A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns

Bülent Köksal

MPRA Paper from University Library of Munich, Germany

Abstract: We compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and their forecasting performance of the conditional variance in an out-of-sample setting. Exponential GARCH model of Nelson (1991) with “constant mean, t-distribution, one lag moving average term” specification achieves the best overall performance for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed returns better than the Gaussian distribution or the generalized error distribution. In terms of forecasting performance, the best models are the ones that can accommodate a leverage effect. Results from fitting the selected exponential GARCH model to the historical ISE-100 return data indicates that the return volatility reacts to bad news 24% more than they react to good news as a result of a one standard deviation shock to the returns. As the magnitude of shock increases, the asymmetry becomes larger.

Keywords: GARCH; Volatility Models; Istanbul Stock Exchange; ISE-100 (search for similar items in EconPapers)
JEL-codes: C52 G10 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Journal of Economic and Social Research 11.2(2009): pp. 1-29

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/30510/1/MPRA_paper_30510.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:30510

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().