated in Glosten, Jagannathan e Runkle (1993), on the equation of the conditional univariate variances to allow asymmetric effects in volatility be captured. The database was built with the series of daily closing stock market indices in the United States (SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) for the period from 02/01/2003 to 20/09/2012. Throughout the work the methodology was compared with others most widespread in the literature, and the model RSDC with two regimes was defined as the most appropriate for the selected sample. The set of results provide evidence for the existence of financial contagion between markets of the four countries considering the definition of financial contagion from the World Bank called 'very restrictive'. Such a conclusion should be evaluated carefully considering the wide diversity of definitions of contagion in the literature.
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Bozovic, Milos. "Risks in Commodity and Currency Markets." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7388.
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This thesis analyzes market risk factors in commodity and currency markets. It focuses on the impact of extreme events on the prices of financial products traded in these markets, and on the overall market risk faced by the investors. The first chapter develops a simple two-factor jump-diffusion model for valuation of contingent claims on commodities in order to investigate the pricing implications of shocks that are exogenous to this market. The second chapter analyzes the nature and pricing implications of the abrupt changes in exchange rates, as well as the ability of these changes to explain the shapes of option-implied volatility "smiles". Finally, the third chapter employs the notion that key results of the univariate extreme value theory can be applied separately to the principal components of ARMA-GARCH residuals of a multivariate return series. The proposed approach yields more precise Value at Risk forecasts than conventional multivariate methods, while maintaining the same efficiency.El objetivo de esta tesis es analizar los factores del riesgo del mercado de las materias primas y las divisas. Está centrada en el impacto de los eventos extremos tanto en los precios de los productos financieros como en el riesgo total de mercado al cual se enfrentan los inversores. En el primer capítulo se introduce un modelo simple de difusión y saltos (jump-diffusion) con dos factores para la valuación de activos contingentes sobre las materias primas, con el objetivo de investigar las implicaciones de shocks en los precios que son exógenos a este mercado. En el segundo capítulo se analiza la naturaleza e implicaciones para la valuación de los saltos en los tipos de cambio, así como la capacidad de éstos para explicar las formas de sonrisa en la volatilidad implicada. Por último, en el tercer capítulo se utiliza la idea de que los resultados principales de la Teoria de Valores Extremos univariada se pueden aplicar por separado a los componentes principales de los residuos de un modelo ARMA-GARCH de series multivariadas de retorno. El enfoque propuesto produce pronósticos de Value at Risk más precisos que los convencionales métodos multivariados, manteniendo la misma eficiencia.
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Dovonon, Prosper. "Common factors in stochastic volatility of asset returns and new developments of the generalized method of moments." Thèse, 2007. http://hdl.handle.net/1866/1962.
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Marcinek, Daniel. "Kvantitativní metody řízení rizika." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-341222.
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This thesis deals with stock modelling using ARCH and GARCH time series. Important aspect of stock modelling is to capture volatility correctly. Volatility in finance is usually defined as a standard deviation of asset returns. Many different models, which are summarized in the first part of this thesis, are used to model volatility. This thesis focus on multivariate volatility models including multivariate GARCH models. An approach to constructing a conditional maximum likelihood estimate to these methods is given. Discussed theory is applied on real financial data. In numeric application there is a construction of a volatility estimates for two specific stocks using models described in the first part of this thesis. Using the same financial data various bivariate models are compared. Based on comparison using maximum likelihood a specific model for these stocks is recommended. Powered by TCPDF (www.tcpdf.org)
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Aliakseyeu, Aliaksei. "Vládní bondy a volatilita kapitálového trhu: Analýza multivariate GARCH modelem." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-350557.
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The correlation between stock market returns and changes in bond market yields are of big interest among investors because this indicator helps them allocate their assets and diversify investment risk more effectively. An in- vestor should keep track of development of the economies of individual coun- tries, understand the causes of dissimilarities in the correlations among them and take these differences into account for successful international financial investment. The current author contributes to the existing researches by the modeling of stock-bond market co-movements using the updated datasets with focus on Central European countries and differences in public debt levels. The paper contains the empirical analysis of stock and bond market returns condi- tional correlations, modeled by the use of the Asymmetric Generalized Dynamic Conditional Correlation (AG-DCC) Generalized Autoregressive Conditional Het- eroskedasticity (GARCH) specification, for nine Western and Central European countries (the United Kingdom, Germany, France, Spain, Portugal, Italy, Czech Republic, Poland and Hungary) that differ both by their geographic locations and economic development. The main distinctions in the correlations are ob- served during the European sovereign debt crisis. The three types of develop- ment are...
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Křehlík, Tomáš. "Použití moderních spektrálních metod ve finanční ekonometrii." Doctoral thesis, 2017. http://www.nusl.cz/ntk/nusl-368853.
