Forecasting Realized Volatility with Linear and Nonlinear Models

Michael McAleer, MC Medeiros

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)

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 languageEnglish
Pages (from-to)6-18
Number of pages13
JournalJournal of Economic Surveys
Volume25
Issue number1
DOIs
Publication statusPublished - 2011

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