Forecasting Realized Volatility with Linear and Nonlinear Models
2009-11-24
Research Paper
pp 1-26.
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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 the paper.
Keywords
- neural networks
- nonlinear models
- financial econometrics
- realized volatility
- bagging
- volatility forecasting
Classifications using
Journal of Economic Literature (JEL) Classification System
- G12 : Asset Pricing
- C53 : Forecasting and Other Model Applications
- C22 : Time-Series Models; Dynamic Quantile Regressions
- G17 : Financial Forecasting
Automatically Extracted Terms
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- volatility
- bagging
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- nn-har
- network
- forecast
- journal
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- time series
- nonlinear
- nn-har model
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- variance
- paper
- series
- medeiro
- bootstrap
- volatility models
