# Generalized autoregressive conditional heteroskedasticity

**Tim Bollerslev**

CiteWeb id: 19860000004

CiteWeb score: 20017

DOI: 10.1016/0304-4076(86)90063-1

The present paper proposes a generalization of the canonical AutoRegressive Conditional Heteroskedasticity (ARCH) model by extending the conditional variance equation toward past conditional variances. The stationarity conditions and autocorrelation structure of the Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model are derived. Using an empirical example of uncertainty of the inflation rate the paper demonstrates that the GARCH model provides a better fit and a more plausible learning mechanism than the ARCH model. (This abstract was borrowed from another version of this item.)

Links:- public.econ.duke.edu/~boller/Published_Papers/joe_86.pdf
- www-stat.wharton.upenn.edu/~steele/Courses/434/434Context/GARCH/Bollerslev86.pdf
- www.stat.purdue.edu/~mlevins/STAT598K_2012/bollerslev1986.pdf
- www.fhi.sk/files/katedry/kove/predmety/Prognosticke_modely/Bollerslev_GARCH.pdf
- www.sciencedirect.com/science/article/pii/0304407686900631
- https://ideas.repec.org/a/eee/econom/v31y1986i3p307-327.html
- econpapers.repec.org/RePEc:eee:econom:v:31:y:1986:i:3:p:307-327
- citec.repec.org/d/eee/econom/v_31_y_1986_i_3_p_307-327.html
- en.journals.sid.ir/ViewPaper.aspx?ID=294648
- ci.nii.ac.jp/naid/30009036484
- ecsocman.hse.ru/text/17601645/
- www.sciencedirect.com/science/article/B6VC0-46VV78N-4/2/ce371daca28a0d38824736988b1d0ef1

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