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Iryna OKHRIN (Dresden University of Technology, Germany) "On LASSO-GARCH Approach"

June 20, 2019 @ 11:30 am - 12:30 pm
Date: 20th of June 2019
Place: Room 3001
Iryna OKHRIN (Dresden University of Technology, Germany) “On LASSO-GARCH Approach”
Abstract : It is a stylized fact that GARCH type models are good at describing the characteristics of financial data (leverage effects, volatility clustering, etc.). List of various GARCH models has increased tremendously over the last decade, creating a serious model selection problem. Estimation of the most general model is a challenging task and the results are not easy to explain; it also leads to overestimation. In this project, we propose a LASSO-GARCH approach that allows for estimation and simultaneous model simplification. The question estimation and simplification relates not only to the number of lags to be taken into account, but also to the specific characteristics of the data. For example, if no leverage effect is present, there is no need to estimate an asymmetric GARCH model and it is exactly what a LASSO-GARCH procedure avoids. In our LASSO-GARCH study, we consider a family of GARCH models and estimate them via a penalized ML approach. Simulation study results show consistency
Jean-Michel ZAKOIAN (CREST )
 : ENSAE ParisTech
5, avenue Henry Le Chatelier
91120 Palaiseau
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