Neste trabalho, tínhamos por objetivo propor um modelo dinâmico de estrutura a termo de taxas de juros com variáveis macroeconômicas baseado na formulações de Diebold e Li (2006) e Nelson e Siegel (1987) (DNS). A estrutura de estimação proposta permite utilizar dados de frequências distintas, combinando observações diárias de curvas de juros e mensais de variáveis macroeconômicas de interesse através de uma estrutura MIDAS - Mixed Data Sampling. Também utilizamos uma estrutura de volatilidade estocástica multivariada para os fatores latentes e variáveis macroeconômicas e também permitimos que o parâmetro de decaimento do modelo DNS varie no tempo, permitindo capturar mudanças na estrutura de volatilidade condicional e no formato das curvas em períodos longos. O procedimento de estimação é baseado em métodos Bayesianos usando Markov Chain Monte Carlo. Aplicamos este modelos para a curva de juros de títulos do Tesouro Americano entre 1997 e 2011. Os resultados indicam que incorporação de informações diárias e mensais em um mesmo modelo permite ganhos significantes de ajuste, superando as estimativas usuais baseadas em modelos sem informações macroeconômicas e nos métodos usuais de estimação do modelo de Diebold e Li (2006) / In this present work, we propose a dynamic model for the term structure of interest rates with macroeconomic variables based on Diebold e Li (2006)\'s and Nelson e Siegel (1987)\'s researches. The estimation procedure we intend to build allows time series data sampled at different frequencies, mixing daily observations of yield curves and monthly observations of macroeconomic variable through a Mixed Data Sampling (MIDAS) regression. We also make use of a multivariate stochastic volatility structure for the latent factors and allow the parameter that governs the exponential decay rate to vary trough time, which enables us to capture changes both in the conditional volatility structure and in the curve\'s shapes during long periods. The estimation procedure is based on Baeysian inference trough the usage of of Markov Chain Monte Carlo (MCMC) method. We applied these models to the U.S. Treasure bonds\' yield curve from 1997 to 2011. The results denote that joining daily and monthly information into the same model allows significant gains on fitting these models to the term structure, overcoming the usual estimates based on models without macroeconomics information and on regular estimation methods of Diebold e Li (2006)\'s model.

Neste trabalho, tínhamos por objetivo propor um modelo dinâmico de estrutura a termo de taxas de juros com variáveis macroeconômicas baseado na formulações de Diebold e Li (2006) e Nelson e Siegel (1987) (DNS). A estrutura de estimação proposta permite utilizar dados de frequências distintas, combinando observações diárias de curvas de juros e mensais de variáveis macroeconômicas de interesse através de uma estrutura MIDAS - Mixed Data Sampling. Também utilizamos uma estrutura de volatilidade estocástica multivariada para os fatores latentes e variáveis macroeconômicas e também permitimos que o parâmetro de decaimento do modelo DNS varie no tempo, permitindo capturar mudanças na estrutura de volatilidade condicional e no formato das curvas em períodos longos. O procedimento de estimação é baseado em métodos Bayesianos usando Markov Chain Monte Carlo. Aplicamos este modelos para a curva de juros de títulos do Tesouro Americano entre 1997 e 2011. Os resultados indicam que incorporação de informações diárias e mensais em um mesmo modelo permite ganhos significantes de ajuste, superando as estimativas usuais baseadas em modelos sem informações macroeconômicas e nos métodos usuais de estimação do modelo de Diebold e Li (2006) / In this present work, we propose a dynamic model for the term structure of interest rates with macroeconomic variables based on Diebold e Li (2006)\'s and Nelson e Siegel (1987)\'s researches. The estimation procedure we intend to build allows time series data sampled at different frequencies, mixing daily observations of yield curves and monthly observations of macroeconomic variable through a Mixed Data Sampling (MIDAS) regression. We also make use of a multivariate stochastic volatility structure for the latent factors and allow the parameter that governs the exponential decay rate to vary trough time, which enables us to capture changes both in the conditional volatility structure and in the curve\'s shapes during long periods. The estimation procedure is based on Baeysian inference trough the usage of of Markov Chain Monte Carlo (MCMC) method. We applied these models to the U.S. Treasure bonds\' yield curve from 1997 to 2011. The results denote that joining daily and monthly information into the same model allows significant gains on fitting these models to the term structure, overcoming the usual estimates based on models without macroeconomics information and on regular estimation methods of Diebold e Li (2006)\'s model.

