Forecasting Realized Volatility with Linear and Nonlinear Models

McAleer, Michael; Medeiros, Marcelo C.

doi:10.2139/ssrn.1496861

Cited by 2 publications

(2 citation statements)

References 49 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…McAleer and Medeiros (2008) considered a smooth transition version of the HAR while Hillebrand and Medeiros (2016) considered the combination of smooth transitions, long memory, and NN models. Hillebrand and Medeiros (2010) and McAleer and Medeiros (2011) combined NN models with bagging and Scharth and Medeiros (2009) considered smooth transition regression trees. The use of LASSO and its generalizations to estimate extensions of the HAR model was proposed by Audrino and Knaus (2016).…”

Section: Applications Of ML Methods To Economic and Financial Forecas...mentioning

confidence: 99%

Machine learning advances for time series forecasting

Masini

Medeiros

Mendes

2021

Journal of Economic Surveys

167

View full text Add to dashboard Cite

In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data.

show abstract

Section: Applications Of ML Methods To Economic and Financial Forecas...mentioning

confidence: 99%

Machine learning advances for time series forecasting

Masini

Medeiros

Mendes

2021

Journal of Economic Surveys

167

View full text Add to dashboard Cite

show abstract

“…McAleer and Medeiros (2008) considered a smooth transition version of the HAR while Hillebrand and Medeiros (2016) considered the combination of smooth transitions, long memory and neural network models. Hillebrand and Medeiros (2010) and McAleer and Medeiros (2011) combined NN models with bagging and Scharth and Medeiros (2009) considered smooth transition regression trees. The use of LASSO and its generalizations to estimate extensions of the HAR model was proposed by Audrino and Knaus (2016).…”

Section: Empirical Illustrationmentioning

confidence: 99%

Machine Learning Advances for Time Series Forecasting

Masini¹,

Medeiros²,

Mendes³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.

show abstract

Forecasting Realized Volatility with Linear and Nonlinear Models

Cited by 2 publications

References 49 publications

Machine learning advances for time series forecasting

Machine learning advances for time series forecasting

Machine Learning Advances for Time Series Forecasting

Contact Info

Product

Resources

About