Forecasting Realized Volatility with Linear and Nonlinear Univariate 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 futures. 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.

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Year of publication:	2010-05-01
Authors:	McAleer, Michael ; Medeiros, Marcelo C.
Institutions:	Department of Economics and Finance, College of Business and Economics
Subject:	Financial econometrics \| volatility forecasting \| neural networks \| nonlinear models \| realized volatility \| bagging

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Extent:	application/pdf
Series:	Working Papers in Economics.
Type of publication:	Book / Working Paper
Language:	English
Notes:	26 pages
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008458994