Modelling stock returns with AR-GARCH processes.

Elzbieta Ferenstein; Miroslaw Gasowski

SORT (2004)

  • Volume: 28, Issue: 1, page 55-68
  • ISSN: 1696-2281

Abstract

top
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions.

How to cite

top

Ferenstein, Elzbieta, and Gasowski, Miroslaw. "Modelling stock returns with AR-GARCH processes.." SORT 28.1 (2004): 55-68. <http://eudml.org/doc/40452>.

@article{Ferenstein2004,
abstract = {Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions.},
author = {Ferenstein, Elzbieta, Gasowski, Miroslaw},
journal = {SORT},
keywords = {Series temporales; Autorregresión; Procesos estocásticos; Bolsa de valores; autoregressive process; GARCH and EGARCH models; conditional heteroscedastic variance; financial log returns},
language = {eng},
number = {1},
pages = {55-68},
title = {Modelling stock returns with AR-GARCH processes.},
url = {http://eudml.org/doc/40452},
volume = {28},
year = {2004},
}

TY - JOUR
AU - Ferenstein, Elzbieta
AU - Gasowski, Miroslaw
TI - Modelling stock returns with AR-GARCH processes.
JO - SORT
PY - 2004
VL - 28
IS - 1
SP - 55
EP - 68
AB - Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions.
LA - eng
KW - Series temporales; Autorregresión; Procesos estocásticos; Bolsa de valores; autoregressive process; GARCH and EGARCH models; conditional heteroscedastic variance; financial log returns
UR - http://eudml.org/doc/40452
ER -

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.