Outliers and GARCH Models in Financial Data

Amélie Charles, Olivier Darné Coders: Amélie Charles, Denisa Banulescu, Elena-Ivona Dumitrescu, Olivier Darné

Code and Data Abstract

This code identifies additive outliers (AO) and innovative outliers (IO) in a GARCH(1,1) model. Based on Franses and Ghijsels (1999), it uses the outlier detection method proposed by Chen and Liu (1993). To run this code, input the series of returns and choose the type of outlier to be identified as well as the critical value. The recommended value of the critical value (C=10) is based on simulation experiments proposed by Franses and Dijk (2000).

Paper Abstract

We propose to extend the additive outlier (AO) identification procedure developed by Franses and Ghijsels(Franses, P.H., Ghijsels, H., 1999. Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15, 1–9) to take into account the innovative outliers (IOs) in a GARCH model. We apply it to three daily stock market indexes and examine the effects of outliers on the diagnostics of normality.

Amélie Charles, Olivier Darné Coders: Amélie Charles, Denisa Banulescu, Elena-Ivona Dumitrescu, et al. "Outliers and GARCH Models in Financial Data." Economics Letters (2005).     Retrieved 06/24/2019 from researchcompendia.org/compendia/2013.78/

Page Owner

sheila@codersquid.com

created 11/12/2013

modified 01/16/2014

blog comments powered by Disqus

rmc id