Confidence Intervals for the Autocorrelations of the Squares of GARCH Sequences
نویسندگان
چکیده
We compare three methods of constructing confidence intervals for sample autocorrelations of squared returns modeled by models from the GARCH family. We compare the residual bootstrap, block bootstrap and subsampling methods. The residual bootstrap based on the standard GARCH(1,1) model is seen to perform best.
منابع مشابه
Comparison of Maximum Likelihood Estimation and Bayesian with Generalized Gibbs Sampling for Ordinal Regression Analysis of Ovarian Hyperstimulation Syndrome
Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data. Methods: This study use...
متن کاملGARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics
he great workhorse of applied econometrics is the least squares model. This is a natural choice, because applied econometricians are typically called upon to determine how much one variable will change in response to a change in some other variable. Increasingly however, econometricians are being asked to forecast and analyze the size of the errors of the model. In this case, the questions are ...
متن کاملEffects of Outliers on the Identification and Estimation of Garch Models
This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be di...
متن کاملEstimation of Garch Models from the Autocorrelations of the Squares of a Process by Richard T. Baillie
This paper shows how the parameters of a stable GARCH(1, 1) model can be estimated from the autocorrelations of the squared process. Speci®cally, the method applies a minimum distance estimator (MDE) to the sample autocorrelations of the squared realization. The asymptotic ef®ciency of the estimator is calculated from using the ®rst g autocorrelations. The estimator can be surprisingly ef®cient...
متن کاملEvaluation of hybrid fuzzy regression capability based on comparison with other regression methods
In this paper, the difference between classical regression and fuzzy regression is discussed. In fuzzy regression, nonphase and fuzzy data can be used for modeling. While in classical regression only non-fuzzy data is used. The purpose of the study is to investigate the possibility of regression method, least squares regression based on regression and linear least squares linear regression met...
متن کامل