Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-10T16:15:35Z No. of bitstreams: 1 LYA RAQUEL OLIVEIRA DE SOUSA - DISSERTAÇÃO PPGMAT 2006..pdf: 539595 bytes, checksum: 9c8bde69fa077d93bd6a4ec01ebd5171 (MD5) / Made available in DSpace on 2018-07-10T16:15:35Z (GMT). No. of bitstreams: 1 LYA RAQUEL OLIVEIRA DE SOUSA - DISSERTAÇÃO PPGMAT 2006..pdf: 539595 bytes, checksum: 9c8bde69fa077d93bd6a4ec01ebd5171 (MD5) Previous issue date: 2006-03 / Capes / Neste trabalho apresentamos como a inferência Bayesiana e a relação entre região H.P.D. e os testes de hipóteses, obtida através de algumas propriedades da distribuição t-Studente multivariada, podem ser aplicados no estudo de dados de um Modelo de Covariância Linear com e sem erros nas variáveis. Para o nosso trabalho, consideramos um experimento planejado em k tratamentos, sendo cada um deles repetido em ni unidades experimentais, i=1,2,...,k. / In this work we present as the Bayesian inference and the relation between region H.P.D. and the tests of hypothesis, gotten through some properties of the distribution t-Student multivaried, they can to be applied in the study of data of a Model of Linear Covariance with and without errors in the variables. For our work, we consider an experiment planned in k treatments, being each one of them repeated in ni experimental units, i=1,2,...,k.

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.