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Donaldson, R.G. and Kamstra, M. (1997) An Artificial Neural Network—GARCH Model for International Stock Return Volatility. Journal of Empirical Finance, 4, 17-46.
https://doi.org/10.1016/s0927-5398(96)00011-4

has been cited by the following article:

• TITLE: An Empirical Evaluation in GARCH Volatility Modeling: Evidence from the Stockholm Stock Exchange

  AUTHORS: Chaido Dritsaki

  KEYWORDS: Stockholm Stock Exchange, Volatility, GARCH Models, Leverage Effect, Forecasting

  JOURNAL NAME: Journal of Mathematical Finance, Vol.7 No.2, May 19, 2017

  ABSTRACT: In this paper, we use daily stock returns from the Stockholm Stock Exchange in order to examine their volatility. For this reason, we estimate not only GARCH (1,1) symmetric model but also asymmetric models EGARCH (1,1) and GJR-GARCH (1,1) with different residual distributions. The parameters of the volatility models are estimated with the Maximum Likelihood (ML) using the Marquardt algorithm (Marquardt [1]). The findings reveal that negative shocks have a large impact than positive shocks in this market. Also, indices for the return of forecasting have shown that the ARIMA (0,0,1)-EGARCH (1,1) model with t-student provide more precise forecasting on volatilities and expected returns of the Stockholm Stock Exchange.