Özet:

Autoregressive conditional heteroscedasticity (ARCH) and Generalized ARCH (GARCH) models with various alternatives have been widely analyzed in the finance literature in order to model the volatility of the returns. In all of these models, the hidden variable volatility depends parametrically on lagged values of the process and lagged values of volatility (Bühlmann and McNeill, 2002) where the parameters are estimated with a nonlinear maximum likelihood function. In this paper a nonparametric approach to GARCH models proposed by Bühlmann and McNeill (2002) is followed to model the volatility of daily stock returns of the Istanbul Stock Exchange 100 (ISE 100) market from January 1991 to November 2012.

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