Using neural networks for forecasting volatility of S&P 500 Index futures prices

By S. Hamid, Z. Iqbal. 2004

206 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

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Citations in the corpus

Deep Learning Stock Volatility with Google Domestic Trends (2015, background): “[3] instead reported a volatility forecasting model, and Ref.”
Artificial neural networks in business: Two decades of research (2016, background): “However, several authors suggested preprocessing the input data to generate sound predictions [142]”
Modeling and predicting the CBOE market volatility index (2014, background): “The motivation rests on the typical success that neural networks experience in the context of volatility modeling and forecasting (Donaldson and Kamstra, 1997; Hu and Tsoukalas, 1999; Hamid and Iqbal, 2004).”
Financial time series forecasting with machine learning techniques: a survey (2010, background): “Others 10 [4], [5], [15], [17], [20], [26], [35], [38], [39], [41] Table 3: Reviewed papers classified by input variables”
TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns (2010, methodology): “Therefore in these models variables have t 1 lags included and there are two neurons corresponding to two regimes as in the results obtained following the Hansen and Seo (2002). At the second modeling approach, we relaxed these assumptions by augmenting the analyzed models to have more lagged input ”
Sentiment-aware stock market prediction: A deep learning method (2017, background): “This early work [23] is among the first a few which use recurrent neural nets to predict stock prices, [24] instead reported a volatility forecasting model, [25] incorporated public mood data in the up and down direction prediction of market fund Dow Jones Industrial Average.”
Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM (2020, methodology): “Gonzalez Miranda and Burgess [43], Hamid and Iqbal [44], and Bildirici and Ersin [45] combined the neural network method with the GARCH model to predict oil prices.”
Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach (2009, unknown): “…estimated and forecasted stock prices (Black & McMillan, 2004; Donaldson & Kamstra, 1996; Jasic & Wood, 2004; Kanas, 2001; Kanas & Yannopoulos1, 2001; Maasoumi & Racine, 2002; Qi, 1999; Rapach & Wohar, 2005; Shively, 2003) and stock volatilities (Dunis & Huang, 2002; Hamid & Iqbal, 2004).”