Forecasting realized volatility models:the benefits of bagging and nonlinear specifications
AbstractWe 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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Bibliographic InfoPaper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 547.
Date of creation: Aug 2007
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-08-27 (All new papers)
- NEP-ECM-2007-08-27 (Econometrics)
- NEP-ETS-2007-08-27 (Econometric Time Series)
- NEP-FOR-2007-08-27 (Forecasting)
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- Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012.
"Let's Do It Again: Bagging Equity Premium Predictors,"
CREATES Research Papers
2012-41, School of Economics and Management, University of Aarhus.
- Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussÃ£o 604, Department of Economics PUC-Rio (Brazil).
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