Econometric Analysis of Financial and Economic Time Series Part A, Volume 20 (Advances in Econometrics)

Covers time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, and the application of the technique of boosting in volatility forecasting. The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. The basic themes of this part of Volume 20 of "Advances in Econometrics" are time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in GARCH modelling, out-of-sample evaluation of the Fed Model in stock price valuation, structural change as an alternative to long memory, the use of smooth transition auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness of regressions analyzing Taylor-Type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. This Series aids in the diffusion of new econometric techniques. Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume. It illustrates new concepts Part A. Pt. 1. Multivariate Volatility Models -- Pt. 2. High Frequency Volatility Mnodels -- Pt. 3. Univariate Volatility Models -- Part B. Realized Beta: Persistence And Predictability -- Asymmetric Predictive Abilities Of Nonlinear Models For Stock Returns: Evidence From Density Forecast Comparison -- Flexible Seasonal Time Series Models -- Estimation Of Long-memory Time Series Models: A Survey Of Different Likelihood-based Methods -- Boosting-based Frameworks In Financial Modeling: Application To Symbolic Volatility Forecasting -- Overlaying Time Scales In Financial Volatility Data -- Evaluating The 'fed Model' Of Stock Price Valuation: An Out-of-sample Forecasting Perspective -- Structural Change As An Alternative To Long Memory In Financial Time Series -- Time Series Mean Level And Stochastic Volatility Modeling By Smooth Transition Autogressions: A Bayesian Approach -- Estimating Taylor-type Rules: An Unbalanced Regression? -- Bayesian Inference On Mixture-of-experts For Estimation Of Stochastic Volatility -- A Modern Time Series Assessment Of A Statistical Model For Sunspot Activity By C.w. Granger (1957) -- Personal Comments On Yoon's Discussion Of My 1957 Paper -- A New Class Of Tail-dependent Time-series Models And Its Applications In Financial Time Series. Edited By Dek Terrell, Thomas B. Fomby. Includes Bibliographical References. This is a two-part volume honouring the Nobel prizes in Economics received by Robert Engle and Clive Granger in 2003. Part A of the volume contains remarks by Engle and Granger on the field of time series econometrics given at the Third Annual Advances in Econometrics Conference held at Louisiana State University in November of 2004 as well as 13 papers on the subject of the measurement and forecasting of volatility in financial and economic time series. These 13 volatility papers are broken into three groups: Multivariate Volatility Models, High Frequency Models, and Univariate Volatility Models. Part B of the volume likewise contains the conference remarks of Engle and Granger and, in addition, contains 13 papers on a broader range of subjects including cointegration, modelling long-memory, nonlinear models of stock market behaviour, unbalanced regression models, seasonal time series models, mixture-of-expert models, and tail-dependent time series models, among other topics. Also Granger provides comments on a paper that reviews his first professional journal publication. The knowledge of the time-varying behavior of correlations and covariances between asset returns is an essential part in asset pricing, portfolio selection and risk management. The papers in this volume focus on volatility models and are organized by multivariate, high frequency and univariate types A valuable research tool for those with an interest in the latest developments in econometric methodology