Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics

115 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Uncertainty and crude oil market volatility: new evidence (2020, methodology): “Audrino and Knaus 2016; Li and Tsiakas 2017; Zhang et al. 2019b). In particular, Zhang, Ma, and Wang (2019a) employ two prevailing model shrinkage approaches (i.”
Machine Learning Advances for Time Series Forecasting (2020, methodology): “The use of LASSO and its generalizations to estimate extensions of the HAR model was proposed by Audrino and Knaus (2016).”
Risk Everywhere: Modeling and Managing Volatility (2018, unknown): “However, the lasso-based model selection procedures explored in Audrino and Knaus (2015) generally do not recover the exact lag structure of the original HAR model, although the out-of-sample performance of the selected models appear indistinguishable from that of the original HAR based on the daily”
Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017, unknown): “Our paper is related to the work of Audrino and Knaus (2014) who applied the Lasso to univariate autoregressive models in order to forecast realized measures of volatility. The contributions of this paper are as follows. First, we put forward a methodology to model and forecast large time-varying re”