GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY
Tim BOLLERSLEV* 1986文献,21页
也算是~~~~~~~值了~~~~~~~~
GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY
BY Tim BOLLERSLEV*
University of California at San Diego, La Jolla, CA 92093, USA
Institute of Economics, University of Aarhus, Denmark
Received May 1985, final version received February 1986
A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process
introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.
----------------------------------------------------------------------------------
1. Introduction
While conventional time series and econometric models operate under an assumption of constant variance, the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) allows the conditional variance to change over time as a function bf past errors leaving the unconditional variance constant.
This type of model behavior has already proven useful in modelling several different economic phenomena. In Engle (1982), Engle (1983) and Engle and Kraft (1983), models for the inflation rate are constructed recognizing that the uncertainty of inflation tends to change over time.
In Coulson and Robins (1985) the estimated inflation volatility is related to some key macroeconomic variables. Models for the term structure using an estimate of the conditional variance as a proxy for the risk premium are given in Engle, Lilien and Robins (1985).
The same idea is applied to the foreign exchange market in Domowitz and Hakkio (1985). In Weiss (1984) ARMA models with ARCH errors are found to be successful in modelling thirteen different U.S. macroeconomic time series. Common to most of the above applications however, is the introduction of a rather arbitrary linear declining lag structure……………………………………………………