Generalized autoregressive conditional heteroskedasticity

13.2kCitations
Citations of this article
2.9kReaders
Mendeley users who have this article in their library.
Get full text

Abstract

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. © 1986.

References Powered by Scopus

DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS

711Citations
N/AReaders
Get full text

Conditional variance and the risk premium in the foreign exchange market

285Citations
N/AReaders
Get full text

ARMA MODELS WITH ARCH ERRORS

193Citations
N/AReaders
Get full text

Cited by Powered by Scopus

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

5368Citations
N/AReaders
Get full text

New introduction to multiple time series analysis

3359Citations
N/AReaders
Get full text

Multivariate simultaneous generalized arch

2607Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 1064

72%

Professor / Associate Prof. 156

11%

Researcher 149

10%

Lecturer / Post doc 108

7%

Readers' Discipline

Tooltip

Economics, Econometrics and Finance 839

58%

Business, Management and Accounting 329

23%

Mathematics 182

12%

Engineering 109

7%

Article Metrics

Tooltip
Mentions
Blog Mentions: 1
News Mentions: 5
References: 10
Social Media
Shares, Likes & Comments: 29

Save time finding and organizing research with Mendeley

Sign up for free