Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148883
Authors: 
Zbonakova, Lenka
Härdle, Wolfgang
Wang, Weining
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-047
Abstract: 
In the present paper we study the dynamics of penalization parameter ? of the least absolute shrinkage and selection operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The dynamic behaviour of the parameter ? can be observed when the model is assumed to vary over time and therefore the fitting is performed with the use of moving windows. The proposal of investigating time series of ? and its dependency on model characteristics was brought into focus by H¨ardle et al. (2016), which was a foundation of FinancialRiskMeter (http://frm.wiwi.hu-berlin.de). Following the ideas behind the two aforementioned projects, we use the derivation of the formula for the penalization parameter ? as a result of the optimization problem. This reveals three possible effects driving ?; variance of the error term, correlation structure of the covariates and number of nonzero coefficients of the model. Our aim is to disentangle these three effect and investigate their relationship with the tuning parameter ?, which is conducted by a simulation study. After dealing with the theoretical impact of the three model characteristics on ?, empirical application is performed and the idea of implementing the parameter ? into a systemic risk measure is presented. The codes used to obtain the results included in this work are available on http://quantlet.de/d3/ia/.
Subjects: 
Lasso
quantile regression
systemic risk
high dimensions
penalization parameter
JEL: 
C21
G01
G20
G32
Document Type: 
Working Paper

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