The Benefits of Bagging for Forecast Models of Realized Volatility

Hillebrand, Eric; Medeiros, Marcelo C.

doi:10.1080/07474938.2010.481554

Cited by 79 publications

(53 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The resulting ensemble is robust to small changes in the sample, alleviating this type of uncertainty. Recent research has lead to a series of studies involving the application of the Bagging algorithm for forecasting purposes with positive results in many application areas (Inoue and Kilian, 2008;Lee and Yang, 2006;Chen and Ren, 2009;Hillebrand and Medeiros, 2010;Langella et al, 2010). Apart from improving accuracy, using ensembles also avoids the problem of identifying and choosing the best trained network.…”

Section: Forecasting With Neural Networkmentioning

confidence: 99%

Neural network ensemble operators for time series forecasting

Kourentzes

Barrow

Crone

2014

Expert Systems with Applications

249

126

View full text Add to dashboard Cite

The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single "best" network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training and the distribution of the forecasts.

show abstract

Section: Forecasting With Neural Networkmentioning

confidence: 99%

Neural network ensemble operators for time series forecasting

Kourentzes

Barrow

Crone

2014

Expert Systems with Applications

249

126

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider the forecasting of stock market volatility via nonlinear models based on a neural network (NN) version of the heterogeneous autoregressive model (HAR) of Corsi (2009). As in Hillebrand and Medeiros (2009) we evaluate the benefits of bagging (bootstrap aggregation) in forecasting daily volatility as well as the inclusion of past cumulated returns over different horizons as possible predictors. As the number of predictors can get quite large, the application of bagging is recommended as a device to improve forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Realized Volatility With Linear and Nonlinear Univariate Models

McAleer

Medeiros

2010

Journal of Economic Surveys

Self Cite

View full text Add to dashboard Cite

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 intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.

show abstract

“…As in Hillebrand and Medeiros (2009) we evaluate the benefits of bagging (bootstrap aggregation) 1 in forecasting daily volatility as well as the inclusion of past cumulated returns over different horizons as possible predictors. As the number of predictors can get quite large, the application of bagging is recommended as a device to improve forecasting performance.…”

Section: Introductionmentioning

confidence: 99%