Dissertations / Theses on the topic 'Time-Varying Multivariate GARCH Models'
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Grziska, Martin. "Multivariate GARCH and dynamic copula models for financial time series." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-179219.
Full textNoureldin, 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.
Full textAndersson-Säll, Tim, and Johan Lindskog. "A STUDY ON THE DCC-GARCH MODEL’S FORECASTING ABILITY WITH VALUE-AT-RISK APPLICATIONS ON THE SCANDINAVIAN FOREIGN EXCHANGE MARKET." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-375201.
Full textGrziska, Martin [Verfasser], and Stefan [Akademischer Betreuer] Mittnik. "Multivariate GARCH and dynamic copula models for financial time series : with an application to emerging markets / Martin Grziska. Betreuer: Stefan Mittnik." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1068460628/34.
Full textGrziska, Martin Verfasser], and Stefan [Akademischer Betreuer] [Mittnik. "Multivariate GARCH and dynamic copula models for financial time series : with an application to emerging markets / Martin Grziska. Betreuer: Stefan Mittnik." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-179219.
Full textSeerattan, Dave Arnold. "The effectiveness of central bank interventions in the foreign exchange market." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7361.
Full textGuesmi, Khaled. "Dynamique d'intégration des marchés boursiers émergents." Thesis, Paris 10, 2011. http://www.theses.fr/2011PA100169.
Full textThe purpose of this thesis is to study the dynamics of the global integration process of four emerging market regions into the world and the regional market, while taking into account the importance of exchange rate and local market risk. An international capital asset pricing model suitable for partially integrated markets and departure from purchasing power parity was developed in the spirit of Bekaert and Harvey (1995)’s regime-switching model in order to explain the time-variations in expected returns on regional emerging market indices. In its fully functional form, the model allows the market integration measure as well as the global and local risk premiums to vary through time. We mainly find that the integration degree in emerging market regions (Latin America, Asia, Southeastern Europe, and the Middle East) varied widely through time over the period 1996-2008 and is satisfactorily explained by global, regional and national factors. Even though it reaches fairly high values during several periods, and exhibit an upward trend towards the end of the estimation period, the emerging market regions under consideration still remain segmented from the world and regional market. These results thus suggest that diversification into emerging market assets continue to produce substantial profits and that the asset pricing rules should reflect a state of partial integration. Our investigation, which addresses the evolution and formation of total risk premiums, confirm this empirically
Wu, Hao. "Forecasting the time-varying beta of UK and US firms: evidence from GARCH and non-GARCH models." Thesis, University of Southampton, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494769.
Full textLiu, Yi. "Time-Varying Coefficient Models for Recurrent Events." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/97999.
Full textPHD
Sørensen, Steffen. "Estimation of time-varying risk premia on stock market indices and exchange rates pricing macroeconomic variables : a multivariate GARCH-in-mean approach." Thesis, University of York, 2004. http://etheses.whiterose.ac.uk/10957/.
Full textSilvennoinen, 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.
Full textAhmad, Ali. "Contribution à l'économétrie des séries temporelles à valeurs entières." Thesis, Lille 3, 2016. http://www.theses.fr/2016LIL30059/document.
Full textThe framework of this PhD dissertation is the conditional mean count time seriesmodels. We propose the Poisson quasi-maximum likelihood estimator (PQMLE) for the conditional mean parameters. We show that, under quite general regularityconditions, this estimator is consistent and asymptotically normal for a wide classeof count time series models. Since the conditional mean parameters of some modelsare positively constrained, as, for example, in the integer-valued autoregressive (INAR) and in the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH), we study the asymptotic distribution of this estimator when the parameter lies at the boundary of the parameter space. We deduce a Waldtype test for the significance of the parameters and another Wald-type test for the constance of the conditional mean. Subsequently, we propose a robust and general goodness-of-fit test for the count time series models. We derive the joint distribution of the PQMLE and of the empirical residual autocovariances. Then, we deduce the asymptotic distribution of the estimated residual autocovariances and also of a portmanteau test. Finally, we propose the PQMLE for estimating, equation-by-equation (EbE), the conditional mean parameters of a multivariate time series of counts. By using slightly different assumptions from those given for PQMLE, we show the consistency and the asymptotic normality of this estimator for a considerable variety of multivariate count time series models
Nováková, Martina. "Mnohorozměrné modely zobecněné autoregresní podmíněné heteroskedasticity." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-437910.
Full textVeselý, Daniel. "Vícerozměrné finanční časové řady." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-313775.
Full textLivingston, Jr Glen. "A Bayesian analysis of a regime switching volatility model." Thesis, 2017. http://hdl.handle.net/1959.13/1342483.
Full textNon-linear time series data is often generated by complex systems. While linear models provide a good first approximation of a system, often a more sophisticated non-linear model is required to properly account for the features of such data. Correctly accounting for these features should lead to the fitting of a more appropriate model. Determining the features exhibited by a particular data set is a difficult task, particularly for inexperienced modellers. Therefore, it is important to move towards a modelling paradigm where little to no user input is required, in order to open statistical modelling to users less experienced in MCMC. This sort of modelling process requires a general class of models that is able to account for the features found in most linear and non-linear data sets. One such class is the STAR-GARCH class of models. These are reasonably general models that permit regime changes in the conditional mean and allow for changes in the conditional covariance. In this thesis, we develop original algorithms that combine the tasks of parameter estimation and model selection for univariate and multivariate STAR-GARCH models. The model order of the conditional mean and the model index of the conditional covariance equation are included as parameters for the model requiring estimation. Combining the tasks of parameter estimation and model selection is facilitated through the Reversible Jump MCMC methodology. Other MCMC algorithms employed for the posterior distribution simulators are the Gibbs sampler, Metropolis-Hastings, Multiple-Try Metropolis and Delayed Rejection Metropolis-Hastings algorithms. The posterior simulation algorithms are successfully implemented in the statistical software program R, and their performance is tested in both extensive simulation studies and practical applications to real world data. The current literature on multivariate extensions of STAR, GARCH, and STAR-GARCH models is quite limited from a Bayesian perspective. The implementation of a set of estimation algorithms that not only provide parameter estimates but is also able to automatically fit the model with highest posterior probability is a significant and original contribution. The impact of such a contribution will hopefully be a step forward on the path towards the automation of time series modelling.
Kudela, Maria Aleksandra. "Statistical methods for high-dimensional data with complex correlation structure applied to the brain dynamic functional connectivity studyDY." Diss., 2017. http://hdl.handle.net/1805/12835.
Full textA popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.