Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics
AbstractRealized volatility computed from high-frequency data is an important measure for many applications in finance. However, its dynamics are not well understood to date. Recent notable advances that perform well include the heterogeneous autoregressive (HAR) model which is economically interpretable and but still easy to estimate. It also features good out-of-sample performance and has been extremely well received by the research community. We present a data driven approach based on the absolute shrinkage and selection operator (lasso) which should identify the aforementioned model. We prove that the lasso indeed recovers the HAR model asymptotically if it is the true model, and we present Monte Carlo evidence in finite sample. The HAR model is not recovered by the lasso on real data. This, together with an empirical out-of-sample analysis that shows equal performance of the HAR model and the lasso approach, leads to the conclusion that the HAR model may not be the true model but it captures a linear footprint of the true volatility dynamics.
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Bibliographic InfoPaper provided by University of St. Gallen, School of Economics and Political Science in its series Economics Working Paper Series with number 1224.
Length: 49 pages
Date of creation: Nov 2012
Date of revision:
Realized Volatility; Heterogeneous Autoregressive Model; Lasso; Model Selection;
Find related papers by JEL classification:
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-10 (All new papers)
- NEP-ECM-2012-12-10 (Econometrics)
- NEP-ETS-2012-12-10 (Econometric Time Series)
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