WITHDRAWN: ARCH Models Application on Istanbul Stock Market Data

• Activity Number: 537
• Type: Contributed
• Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Atilla Aslanargun and Berna Yazici and Betül Kan and Zeynep Ozgun
• Companies: Anadolu University and Anadolu University and Anadolu University and Anadolu University
• Keywords: ARCH ; GARCH ; EGARCH ; TGARCH ; Heteroscedasticity ; Volatility

Abstract

In financial series, the nonlinear conditional heteroscedastic models are more commonly used than the linear time series models since the properties they have. Time series analysis requires the models that take into account the heteroscedasticity since the prediction errors do not have the constant variances. Robert F. Engle 1982 generalizes the assumption about the heteroscedasticity and proposes a new stochastic models class "Auto Regressive Conditional Heteroscedasticity (ARCH)". In this study different ARCH models; GARCH, GARCH-M, EGARCH, and TGARCH are applied on the volatility in Istanbul stock market, namely ISE-100 Index (ISE=Istanbul Stock Index). 22 years daily data (January 1988-January 2010) in US dollars are used. The resulting models are compared with each other and the best model for the problem in question is defined.