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FORECASTING REALIZED VOLATILITY WITH LINEAR AND NONLINEAR UNIVARIATE MODELS
Author(s) -
McAleer Michael,
Medeiros Marcelo C.
Publication year - 2011
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/j.1467-6419.2010.00640.x
Subject(s) - volatility (finance) , econometrics , univariate , futures contract , estimator , realized variance , economics , proxy (statistics) , stochastic volatility , nonlinear system , forward volatility , implied volatility , linear model , mathematics , financial economics , statistics , multivariate statistics , quantum mechanics , physics
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.