Arbeitspapier
Factorisable sparse tail event curves
In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or FASTEC for short, exploits the potential factor structure of multivariate conditional quantiles through nuclear norm regularization and is particularly suitable for dealing with extreme quantiles. We study both theoretical properties and computational aspects of the estimating procedure for FASTEC. In particular, we derive nonasymptotic oracle bounds for the estimation error, and develope an efficient proximal gradient algorithm for the non-smooth optimization problem incurred in our estimating procedure. Merits of the proposed methodology are further demonstrated through applications to Conditional Autoregressive Value-at-Risk (CAViaR) (Engle and Manganelli; 2004), and a Chinese temperature dataset.
- Language
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Englisch
- Bibliographic citation
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Series: SFB 649 Discussion Paper ; No. 2015-034
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Large Data Sets: Modeling and Analysis
Computational Techniques; Simulation Modeling
Financial Forecasting and Simulation
Financial Institutions and Services: General
- Subject
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high-dimensional data analysis
multivariate quantile regression
quantile regression
value-at-risk
nuclear norm
multi-task learning
- Event
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Geistige Schöpfung
- (who)
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Chao, Shih-Kang
Härdle, Wolfgang Karl
Yuan, Ming
- Event
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Veröffentlichung
- (who)
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Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (where)
-
Berlin
- (when)
-
2015
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chao, Shih-Kang
- Härdle, Wolfgang Karl
- Yuan, Ming
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Time of origin
- 2015
Other Objects (12)
Factorisable sparse tail event curves with expectiles
Factorisable Sparse Tail Event Curves
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A confidence corridor for sparse longitudinal data curves
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Tail event driven networks of SIFIs
TEDAS - Tail Event Driven ASset Allocation
Detecting Serial Dependence in Tail Events
Tail Event Driven Factor Augmented Dynamic Model
TERES: tail event risk expectile based shortfall
Modeling extreme events: time-varying extreme tail shape
Modeling extreme events: Time-varying extreme tail shape
Factorisable sparse tail event curves with expectiles
Factorisable Sparse Tail Event Curves
Factorisable Sparse Tail Event Curves with Expectiles
A confidence corridor for sparse longitudinal data curves
TENET: Tail-Event driven NETwork risk
Tail event driven networks of SIFIs
TEDAS - Tail Event Driven ASset Allocation
Detecting Serial Dependence in Tail Events
Tail Event Driven Factor Augmented Dynamic Model
TERES: tail event risk expectile based shortfall
Modeling extreme events: time-varying extreme tail shape
Modeling extreme events: Time-varying extreme tail shape
Factorisable sparse tail event curves with expectiles
Factorisable Sparse Tail Event Curves
Factorisable Sparse Tail Event Curves with Expectiles
A confidence corridor for sparse longitudinal data curves
TENET: Tail-Event driven NETwork risk
Tail event driven networks of SIFIs
TEDAS - Tail Event Driven ASset Allocation
Detecting Serial Dependence in Tail Events
Tail Event Driven Factor Augmented Dynamic Model
TERES: tail event risk expectile based shortfall
Modeling extreme events: time-varying extreme tail shape