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 this paper.

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Year of publication:	2010
Authors:	McAleer, Michael ; Medeiros, Marcelo C.
Publisher:	Rio de Janeiro : Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
Subject:	Volatilität \| Prognoseverfahren \| Nichtlineare Regression \| Neuronale Netze \| Algorithmus \| Financial econometrics \| volatility forecasting \| neural networks \| nonlinear models \| realized volatility \| bagging.

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Series:	Texto para discussão ; 568
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	625814436 [GVK] hdl:10419/176051 [Handle] RePEc:rio:texdis:568 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10011807392