Workshops

The Applied Statistics Workshop 2017

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¦ 2017”N3ŒŽ29“ϊŒ»έ‚Μ—\’θ‚Ε‚·B

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2017”N4ŒŽ7“ϊi‹ΰ Fridayj@16:50-18:35

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“Œ‹ž‘εŠw‘εŠw‰@ŒoΟŠwŒ€‹†‰Θ ŠwpŒπ—¬“ i¬“‡ƒz[ƒ‹j1ŠK ‘ζ1ƒZƒ~ƒi[ŽΊ [’n}]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

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Mingli Chen (University of Warwick)
"Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management"
(with A Belloni and V. Chernozhukov)

—vŽ|(Abstract) We propose Quantile Graphical Models (QGMs) to characterize predictive and conditional independence relationships within a set of random variables of interest. This framework is intended to quantify the dependence in non-Gaussian settings which are ubiquitous in many econometric applications. We consider two distinct QGMs. First, Condition Independence QGMs characterize conditional independence at each quantile index revealing the distributional dependence structure. Second, Predictive QGMs characterize the best linear predictor under asymmetric loss functions. Under Gaussianity these notions essentially coincide but non-Gaussian settings lead us to different models as prediction and conditional independence are fundamentally different properties. Combined the models complement the methods based on normal and nonparanormal distributions that study mean predictability and use covariance and precision matrices for conditional independence. We also propose estimators for each QGMs. The estimators are based on high-dimension techniques including (a continuum of) l1-penalized quantile regressions and low biased equations, which allows us to handle the potentially large number of variables. We build upon recent results to obtain valid choice of the penalty parameters and rates of convergence. These results are derived without any assumptions on the separation from zero and are uniformly valid across a wide-range of models. With the additional assumptions that the coefficients are well-separated from zero, we can consistently estimate the graph associated with the dependence structure by hard thresholding the proposed estimators. Further we show how QGM can be used in measuring systemic risk contributions and the impact of downside movement in the market on the dependence structure of assets' return.

 

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2017”N4ŒŽ28“ϊi‹ΰ Fridayj@16:50-18:35

ŽεΓFƒ~ƒNƒŽΐΨ•ͺΝƒ[ƒNƒVƒ‡ƒbƒv

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“Œ‹ž‘εŠw‘εŠw‰@ŒoΟŠwŒ€‹†‰Θ ŠwpŒπ—¬“ i¬“‡ƒz[ƒ‹j1ŠK ‘ζ1ƒZƒ~ƒi[ŽΊ [’n}]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

•ρ

Songnian Chen i`‰Θ‹Z‘εj
TBA

—vŽ|(Abstract)  

 

“ϊŽž

2017”N6ŒŽ9“ϊi‹ΰ Fridayj@16:50-18:35

κŠ

“Œ‹ž‘εŠw‘εŠw‰@ŒoΟŠwŒ€‹†‰Θ ŠwpŒπ—¬“ i¬“‡ƒz[ƒ‹j1ŠK ‘ζ1ƒZƒ~ƒi[ŽΊ [’n}]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

•ρ

‹ΰ’JM (Aarhus University)
TBA

—vŽ|(Abstract)  

 

“ϊŽž

2017”N6ŒŽ23“ϊi‹ΰ Fridayj@16:50-18:35

κŠ

“Œ‹ž‘εŠw‘εŠw‰@ŒoΟŠwŒ€‹†‰Θ ŠwpŒπ—¬“ i¬“‡ƒz[ƒ‹j1ŠK ‘ζ1ƒZƒ~ƒi[ŽΊ [’n}]
in Seminar Room 1 on the 1st floor of the Economics Research Annex (Kojima Hall) [Map]

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–kμ“§iUniversity College London)
TBA

—vŽ|(Abstract)  

 

 

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