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Forecasting realized volatility models:the benefits of bagging and nonlinear specifications

  • Eric Hillebrand



  • Marcelo Cunha Medeiros


    (Department of Economics, PUC-Rio)

We forecast daily realized volatilities with linear and nonlinear models and evaluate the benefits of bootstrap aggregation (bagging) in producing more precise forecasts. We consider the linear autoregressive (AR) model, the Heterogeneous Autoregressive model (HAR), and a non-linear HAR model based on a neural network specification that allows for logistic transition effects (NNHAR). The models and the bagging schemes are applied to the realized volatility time series of the S&P500 index from 3-Jan-2000 through 30-Dec-2005. Our main findings are: (1) For the HAR model, bagging successfully averages over the randomness of variable selection; however, when the NN model is considered, there is no clear benefit from using bagging; (2) including past returns in the models improves the forecast precision; and (3) the NNHAR model outperforms the linear alternatives.

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Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 547.

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Length: 30p
Date of creation: Aug 2007
Date of revision:
Handle: RePEc:rio:texdis:547
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  1. Lutz Kilian & Atsushi Inoue, 2004. "Bagging Time Series Models," Econometric Society 2004 North American Summer Meetings 110, Econometric Society.
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