This thesis investigates the use of realized volatility features from high frequency data in com- bination with neural networks to improve forecasts of the yield curve of government bonds. I use high frequency data on futures of four U.S. Treasury securities to estimate the Nelson-Siegel yield curve and realized variance of its parameters over the period of 25 years. The estimated parameters are used in prediction of the level, slope and curvature of the yield curve using an LSTM neural network and compared to the Dynamic Nelson-Siegel model. Results show that the use of realized variance and neural network outperforms autoregressive methods in prediction of the level and curvature in daily and monthly forecasts. The yield curve of government bonds itself has a predictive power on multiple macroeconomic variables, therefore improvements in its forecastability may have broader implications on forecasting the overall state of the economy.

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.

Submitted by Camilo de Léllis Cavalcanti Júnior (camilojr@gmail.com) on 2013-09-20T14:38:32Z No. of bitstreams: 1 Dissertação - Estratégia de Trading Utilizando o Modelo Dinâmico de Nelson-Siegel Final.pdf: 1310470 bytes, checksum: f90849f3305d9519f30ddd197d650214 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-09-20T14:43:47Z (GMT) No. of bitstreams: 1 Dissertação - Estratégia de Trading Utilizando o Modelo Dinâmico de Nelson-Siegel Final.pdf: 1310470 bytes, checksum: f90849f3305d9519f30ddd197d650214 (MD5) / Made available in DSpace on 2013-09-20T14:49:57Z (GMT). No. of bitstreams: 1 Dissertação - Estratégia de Trading Utilizando o Modelo Dinâmico de Nelson-Siegel Final.pdf: 1310470 bytes, checksum: f90849f3305d9519f30ddd197d650214 (MD5) Previous issue date: 2013-08-21 / Esta pesquisa busca testar a eficácia de uma estratégia de arbitragem de taxas de juros no Brasil baseada na utilização do modelo de Nelson-Siegel dinâmico aplicada à curva de contratos futuros de taxa de juros de 1 dia da BM&FBovespa para o período compreendido entre 02 de janeiro de 2008 e 03 de dezembro de 2012. O trabalho adapta para o mercado brasileiro o modelo original proposto por Nelson e Siegel (1987), e algumas de suas extensões e interpretações, chegando a um dos modelos propostos por Diebold, Rudebusch e Aruoba (2006), no qual estimam os parâmetros do modelo de Nelson-Siegel em uma única etapa, colocando-o em formato de espaço de estados e utilizando o Filtro de Kalman para realizar a previsão dos fatores, assumindo que o comportamento dos mesmos é um VAR de ordem 1. Desta maneira, o modelo possui a vantagem de que todos os parâmetros são estimados simultaneamente, e os autores mostraram que este modelo possui bom poder preditivo. Os resultados da estratégia adotada foram animadores quando considerados para negociação apenas os 7 primeiros vencimentos abertos para negociação na BM&FBovespa, que possuem maturidade máxima próxima a 1 ano. / This research tries to test the effectiveness of an interest rate arbitrage strategy in Brazil based on a Dynamic Nelson-Siegel model applied to the term structure of future contracts of 1 day of interest rates traded at BM&FBovespa for the time between January, 2nd of 2008, and December, 3rd, 2012. The work adapts to the Brazilian market the mode originally proposed by Nelson and Siegel (1987), and some of its extensions and interpretations, reaching one of the models proposed by Diebold, Rudebusch and Aruoba (2006), in which they estimate the parameters of Nelson-Siegel Model in one only step, putting it in a state-space form and using the Kalman Filter to make the factors’ forecast, assuming that their behavior is an order 1 VAR. The model has the advantage that all the parameters are estimated simultaneously, and the authors showed that it has a good forecast power. The results of the adopted strategy were encouraging when considered for negotiation only the 7 first available maturities at BM&FBovespa, which have maturity of around 1 year.