Forecasting Financial Time Series: Normal GARCH with Outliers or Heavy Tailed Distribution Assumptions?

26 Pages Posted: 10 Oct 2011 Last revised: 18 Oct 2011

See all articles by Christoph Hartz

Christoph Hartz

University of Munich

Marc S. Paolella

University of Zurich - Department Finance; Swiss Finance Institute

Date Written: September 1, 2011

Abstract

The use of GARCH models is widely used as an effective method for capturing the volatility clustering inherent in financial returns series. The residuals from such models are however often non-Gaussian, and two methods suggest themselves for dealing with this; outlier removal, or use of non-Gaussian innovation distributions. While there are benefits to both, we show that the latter method is better if interest centers on volatility and value-at-risk prediction. New volatility measures based on OHLC (open high low close) data are derived and used. Use of OHLC measures are shown to be superior to use of the naive estimator used in other GARCH outlier studies.

Keywords: Outliers, fat-tailed distributions, GARCH, volatility

Suggested Citation

Hartz, Christoph and Paolella, Marc S., Forecasting Financial Time Series: Normal GARCH with Outliers or Heavy Tailed Distribution Assumptions? (September 1, 2011). Swiss Finance Institute Research Paper No. 11-45, Available at SSRN: https://ssrn.com/abstract=1941699 or http://dx.doi.org/10.2139/ssrn.1941699

Christoph Hartz

University of Munich ( email )

Geschwister-Scholl-Platz 1
Munich, DE Bavaria 80539
Germany

Marc S. Paolella (Contact Author)

University of Zurich - Department Finance

Plattenstr. 14
Zürich, 8032
Switzerland

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

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