Forecasting realized volatility with linear and nonlinear univariate models

Michael McAleer; Marcelo C. Medeiros

doi:10.1111/j.1467-6419.2010.00640.x

Forecasting realized volatility with linear and nonlinear univariate models

Research output: Contribution to journal › Article › peer-review

Abstract

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 intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.

Original language	English (US)
Pages (from-to)	6-18
Number of pages	13
Journal	Journal of Economic Surveys
Volume	25
Issue number	1
DOIs	https://doi.org/10.1111/j.1467-6419.2010.00640.x
State	Published - Feb 2011
Externally published	Yes

Keywords

Bagging
Financial econometrics
Neural networks
Nonlinear models
Realized volatility
Volatility forecasting

ASJC Scopus subject areas

Economics and Econometrics

Online availability

10.1111/j.1467-6419.2010.00640.x

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AB - 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 intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.

KW - Bagging

KW - Financial econometrics

KW - Neural networks

KW - Nonlinear models

KW - Realized volatility

KW - Volatility forecasting

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Forecasting realized volatility with linear and nonlinear univariate models

Abstract

Keywords

ASJC Scopus subject areas

Online availability

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