M. Medeiros (Marcelo)
http://repub.eur.nl/ppl/14174/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryAsymmetry and long memory in volatility modeling
http://repub.eur.nl/pub/76741/
Fri, 01 Jun 2012 00:00:01 GMT<div>M. Asai</div><div>M.J. McAleer</div><div>M. Medeiros</div>
In this paper, we propose a long memory asymmetric volatility model, which captures more flexible asymmetric patterns as compared with several existing models. We extend the new specification to realized volatility (RV) by taking account of measurement errors and use the Efficient Importance Sampling technique to estimate the model. We apply the model to the RV of S&P500. Overall, the results of the out-of-sample forecasts show the adequacy of the new asymmetric and long memory volatility model for the period including the global financial crisis.Modelling and forecasting noisy realized volatility
http://repub.eur.nl/pub/31092/
Sun, 01 Jan 2012 00:00:01 GMT<div>M. Asai</div><div>M.J. McAleer</div><div>M. Medeiros</div>
Several methods have recently been proposed in the ultra-high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex post daily volatility. Even bias-corrected and consistent realized volatility (RV) estimates of IV can contain residual microstructure noise and other measurement errors. Such noise is called "realized volatility error". As such errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators; (ii) the effects of RV errors on one-step-ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; (iii) even the partially correctedR2recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction ofR2. An empirical example for S&P 500 data is used to demonstrate the techniques developed in this paper. Moment-based estimation of smooth transition regression models with endogenous variables
http://repub.eur.nl/pub/30761/
Thu, 03 Nov 2011 00:00:01 GMT<div>W.D. Areosa</div><div>M.J. McAleer</div><div>M. Medeiros</div>
Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature. Forecasting realized volatility with linear and nonlinear univariate models
http://repub.eur.nl/pub/31749/
Tue, 01 Feb 2011 00:00:01 GMT<div>M.J. McAleer</div><div>M. Medeiros</div>
In this paper, we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high-frequency intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper. Modelling and Forecasting Noisy Realized Volatility
http://repub.eur.nl/pub/22284/
Mon, 31 Jan 2011 00:00:01 GMT<div>M. Asai</div><div>M.J. McAleer</div><div>M. Medeiros</div>
Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent realized volatility (RV) estimates of IV can contain residual microstructure noise and other measurement errors. Such noise is called “realized volatility error”. Since such errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of . An empirical example for S&P 500 data is used to demonstrate the techniques developed in the paper.Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination
http://repub.eur.nl/pub/11137/
Sat, 01 Oct 2005 00:00:01 GMT<div>T. Terasvirta</div><div>D.J.C. van Dijk</div><div>M. Medeiros</div>
In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.Reply
http://repub.eur.nl/pub/54058/
Sat, 01 Oct 2005 00:00:01 GMT<div>T. Teräsvirta</div><div>D.J.C. van Dijk</div><div>M. Medeiros</div>