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Title:Quantile regression in risk calibration PDF Logo
Authors:Chao, Shih-Kang
Härdle, Wolfgang Karl
Wang, Weining
Issue Date:2012
Series/Report no.:SFB 649 discussion paper 2012-006
Abstract:Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a stressful situation for one market participant, one likes to measure how this stress affects other factors. The CoVaR (Conditional VaR) framework has been developed for this purpose. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semiparametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.
Subjects:CoVaR
Value-at-Risk
quantile regression
locally linear quantile regression
partial linear model
semiparametric model
JEL:C14
C21
C22
C53
G01
G10
G20
G32
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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