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Spectral tools in econometrics have lately experienced a renewed surge in interest. This dissertation contributes to this literature by providing conceptually different spectral-based methods and their applications to problems of modern economics. In the first part, we take a spectral decomposition of realized volatility and construct a multivariate GARCH style model that we fit by standard quasi-maximum likelihood and generalized autoregressive score procedures. We build our model on a belief that market agents obtain information in various time horizons and therefore form their expectations in various informational horizons. This behavior creates an overall volatility process that is a mixture of spectrum specific processes. We then apply the model to the currency markets, namely GBP, CHF, and EUR. With the help of the model confidence set test we show that the multi-scale model and the generalized autoregressive score based models produce forecasts that are in most cases superior to the competing models. Moreover, we find that most of the information for future volatility comes from the high frequency part of the spectra representing the very short investment horizons. In the second part, we provide a spectral decomposition of a system multivariate connectedness measure based on Diebold and Yilmaz...
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LaBarr, Aric David. "Multivariate robust estimation of DCC-GARCH volatility model." 2010. http://www.lib.ncsu.edu/resolver/1840.16/6015.
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Čech, František. "Multivariate volatility modeling of medium and large size portfolios." Doctoral thesis, 2019. http://www.nusl.cz/ntk/nusl-396673.
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Guay, Francois. "Parameter inference for multivariate stochastic processes with jumps." Thesis, 2016. https://hdl.handle.net/2144/17713.
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This dissertation addresses various aspects of estimation and inference for multivariate stochastic processes with jumps.
The first chapter develops an unbiased Monte Carlo estimator of the transition density of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. The density estimator proposed enables efficient parametric estimation of multivariate jump-diffusion models based on discretely observed data. Under mild conditions, the resulting parameter estimates have the same asymptotic behavior as maximum likelihood estimators as the number of data points grows, even when the sampling frequency of the data is fixed. In a numerical case study of practical relevance, the density and parameter estimators are shown to be highly accurate and computationally efficient.
In the second chapter, I examine continuous-time stochastic volatility models with jumps in returns and volatility in which the parameters governing the jumps are allowed to switch according to a Markov chain. I estimate the parameters and the latent processes using the S&P 500 and Nasdaq indices from 1990 to 2014. The Markov-switching parameters characterize well the periods of market stress, such as those in 1997-1998, 2001 and 2007-2010. Several statistical tests favor the model with Markov-switching jump parameters. These results provide empirical evidence about the state-dependent and time-varying nature of asset price jumps, a feature of asset prices that has recently been documented using high-frequency data.
The third chapter considers applying Markov-switching affine stochastic volatility models with jumps in returns and volatility, where the jump parameters are not regime-switching. The estimation is performed via Markov Chain Monte Carlo methods, allowing to obtain the latent processes induced by the structure of the models. Furthermore, I propose some misspecification tests and develop a Markov-switching test based on the odds ratios. The parameters and the latent processes are estimated using the S&P 500 index from 1970 to 2014. I show that the S&P 500 stochastic volatility exhibits a Markov-switching behavior, and that most of the high volatility regimes coincide with the recessions identified ex-post by the National Bureau of Economic Research.
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HUANG, TA-WEI, and 黃大維. "The Study of Optimal Portfolio Selection with Factor Multivariate Volatility Models." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p7z239.
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碩士國立臺灣大學
統計碩士學位學程
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In this thesis, we extend the traditional Markowitz''s procedure to an easy-to-implement three-stage portfolio selection framework. By introducing the portfolio derivation strategy, we smartly avoid the problem of high-dimensional covariance matrix forecasting and leverage the maturity of univariate volatility models. Specifically, we apply 3 portfolio derivation strategies by factor volatility models, 4 portfolio selection strategies, and 2 risk-adjusted return portfolio selection measures. We implement these algorithms on foreign exchange rate dataset and the semiconductor stock dataset, leading to outstanding performances. We also conduct detailed analyses about our proposed trading strategies. The result suggests that (1) the forecast accuracy of portfolio returns is not the most important thing and (2) our proposed strategies outperforms traditional minimum-variance and equally-weighted portfolios.
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Wei-Ting, Hsu, and 許瑋庭. "Volatility spillovers in precious metals, exchange rate and interest rate: Multivariate GARHC models." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/9zwcyg.
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碩士國立臺北商業大學
財務金融研究所
104
This study use two multivariate GARCH models to examine the volatility transmissions and volatility spillovers for four precious metals (gold, silver, platinum and palladium), while accounting for 2008 financial crisis within a multivariate system. Furthermore, these results become more pervasive when the U.S. dollar/Euro exchange rate and U.S. T-bond interest rate are included. The AR-GARCH model result shows that GARCH effect dominating the ARCH effect, implying that conditional volatility is predictable from past data, and all precious metals are sensitive to their own past shock and volatility dependency. But the cross-shock effects among all the four metals are limited and the cross volatility impacts are, however, small relative to its own impact. Additionally, since we include the interest rate as exogenous variables and 2008 financial crisis dummy, the results show that interest rate affects negatively only gold returns. When accounting for the exchange rate as an endogenous variable in the system, the strong volatility spillovers from the exchange rate to the precious metals are higher than the spillover effects among all the four metals. Finally, the impact of the 2008 financial crisis on metal returns is significant except platinum, but the impact on metal volatility spillovers is not significant.