Forecasting Realized Volatility with Linear and Nonlinear Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.

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Year of publication:	2009-11-24
Authors:	McAleer, M.J. ; Medeiros, M.C.
Institutions:	Erasmus University Rotterdam, Econometric Institute
Subject:	financial econometrics \| volatility forecasting \| neural networks \| nonlinear models \| realized volatility \| bagging

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Extent:	application/pdf
Series:	Econometric Institute Report. - ISSN 1566-7294.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series RePEc:dgr:eureir Number EI 2009-37
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008584727