Download or read book Robust Estimation for the Orthogonal GARCH Model written by Farhat Iqbal and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we propose a class of robust M-estimators for the orthogonal generalized autoregressive conditional heteroscedastic (GARCH) model. The method involves the estimation of only univariate GARCH models and hence easy to estimate and does not put additional constraints on the model. The forecasting performance of the class of robust estimators in predicting correlation and value-at-risk using various evaluation measures are investigated. We found empirical evidences of the better predictive potential of estimators such as least absolute deviation and B-estimator over the widely used quasi-maximum likelihood estimator when the error distribution is heavy-tailed and asymmetric. Applications to real data sets are also presented.

Download or read book Robust M Estimation of Multivariate GARCH Models written by Kris Boudt and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application document the good robustness properties of the M-estimator with a fat-tailed Student t loss function and volatility models with the property of bounded innovation propagation.

Download or read book Robust Estimation and Inference for Heavy Tailed GARCH written by Jonathan B. Hill and published by . This book was released on 2014 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop two new estimators for a general class of stationary GARCH models with possibly heavy tailed asymmetrically distributed errors, covering processes with symmetric and asymmetric feedback like GARCH, Asymmetric GARCH, VGARCH and Quadratic GARCH. The first estimator arises from negligibly trimming QML criterion equations according to error extremes. The second imbeds negligibly transformed errors into QML score equations for a Method of Moments estimator. In this case we exploit a sub-class of redescending transforms that includes tail-trimming and functions popular in the robust estimation literature, and we re-center the transformed errors to minimize small sample bias. The negligible transforms allow both identification of the true parameter and asymptotic normality. We present a consistent estimator of the covariance matrix that permits classic inference without knowledge of the rate of convergence. A simulation study shows both of our estimators trump existing ones for sharpness and approximate normality including QML, Log-LAD, and two types of non-Gaussian QML (Laplace and Power-Law). Finally, we apply the tail-trimmed QML estimator to financial data.

Download or read book Robust Estimation for GARCH Models and VARMA Models written by Hang Liu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation written by Jeroen Hinloopen and published by . This book was released on 1995 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient and Robust Estimation of GARCH Models written by X. Jiang and published by . This book was released on 2015 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized autoregressive conditional heteroscedastic (GARCH) models have been a powerful tool for modeling volatility. In this paper, we propose an efficient and robust method for estimating the parameters of GARCH models. This method involves a sequence of weights and takes a data-driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy- or light-tailed error distributions. Simulations endorse our theoretical results. Our approach is applied to analyze the S&P 500 Composite index in the U.S. financial market and run some regression diagnostics to validate the fitted model.

Download or read book Robust Estimation with Non normal Data and Finite Samples in the Analysis of Covariance Structures written by Susan Joan Henly and published by . This book was released on 1991 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation in Semiparametric Models written by Zaiqian Shen and published by . This book was released on 1992 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation for the Errors in variables Model written by Ruben Horacio Zamar and published by . This book was released on 1985 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation of the Linkage Matrix in O GARCH Model and GO GARCH Model written by Lingyu Zheng and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics

Download or read book GEL Estimation for Heavy Tailed GARCH Models with Robust Empirical Likelihood Inference written by Jonathan B. Hill and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We construct a Generalized Empirical Likelihood estimator for a GARCH(1,1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions for very heavy-tailed random volatility data. We show the implied probabilities from the tail-trimmed Continuously Updated Estimator elevate weight for usable large values, assign large but not maximum weight to extreme observations, and give the lowest weight to non-leverage points. Finally, we present robust versions of Generalized Empirical Likelihood Ratio, Wald, and Lagrange Multiplier tests, and an efficient and heavy tail robust moment estimator with an application to the estimation of a conditionally heteroscedastic asset's expected shortfall.

Download or read book Robust Estimation of Linear Mixed Models written by Manuel Koller and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Method of Moments Estimation of GO GARCH Models written by Peter H. Boswijk and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation Based on Grouped adjusted Data in Linear Regression Models written by Stanford University. Econometric Workshop and published by . This book was released on 1985 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation in Cox s Regression Model written by Tadeusz Bednarski and published by . This book was released on 1991 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation of the Regression Vector in the Linear Model in the Presence of Asymmetry written by Jerome N. Sheahan and published by . This book was released on 1979 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Orthogonal GARCH to Forecast Covariance Matrix of Stock Returns written by Jingjing Bai and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The motivation of this paper is to study the estimation problems in large dimension systems in quantitative finance. The paper firstly presents principal component analysis to obtain the most important information in the data. Then, the orthogonal GARCH model introduced by Alexander and Chibumba (1997) and Alexander (2000) is provided to forecast five energy stocks0́9 monthly volatilities and correlations. I show that as long as the stocks are already highly correlated with one another, the orthogonal GARCH approach will reduce computational complexity, control the amount of 0́8noise0́9, and produce volatility and correlations for all the assets. All the computation procedures were accomplished in Microsoft Excel. Forecasting of volatility and correlation of stock returns is significant in the analysis of option pricing, portfolio optimization and value-at-risk models.